Introduction

Tiders is an open-source framework that simplifies getting data out of blockchains and into your favorite tools. Whether you are building a DeFi dashboard, tracking NFT transfers, or running complex analytics, Tiders handles the heavy lifting of fetching, cleaning, transforming and storing blockchain data.

Tiders is modular. A Tiders pipeline is built from four components:

Why Tiders?

Most indexers lock you into a specific platform or database. Tiders is built to be modular, meaning you can swap parts in and out without breaking your setup:

• Swap Providers: Don’t like your current data source? Switch between HyperSync, SQD, or a standard RPC node by changing one line of code.
• Plug-and-Play data transformations: Need to decode smart contract events or change data types? Use our built-in Rust-powered steps or write your own custom logic.
• Write Anywhere: Send your data to a local DuckDB file for prototyping, or a production-grade ClickHouse or PostgreSQL instance when you’re ready to scale.
• Modular Reusable Pipelines: Protocols often reuse the same data structures. You don’t need write modules from scratch every time. Since Tiders pipelines are regular Python objects, you can build functions around them, reuse across pipelines, or set input parameters to customize as needed.

Two ways to use Tiders

Both modes share the same pipeline engine.

You can also use tiders codegen to generate a Python script from a YAML config — a quick way to move from no-code to full Python control.

Key Features

• Continuous Ingestion: Keep your datasets live and fresh. Tiders can poll the chain head to ensure your data is always up to date.
• Switch Providers: Move between HyperSync, SQD, or standard RPC nodes with a single config change.
• No Vendor Lock-in: Use the best data providers in the industry without being tied to their specific platforms or database formats.
• Custom Logic: Easily extend and customize your pipeline code in Python for complete flexibility.
• Advanced Analytics: Seamlessly works with industry-standard tools like Polars, Pandas, DataFusion and PyArrow as the data is fetched.
• Multiple Outputs: Send the same data to a local file and a production database simultaneously.
• Rust-Powered Speed: Core tasks like decoding and transforming data are handled in Rust, giving you massive performance without needing to learn a low-level language.
• Parallel Execution: Tiders doesn’t wait around. While it’s writing the last batch of data to your database, it’s already fetching and processing the next one in the background.

Data Providers

Connect to the best data sources in the industry without vendor lock-in. Tiders decouples the provider from the destination, giving you a consistent way to fetch data.

Tiders can support new providers. If your project has custom APIs to fetch blockchain data, especially ones that support server-side filtering, you can create a client for it, similar to the Tiders RPC client. Get in touch with us.

Transformations

Leverage the tools you already know. Tiders automatically convert data batch-by-batch into your engine’s native format, allowing for seamless, custom transformations on every incoming increment immediately before it is written.

Supported Output Formats

Whether local or a production-grade data lake, Tiders handles the schema mapping and batch-loading to your destination of choice.

Architecture

Tiders is composed of some repositories. 3 owned ones.

API Reference

Auto-generated Rust API documentation is available at:

• tiders-core rustdoc
• tiders-rpc-client rustdoc

Installation

CLI (No-Code Mode)

To use tiders start with a YAML config, install the cli:

pip install "tiders"

This adds everything needed to run pipelines from a YAML file.

Combine with writer extras as needed:

pip install "tiders[duckdb]"
pip install "tiders[delta_lake]"
pip install "tiders[clickhouse]"
pip install "tiders[iceberg]"

Or install everything at once:

pip install "tiders[all]"

Python SDK

To create pipelines scripts in python, install Tiders as libraries. Tiders is published to PyPI as two packages:

• tiders — the Python pipeline SDK
• tiders-core — pre-built Rust bindings (installed automatically as a dependency)

Using pip

pip install tiders tiders-core

Using uv (recommended)

uv pip install tiders tiders-core

Optional dependencies

Depending on your selected writer or transformation engine, you may need additional packages:

For transformation steps:

uv pip install tiders[duckdb, polars] tiders-core

No-Code Quick Start

Run a blockchain data pipeline without writing Python — just a YAML config file.

1. Install

pip install "tiders[duckdb]"

2. Create a config file

Create tiders.yaml:

project:
  name: erc20_transfers
  description: Fetch ERC-20 Transfer events and write to DuckDB.

provider:
  kind: rpc
  url: "https://mainnet.gateway.tenderly.co" # or change to read from the .env file ${PROVIDER_URL}

contracts:
  - name: erc20
    address: "0xae78736Cd615f374D3085123A210448E74Fc6393" # rETH contract, we need a erc20 reference to download the ABI.
    # An abi: ./erc20.abi.json config will be added after using CLI command `tiders abi` in this folder

query:
  kind: evm
  from_block: 18000000
  to_block: 18000100
  logs:
    - topic0: erc20.Events.Transfer.topic0
  fields:
    log: [address, topic0, topic1, topic2, topic3, data, block_number, transaction_hash, log_index]

steps:
  - kind: evm_decode_events
    config:
      event_signature: erc20.Events.Transfer.signature
      output_table: transfers
      allow_decode_fail: true
      hstack: false
  - kind: hex_encode

writer:
  kind: duckdb
  config:
    path: data/transfers.duckdb

3. Environment Variables

Use ${VAR_NAME} placeholders anywhere in the YAML to keep secrets and environment-specific values out of your config file. This works for any string field — provider URLs, credentials, file paths, etc.

provider:
  kind: rpc
  url: ${PROVIDER_URL}
  bearer_token: ${PROVIDER_BEARER_TOKEN}

At startup, the CLI automatically loads a .env file from the same directory as the config file, then substitutes all ${VAR_NAME} placeholders with their values. If a variable is referenced in the YAML but not defined, the CLI raises an error.

Create a .env file alongside your config:

PROVIDER_URL=https://mainnet.gateway.tenderly.co
PROVIDER_BEARER_TOKEN=12345678

You can also point to a different .env file using the --env-file flag:

tiders start --env-file /path/to/.env tiders.yaml

4. Download ABIs

Tiders CLI provides a command to make it easy to download ABIs defined in the YAML file and save them in the folder.

tiders abi

5. Run

tiders start

The CLI auto-discovers tiders.yaml in the current directory. However, you can also pass a path explicitly:

tiders start path/tiders.yaml

5. Generate a Python script (optional)

Once your YAML pipeline is working, you can generate an equivalent Python script using tiders codegen:

tiders codegen

This reads the same YAML file and outputs a standalone Python script that constructs and runs the same pipeline using the tiders Python SDK. By default, the output file is named after the project in snake_case (e.g. erc20_transfers.py). You can specify a custom output path with -o:

tiders codegen -o my_pipeline.py

This is useful when you want to move beyond YAML and customize the pipeline logic in Python — for example, adding custom transformation steps, conditional logic, or integrating with other libraries.

Next steps

• CLI Overview — CLI commands, flags, env var substitution, config auto-discovery
• CLI YAML Reference — full reference for all YAML sections
• rETH Transfer Example — complete annotated example

Your First Pipeline

This tutorial builds a pipeline that fetches ERC-20 transfer events from Ethereum and writes them to DuckDB.

Pipeline Anatomy

Every tiders pipeline has five parts:

1. Contracts — optional, helper for contract information
2. Provider — where to fetch data from
3. Query — what data to fetch
4. Steps — transformations to apply
5. Writer — where to write the output

Step 1: Define the Contracts

Contracts is an optional module that makes it easier to get contract information, such as Events, Functions and their params.

Use evm_abi_events and evm_abi_functions from tiders_core. These functions take a JSON ABI string and return a list[EvmAbiEvent] / list[EvmAbiFunction] with the fields described above.

from pathlib import Path
from tiders_core import evm_abi_events, evm_abi_functions

erc20_address = '0xae78736Cd615f374D3085123A210448E74Fc6393'  # rETH token contract

erc20_abi_path = Path('/home/yulesa/repos/tiders/examples/first_pipeline/erc20.abi.json')
erc20_abi_json = erc20_abi_path.read_text()

# Build a dict of events keyed by name, e.g. erc20_events["Transfer"]["topic0"]
erc20_events = {
    ev.name: {
        'topic0': ev.topic0,
        'signature': ev.signature,
        'name_snake_case': ev.name_snake_case,
        'selector_signature': ev.selector_signature,
    }
    for ev in evm_abi_events(erc20_abi_json)}

# Build a dict of functions keyed by name, e.g. erc20_functions["approve"]["selector"]
erc20_functions = {
    fn.name: {
        'selector': fn.selector,
        'signature': fn.signature,
        'name_snake_case': fn.name_snake_case,
        'selector_signature': fn.selector_signature,
    }
    for fn in evm_abi_functions(erc20_abi_json)}

Step 2: Define the Provider

from tiders_core.ingest import ProviderConfig, ProviderKind

provider = ProviderConfig(
    kind=ProviderKind.RPC,
    url='https://mainnet.gateway.tenderly.co',
)

Available providers: HYPERSYNC, SQD, RPC.

Step 3: Define the Query

The query defines what data to fetch: block range, filters, and fields.

from tiders_core.ingest import Query, QueryKind
from tiders_core.ingest import evm

query = Query(
    kind=QueryKind.EVM,
    params=evm.Query(
        from_block=18000000,
        to_block=18000100,
        logs=[evm.LogRequest(topic0=[erc20_events["Transfer"]["topic0"]])],
        fields=evm.Fields(
            log=evm.LogFields(
                log_index=True,
                transaction_hash=True,
                block_number=True,
                address=True,
                data=True,
                topic0=True,
                topic1=True,
                topic2=True,
                topic3=True,
            ),
        ),
    ),
)

Step 4: Add Transformation Steps

Steps are transformations applied to the raw data before writing. They run in order, each step’s output feeding into the next.

STEP 1 - EVM_DECODE_EVENTS:

Decodes the raw log data (topic1..3 + data) into named columns using the event signature.

• allow_decode_fail: if True, rows that fail to decode are kept (with nulls)
• hstack: if False, outputs only decoded columns; if True, append them to the original raw log columns

STEP 2 - HEX_ENCODE:

Converts binary columns (addresses, hashes) to hex strings, making them human-readable and compatible with databases like DuckDB.

from tiders.config import EvmDecodeEventsConfig, HexEncodeConfig, Step, StepKind

steps = [
    # Decode the raw log data into typed columns
    cc.Step(
        kind=cc.StepKind.EVM_DECODE_EVENTS,
        config=cc.EvmDecodeEventsConfig(
            event_signature="Transfer(address indexed from, address indexed to, uint256 amount)",
            output_table="transfers",
            allow_decode_fail=True,
        ),
    ),
    # Hex-encode binary fields for readable output
    cc.Step(
        kind=cc.StepKind.HEX_ENCODE,
        config=cc.HexEncodeConfig(),
    ),
]

Step 5: Configure the Writer

The writer defines where transformed data is stored. DuckDB creates a local database file. Other options include ClickHouse, Delta Lake, Iceberg, PostgreSQL, PyArrow Dataset (Parquet), and CSV.

from tiders.config import DuckdbWriterConfig, Writer, WriterKind

writer = Writer(
    kind=WriterKind.DUCKDB,
    config=DuckdbWriterConfig(path='data/transfers.duckdb'),
)

Step 6: Run the Pipeline

The Pipeline ties all parts together. run_pipeline() executes the full ingestion: fetch -> transform -> write.

import asyncio
from tiders import run_pipeline
from tiders.config import Pipeline

pipeline = cc.Pipeline(
    provider=provider,
    query=query,
    writer=writer,
    steps=steps,
)

asyncio.run(run_pipeline(pipeline=pipeline))

Verify the Output

Verify the output by querying the DuckDB file using duckdb-cli:

duckdb data/transfers.db

SHOW TABLES;
SELECT * FROM transfers LIMIT 5;

Next Steps

• Learn about all available providers
• See the full list of transformation steps
• Explore more examples

Choosing a Database

tiders can write data to several backends. This guide helps you pick the right one and get it running.

Which Database Should I Use?

Just getting started? Use DuckDB. It requires no external services — the Your First Pipeline tutorial uses it.

Need a production database? Read on to set up PostgreSQL or ClickHouse with Docker.

DuckDB

DuckDB runs inside your Python process. No server, no Docker, no configuration.

Install

Install tiders with the DuckDB extra dependency.

pip install "tiders[duckdb]"

Querying your data in DuckDB

Open the database file directly using duckdb CLI:

duckdb data/output.duckdb

A few SQL commands to explore:

-- List all tables
SHOW TABLES;
-- Preview data
SELECT * FROM transfers LIMIT 10;
-- Count rows
SELECT count(*) FROM transfers;
-- Exit
.quit

PostgreSQL with Docker

PostgreSQL is a battle-tested relational database going back to 1996. As a row-oriented store, it underperforms in heavy analytical workloads compared to columnar databases like ClickHouse. Use it when you need to read data straight from a pipeline without post-ingestion transformation, or when you want to connect your pipeline data to an existing PostgreSQL instance.

Starting PostgreSQL with Docker

Tiders provides a ready-made Docker Compose file in the tiders/docker_postgres/ folder. Copy this file or paste its contents into your own docker-compose.yaml.

Copy the environment file and edit as needed:

cp .env.example .env

Start the container:

docker compose up -d

When you’re done, stop the database containers:

# Stop the container (data is preserved in the volume)
docker compose down
# Stop and delete all data
docker compose down -v

Install tiders with the PostgreSQL extra dependency.

pip install "tiders[postgresql]"

Querying your data in PostgreSQL with psql

psql is the interactive terminal for PostgreSQL. You can access it through your Docker container:

# Connect via Docker
docker exec -it pg_database psql -U postgres -d tiders
# Or if you have psql installed locally
psql -U postgres -d tiders -h localhost -p 5432

Common psql commands:

Try some queries:

-- Preview your data
SELECT * FROM transfers LIMIT 10;
-- Count rows
SELECT count(*) FROM transfers;
-- Check the PostgreSQL version
SELECT version();

ClickHouse with Docker

ClickHouse is a columnar database built for analytics. It excels at aggregating millions of rows quickly — ideal for blockchain data analysis.

Starting ClickHouse with Docker

Tiders provides a ready-made Docker Compose file in the tiders/docker_clickhouse/ folder. Copy this file or paste its contents into your own docker-compose.yaml.

Copy the environment file and edit as needed:

cp .env.example .env

Start the container:

docker compose up -d

Install tiders with the ClickHouse extra dependency.

pip install "tiders[clickhouse]"

When you’re done, stop the database containers:

# Stop the container (data is preserved in the volume)
docker compose down
# Stop and delete all data
docker compose down -v

Querying your data in ClickHouse

clickhouse-client is the interactive terminal for ClickHouse. Access it through your Docker container:

# Connect via Docker
docker exec -it clickhouse-server clickhouse-client --user default --password secret --database tiders
# Or if you have clickhouse-client installed locally
clickhouse-client --host localhost --port 9000 --user default --password secret --database tiders

You can also use the ClickHouse Web SQL UI at http://localhost:8123/play (assuming default host and port).

Try some queries:

-- List all databases
SHOW DATABASES;
-- List tables in the current database
SHOW TABLES;
-- Show a table's columns and types
DESCRIBE tiders.transfers;
-- Set the default database (so you don't have to prefix with `tiders.`)
USE tiders;
-- Preview your data
SELECT * FROM transfers LIMIT 10;
-- Count rows
SELECT count() FROM transfers;
-- Exit the client (type `exit` or press Ctrl+D)

Parquet Files

Parquet is a column-oriented, binary file (not human-readable) format that offers significantly smaller file sizes, faster query performance, and built-in schema metadata compared to CSV (human-readable). Use it when you want file-based storage without running a database.

Parquet Visualizer is a VS Code extension that lets you browse and run SQL against Parquet files directly in the editor.

You can also read Parquet files with DuckDB, Pandas, Polars, or any tool that supports the format:

# With DuckDB (no server needed)
import duckdb
duckdb.sql("SELECT * FROM 'data/output/transfers/*.parquet' LIMIT 10").show()

# With Pandas
import pandas as pd
df = pd.read_parquet("data/output/transfers/")

# With Polars
import polars as pl
df = pl.read_parquet("data/output/transfers/")

Next Steps

• See the full Writers reference for all configuration options
• Build your first pipeline with the Your First Pipeline tutorial
• Explore examples for complete working pipelines

Development Setup

To develop locally across all repos, clone all three projects side by side:

git clone https://github.com/yulesa/tiders.git
git clone https://github.com/yulesa/tiders-core.git
git clone https://github.com/yulesa/tiders-rpc-client.git

Building tiders-core and tiders-rpc-client from source

If you’re modifying tiders-rpc-client repo locally, you probably want tiders-core to build against your local version.

Build tiders-rpc-client locally:

cd tiders-rpc-client/rust
cargo build

Use local tiders-rpc-clientto build tiders-core, overriding the crates.io version:

cd tiders-core
# Build Rust crates with local tiders-rpc-client
cargo build --config 'patch.crates-io.tiders-rpc-client.path="../tiders-rpc-client/rust"'

# Build Python bindings with the same patch
cd python
maturin develop --config 'patch.crates-io.tiders-rpc-client.path="../../tiders-rpc-client/rust"'
# If using uv
maturin develop --uv --config 'patch.crates-io.tiders-rpc-client.path="../../tiders-rpc-client/rust"'

If you’re modifying tiders-core repo locally, you probably want tiders to use your local tiders-core version.

Build tiders-core as described above, or just cargo build if you haven’t modified tiders-rpc-client.

Use local tiders-core in your enviroment, overriding the PyPI version:

cd tiders
pip install -e ".[all]"
# If using uv
uv pip install -e ".[all]"

Persistent local development

For persistent local development, you can put this in tiders-core/Cargo.toml:

[patch.crates-io]
tiders-rpc-client = { path = "../tiders-rpc-client/rust" }

This avoids passing --config on every build command.

Configure tiders to use your local tiders-core Python package:

[tool.uv.sources]
tiders-core = { path = "../tiders-core/python", editable = true }

cd tiders
uv sync

Tiders Overview (Python SDK)

The tiders Python package is the primary user-facing interface for building blockchain data pipelines.

Two ways to use tiders

Both modes share the same pipeline engine. The CLI parses a YAML config into the same Python objects and calls the same run_pipeline() function.

Installation

pip install tiders tiders-core

For the CLI (no-code mode):

pip install "tiders[cli]"

Core Concepts

A pipeline is built from four components:

Basic Usage

Python

import asyncio
from tiders import config as cc, run_pipeline
from tiders_core import ingest

pipeline = cc.Pipeline(
    provider=ingest.ProviderConfig(kind=ingest.ProviderKind.HYPERSYNC, url="https://eth.hypersync.xyz"),
    query=query,       # see Query docs
    writer=writer,     # see Writers docs
    steps=[...],       # see Steps docs
)

asyncio.run(run_pipeline(pipeline=pipeline))

yaml

provider:
  kind: hypersync
  url: ${PROVIDER_URL}

query:
  kind: evm
  from_block: 18000000

steps: [...]

writer:
  kind: duckdb
  config:
    path: data/output.duckdb

tiders start config.yaml

Module Structure

tiders
├── config          # Pipeline, Step, Writer configuration classes
├── pipeline        # run_pipeline() entry point
├── cli/            # CLI entry point and YAML parser
├── writers/        # Output adapters (DuckDB, ClickHouse, Iceberg, etc.)
└── utils           # Utility functions

Performance Model

tiders parallelizes three phases automatically:

1. Ingestion — fetching data from the provider (async, concurrent)
2. Processing — running transformation steps on each batch
3. Writing — inserting into the output store

The next batch is being fetched while the current batch is being processed and the previous batch is being written.

CLI Overview (No-Code Mode)

The tiders CLI lets you run a complete blockchain data pipeline from a single YAML config file — no Python required.

tiders start config.yaml

How it works

The CLI maps 1:1 to the Python SDK — it parses the YAML into the same Python objects and calls the same run_pipeline() function:

1. Parse — load the YAML file and substitute ${ENV_VAR} placeholders
2. Build — construct ProviderConfig, Query, Steps, and Writer from the config sections
3. Run — call run_pipeline(), identical to Python-mode execution

Commands

tiders start

Run a pipeline from a YAML config file.

tiders start [CONFIG_PATH] [OPTIONS]

Arguments:
  CONFIG_PATH          Path to the YAML config file (optional, default to use the YAML files in the folder)

Options:
  --from-block INTEGER  Override the starting block number from the config
  --to-block   INTEGER  Override the ending block number from the config
  --env-file   PATH     Path to a .env file (overrides default discovery)
  --help                Show this message and exit
  --version             Show the tiders version and exit

tiders codegen

Generate a standalone Python script from a YAML config file. The generated script constructs and runs the same pipeline using the tiders Python SDK — useful as a starting point when you need to customize beyond what YAML supports.

tiders codegen [CONFIG_PATH] [OPTIONS]

Arguments:
  CONFIG_PATH          Path to the YAML config file (optional, same discovery rules as start)

Options:
  -o, --output PATH    Output file path (defaults to <ProjectName>.py in the current directory)
  --env-file   PATH    Path to a .env file (overrides default discovery)
  --help               Show this message and exit

The output filename is derived from the project.name field in the YAML, converted to snake_case (e.g. ERC20 Transfers becomes erc20_transfers.py).

Environment variables referenced in the YAML (e.g. ${PROVIDER_URL}) are emitted as os.environ.get("PROVIDER_URL") calls in the generated script, so secrets stay out of the code.

tiders abi

Fetch contract ABIs from Sourcify or Etherscan and save them as JSON files.

tiders abi [OPTIONS]

Options:
  --address TEXT               Contract address (single-address mode)
  --chain-id TEXT              Chain ID or name (default: 1). See supported chains below
  --yaml-path PATH             Path to YAML file with contract declarations
  -o, --output PATH            Output path. Single-address mode: file path. YAML mode: directory
  --source [sourcify|etherscan] ABI source to try first (default: sourcify). Falls back to the other
  --env-file PATH              Path to a .env file (overrides default discovery)
  --help                       Show this message and exit

Usage modes

1. Single address — fetch one ABI by contract address:

tiders abi --address 0xae78736Cd615f374D3085123A210448E74Fc6393
tiders abi --address 0xae78736Cd615f374D3085123A210448E74Fc6393 --chain-id base

2. From YAML file — fetch ABIs for all contracts declared in the YAML:

tiders abi --yaml-path pipeline.yaml #(optional, autodiscobery in current directory)

The --chain-id option in CLI or in the YAML config accept either a numeric chain ID or a chain name in some chains:

Set ETHERSCAN_API_KEY in your environment or via .env file. Etherscan is skipped with a warning if not set.

Environment variables

Secrets and dynamic values are kept out of the YAML using ${VAR} placeholders:

provider:
  kind: hypersync
  url: ${PROVIDER_URL}
  bearer_token: ${HYPERSYNC_BEARER_TOKEN}

The CLI automatically loads a .env file from the same directory as the config file before substitution. Use --env-file to point to a different location:

tiders start --env-file /path/to/.env config.yaml

An error is raised if any ${VAR} placeholder remains unresolved after substitution.

See the CLI YAML Reference for full details on all sections.

CLI YAML Reference

A tiders YAML config has six top-level sections:

project:       # pipeline metadata (required)
provider:      # data source (required)
contracts:     # ABI + address helpers (optional)
query:         # what data to fetch (required)
steps:         # transformation pipeline (optional)
writer:        # where to write output (required)
table_aliases: # rename default table names (optional)

project

project:
  name: my_pipeline                               # project name
  description: My description.                    # project description
  repository: https://github.com/yulesa/tiders    # optional — informative only
  environment_path: "../../.env"                  # optional — allows to override the .env file path

provider

provider:
  kind: hypersync   # hypersync | sqd | rpc
  url: ${PROVIDER_URL}
  bearer_token: ${TOKEN}   # HyperSync only, optional

See Providers for full details.

contracts

Optional list of contracts. If a ABI path is defined, Tiders reads the events and functions signatures. Addresses, signatures, topic0 and ABI-derived values can be referenced by name anywhere in provider: or query:.

contracts:
  - name: MyToken
    address: "0xabc123..."
    abi: ./MyToken.abi.json
    chain_id: ethereum # numeric chain ID or a chain name for some chains

Reference syntax:

query

The query defines what blockchain data to fetch: the block range, which tables to include, what filters to apply, and which fields to select.

See Query for full details on EVM and SVM query options, field selection, and request filters.

EVM

query:
  kind: evm
  from_block: 18000000
  to_block: 18001000          # optional
  include_all_blocks: false   # optional
  fields:
    log: [address, topic0, topic1, topic2, topic3, data, block_number, transaction_hash, log_index]
    block: [number, timestamp]
    transaction: [hash, from, to, value]
    trace: [action_from, action_to, action_value]
  logs:
    - topic0: "Transfer(address,address,uint256)"  # signature or 0x hex
      address: "0xabc..."
      include_blocks: true
  transactions:
    - from: ["0xabc..."]
      include_blocks: true
  traces:
    - action_from: ["0xabc..."]

SVM

query:
  kind: svm
  from_block: 330000000
  to_block: 330001000
  include_all_blocks: true
  fields:
    instruction: [block_slot, program_id, data, accounts]
    transaction: [signature, fee]
    block: [slot, timestamp]
  instructions:
    - program_id: ["JUP6LkbZbjS1jKKwapdHNy74zcZ3tLUZoi5QNyVTaV4"]
      include_transactions: true
  transactions:
    - signer: ["0xabc..."]
  logs:
    - kind: [program, system_program]
  balances:
    - account: ["0xabc..."]
  token_balances:
    - mint: ["..."]
  rewards:
    - pubkey: ["..."]

steps

Steps are transformations applied to each batch of data before writing. They run in order and can decode, cast, encode, join, or apply custom logic.

See Steps for full details on each step kind.

evm_decode_events

Decode EVM log events using an ABI signature

- kind: evm_decode_events
  config:
    event_signature: "Transfer(address indexed from, address indexed to, uint256 amount)"
    output_table: transfers        # optional — name of the output table for decoded results, default: "decoded_logs"
    input_table: logs              # optional — name of the input table to decode, default: "logs"
    allow_decode_fail: true        # optional — when True rows that fails are nulls values instead of raising an error, default: False
    filter_by_topic0: false        # optional — when True only rows whose ``topic0`` matches the event topic0 are decoded, default: False
    hstack: true                   # optional — when True decoded columns are horizontally stacked with the input columns, default: True

svm_decode_instructions

Decode Solana program instructions

- kind: svm_decode_instructions
  config:
    instruction_signature:
      discriminator: "0xe517cb977ae3ad2a"  # The instruction discriminator bytes used to identify the instruction type.
      params:                              # The list of typed parameters to decode from the instruction data (after the discriminator).
        - name: amount
          type: u64
        - name: data
          type: { type: array, element: u8 }
      accounts_names: [tokenAccountIn, tokenAccountOut] #  Names assigned to positional accounts in the instruction.
    allow_decode_fail: false              # optional — when True, rows that fails are nulls values instead of raising an error, default: False
    filter_by_discriminator: false        # optional — when True, only rows whose data starting bytes matches the event topic0 are decoded, default: False
    input_table: instructions             # optional — name of the input table to decode, default: "instructions"
    output_table: decoded_instructions    # optional — name of the input table to decode, default: "decoded_instructions"
    hstack: true                          # optional — when True, decoded columns are horizontally stacked with the input columns, default: True

svm_decode_logs

Decode Solana program logs

- kind: svm_decode_logs
  config:
    log_signature:              # The list of typed parameters to decode from the log data.
      params:
        - name: amount_in
          type: u64
        - name: amount_out
          type: u64
    allow_decode_fail: false    # optional — when True rows that fails are nulls values instead of raising an error, default: False
    input_table: logs           # optional — name of the input table to decode, default: "logs"
    output_table: decoded_logs  # optional — name of the input table to decode, default: "decoded_logs"
    hstack: true                # optional — when True decoded columns are horizontally stacked with the input columns, default: True

cast_by_type

- kind: cast_by_type
  config:
    from_type: "decimal256(76,0)" # The source pyarrow.DataType to match.
    to_type: "decimal128(38,0)"   # The target pyarrow.DataType to cast
    allow_cast_fail: true         # optional — when True, values that cannot be cast are set to null instead of raising an error, default: False

Supported type strings: int8–int64, uint8–uint64, float16–float64, string, utf8, large_string, binary, large_binary, bool, date32, date64, null, decimal128(p,s), decimal256(p,s).

cast

Cast all columns of one type to another

- kind: cast
  config:
    table_name: transfers         # The name of the table whose columns should be cast.
    mappings:                     # A mapping of column name to target pyarrow.DataType
      amount: "decimal128(38,0)"
      block_number: "int64"
    allow_cast_fail: false        # optional — When True, values that cannot be cast are set to null instead of raising an error, default: False

Supported type strings: int8–int64, uint8–uint64, float16–float64, string, utf8, large_string, binary, large_binary, bool, date32, date64, null, decimal128(p,s), decimal256(p,s).

hex_encode

Hex-encode all binary columns

- kind: hex_encode
  config:
    tables: [transfers]   # optional — list of table names to process. When ``None``, all tables in the data dictionary are processed, default: None
    prefixed: true        # optional — When True, output strings are "0x"-prefixed, default: True

base58_encode

Base58-encode all binary columns

- kind: base58_encode
  config:
    tables: [instructions]   # optional — list of table names to process. When ``None``, all tables in the data dictionary are processed, default: None

join_block_data

Join block fields into other tables (left outer join). Column collisions are prefixed with <block_table_name>_.

- kind: join_block_data
  config:
    tables: [logs]               # optional — tables to join into; default: all tables except the block table
    block_table_name: blocks     # optional, default: "blocks"
    join_left_on: [block_number] # optional, default: ["block_number"]
    join_blocks_on: [number]     # optional, default: ["number"]

join_evm_transaction_data

Join EVM transaction fields into other tables (left outer join). Column collisions are prefixed with <tx_table_name>_.

- kind: join_evm_transaction_data
  config:
    tables: [logs]                                           # optional — tables to join into; default: all except the transactions table
    tx_table_name: transactions                              # optional, default: "transactions"
    join_left_on: [block_number, transaction_index]          # optional, default: ["block_number", "transaction_index"]
    join_transactions_on: [block_number, transaction_index]  # optional, default: ["block_number", "transaction_index"]

join_svm_transaction_data

Join SVM transaction fields into other tables (left outer join). Column collisions are prefixed with <tx_table_name>_.

- kind: join_svm_transaction_data
  config:
    tables: [instructions]                                  # optional — tables to join into; default: all except the transactions table
    tx_table_name: transactions                             # optional, default: "transactions"
    join_left_on: [block_slot, transaction_index]           # optional, default: ["block_slot", "transaction_index"]
    join_transactions_on: [block_slot, transaction_index]   # optional, default: ["block_slot", "transaction_index"]

set_chain_id

Add a chain_id column

- kind: set_chain_id
  config:
    chain_id: 1  # The chain identifier to set (e.g. 1 for Ethereum mainnet).

sql

Run one or more DataFusion SQL queries. CREATE TABLE name AS SELECT ... stores results under name; plain SELECT stores as sql_result.

- kind: sql
  config:
    queries:
      - >
        CREATE TABLE enriched AS
        SELECT t.*, b.timestamp
        FROM transfers t
        JOIN blocks b ON b.number = t.block_number

python_file

Load a custom step function from an external Python file. Paths are relative to the YAML config directory.

- kind: python_file
  name: my_custom_step
  config:
    file: ./steps/my_step.py
    function: transform          # callable name in the file
    step_type: datafusion        # datafusion (default), polars, or pandas
    context:                     # optional — passed as ctx to the function
      threshold: 100

writer

See Writers for full details.

writer accepts either a single writer mapping or a list of writer mappings to write to multiple backends in parallel:

writer:
  - kind: duckdb
    config:
      path: data/output.duckdb
  - kind: csv
    config:
      base_dir: data/output

DuckDB

writer:
  kind: duckdb
  config:
    path: data/output.duckdb   # path to create or connect to a duckdb database

ClickHouse

writer:
  kind: clickhouse
  config:
    host: localhost            # ClickHouse server hostname
    port: 8123                 # ClickHouse HTTP port
    username: default          # ClickHouse username
    password: ${CH_PASSWORD}   # ClickHouse password
    database: default          # ClickHouse database name
    secure: false              # optional — use TLS, default: false
    codec: LZ4                 # optional — default compression codec for all columns
    order_by:                  # optional — per-table ORDER BY columns
      transfers: [block_number, log_index]
    engine: MergeTree()        # optional — ClickHouse table engine, default: MergeTree()
    anchor_table: transfers    # optional — table written last, for ordering guarantees
    create_tables: true        # optional — auto-create tables on first insert, default: true

Delta Lake

writer:
  kind: delta_lake
  config:
    data_uri: s3://my-bucket/delta/   # base URI where Delta tables are stored
    partition_by: [block_number]      # optional — columns used for partitioning
    storage_options:                  # optional — cloud storage credentials/options
      AWS_REGION: us-east-1
      AWS_ACCESS_KEY_ID: ${AWS_KEY}
    anchor_table: transfers           # optional — table written last, for ordering guarantees

Iceberg

writer:
  kind: iceberg
  config:
    namespace: my_namespace                  # Iceberg namespace (database) to write tables into
    catalog_uri: sqlite:///catalog.db        # URI for the Iceberg catalog (e.g. sqlite or jdbc)
    warehouse: s3://my-bucket/iceberg/       # warehouse root URI for the catalog
    catalog_type: sql                        # catalog type (e.g. sql, rest, hive)
    write_location: s3://my-bucket/iceberg/  # storage URI where Iceberg data files are written

PyArrow Dataset (Parquet)

writer:
  kind: pyarrow_dataset
  config:
    base_dir: data/output          # root directory for all output datasets
    anchor_table: transfers        # optional — table written last, for ordering guarantees
    partitioning: [block_number]   # optional — columns or Partitioning object per table
    partitioning_flavor: hive      # optional — partitioning flavor (e.g. hive)
    max_rows_per_file: 1000000     # optional — max rows per output file, default: 0 (unlimited)
    create_dir: true               # optional — create output directory if missing, default: true

CSV

writer:
  kind: csv
  config:
    base_dir: data/output        # required — root directory for all output CSV files
    delimiter: ","               # optional, default: ","
    include_header: true         # optional, default: true
    create_dir: true             # optional — create output directory if missing, default: true
    anchor_table: transfers      # optional — table written last, for ordering guarantees

PostgreSQL

writer:
  kind: postgresql
  config:
    host: localhost               # required — PostgreSQL server hostname
    dbname: postgres              # optional, default: postgres
    port: 5432                    # optional, default: 5432
    user: postgres                # optional, default: postgres
    password: ${PG_PASSWORD}      # optional, default: postgres
    schema: public                # optional — PostgreSQL schema (namespace), default: public
    create_tables: true           # optional — auto-create tables on first push, default: true
    anchor_table: transfers       # optional — table written last, for ordering guarantees

table_aliases

Rename the default ingestion table names.

EVM

table_aliases:
  blocks: my_blocks     # optional — name for the blocks response, default: "blocks"
  transactions: my_txs  # optional — name for the transactions response, default: "transactions"
  logs: my_logs         # optional — name for the logs response, default: "logs"
  traces: my_traces     # optional — name for the traces response, default: "traces"

SVM

table_aliases:
  instructions: my_instructions       # optional — name for the instructions response, default: "instructions"
  transactions: my_txs                # optional — name for the transactions response, default: "transactions"
  logs: my_logs                       # optional — name for the logs response, default: "logs"
  balances: my_balances               # optional — name for the balances response, default: "balances"
  token_balances: my_token_balances   # optional — name for the token_balances response, default: "token_balances"
  rewards: my_rewards                 # optional — name for the rewards response, default: "rewards"
  blocks: my_blocks                   # optional — name for the blocks response, default: "blocks"

Providers

Providers are the data sources that tiders fetches blockchain data from. Each provider connects to a different backend service.

Available Providers

Configuration

All providers use ProviderConfig from tiders_core.ingest:

from tiders_core.ingest import ProviderConfig, ProviderKind

Common Parameters

These parameters are available for all providers:

RPC-only Parameters

HyperSync

Python

provider = ProviderConfig(
    kind=ProviderKind.HYPERSYNC,
    url="https://eth.hypersync.xyz",
    bearer_token = HYPERSYNC_TOKEN
)

yaml

provider:
  kind: hypersync
  url: ${PROVIDER_URL}
  bearer_token: ${HYPERSYNC_BEARER_TOKEN}   # optional

SQD

Python

provider = ProviderConfig(
    kind=ProviderKind.SQD,
    url="https://portal.sqd.dev/datasets/ethereum-mainnet",
)

yaml

provider:
  kind: sqd
  url: ${PROVIDER_URL}

RPC

Use any standard EVM JSON-RPC endpoint (Alchemy, Infura, QuickNode, local node, etc.):

Python

provider = ProviderConfig(
    kind=ProviderKind.RPC,
    url="https://eth-mainnet.g.alchemy.com/v2/YOUR_KEY",
)

yaml

provider:
  kind: rpc
  url: ${PROVIDER_URL}
  stop_on_head: true                     # optional, default: false
  trace_method: trace_block              # optional — trace_block or debug_trace_block_by_number

The RPC provider uses tiders-rpc-client under the hood, which supports adaptive concurrency, retry logic, and streaming.

Choosing a Provider

• HyperSync — fast EVM historical data, allow request filtering; requires API key
• SQD — fast, supports both EVM and SVM, allow request filtering; decentralized
• RPC — works with traditional RPC, don’t allow request filtering; useful when other providers don’t support your chain

Contracts

Contracts is an optional module that makes it easier to get contract information, such as Events, Functions and their params.

When you define a contract, tiders parses the ABI JSON file and extracts all events and functions with their signatures, selectors, and topic hashes — so you don’t have to compute or hard-code them yourself.

Event/Functions Fields

Each event/function parsed from the ABI exposes:

YAML Usage

In YAML configs, define contracts under the contracts: key. Tiders automatically parses the ABI and makes all values available via reference syntax.

contracts:
  - name: MyToken
    address: "0xae78736Cd615f374D3085123A210448E74Fc6393"
    abi: ./MyToken.abi.json # abi path
    chain_id: ethereum      # numeric chain ID or a chain name in some chains (used to download the ABI in the CLI command `tiders abi`)

Reference Syntax

Once a contract is defined, tiders parse will automatically extract ABI information, so you can reference its address, events, and functions by name anywhere in query: or steps: sections:

Python Usage

Use evm_abi_events and evm_abi_functions from tiders_core. These functions take a JSON ABI string and return a list[EvmAbiEvent] / list[EvmAbiFunction] with the fields described above.

from pathlib import Path
from tiders_core import evm_abi_events, evm_abi_functions

# Contract address
my_token_address = "0xae78736Cd615f374D3085123A210448E74Fc6393"
# Load ABI
abi_path = Path("./MyToken.abi.json")
abi_json = abi_path.read_text()

# Parse events — dict keyed by event name
events = {
    ev.name: {
        "topic0": ev.topic0,
        "signature": ev.signature,
        "name_snake_case": ev.name_snake_case,
        "selector_signature": ev.selector_signature,
    }
    for ev in evm_abi_events(abi_json)
}

# Parse functions — dict keyed by function name
functions = {
    fn.name: {
        "selector": fn.selector,
        "signature": fn.signature,
        "name_snake_case": fn.name_snake_case,
        "selector_signature": fn.selector_signature,
    }
    for fn in evm_abi_functions(abi_json)
}

You can then use the parsed values in your query and steps:

query = Query(
    kind=QueryKind.EVM,
    params=evm.Query(
        from_block=18_000_000,
        logs=[
            evm.LogRequest(
                address=[my_token_address],
                topic0=[events["Transfer"]["topic0"]],
            ),
        ],
    ),
)

steps = [
    Step(
        kind=StepKind.EVM_DECODE_EVENTS,
        config=EvmDecodeEventsConfig(
            event_signature=events["Transfer"]["signature"],
        ),
    ),
]

Query

The query defines what blockchain data to fetch: the block range, which tables to include, what filters to apply, and which fields to select. Queries are specific to a blockchain type (Kind), and can be either:

• EVM (for Ethereum and compatible chains) or
• SVM (for Solana).

Each query consists of a request to select subsets of tables/data (block, logs, instructions) and field selectors to specify what columns should be included in the response for each table.

Structure

from tiders_core.ingest import Query, QueryKind

A query has:

• kind — QueryKind.EVM or QueryKind.SVM
• params — chain-specific query parameters

EVM Queries

Python

from tiders_core.ingest import evm

query = Query(
    kind=QueryKind.EVM,
    params=evm.Query(
        from_block=18_000_000,          # required
        to_block=18_001_000,            # optional — defaults to chain head
        include_all_blocks=False,       # optional — include blocks with no matching data
        logs=[evm.LogRequest(...)],     # optional — log filters
        transactions=[evm.TransactionRequest(...)],  # optional — transaction filters
        traces=[evm.TraceRequest(...)], # optional — trace filters
        fields=evm.Fields(...),         # field selection
    ),
)

yaml

query:
  kind: evm
  from_block: 18000000
  to_block: 18001000          # optional — defaults to chain head
  include_all_blocks: false   # optional — default: false
  logs: [...]
  transactions: [...]
  traces: [...]
  fields: {...}

EVM Table filters

The logs, transactionsand traces params enable fine-grained row filtering through [table]Request objects. Each request individually filters for a subset of rows in the tables. You can combine multiple requests to build complex queries tailored to your needs. Except for blocks, table selection is made through explicit inclusion in a dedicated request or an include_[table] parameter.

Log Requests

Filter event logs by contract address and/or topic. All filter fields are combined with OR logic within a field and AND logic across fields.

Python

evm.LogRequest(
    address=["0xabc..."],       # optional — list of log emitter addresses
    topic0=["0xabc..."],        # optional — list of keccak256 hash or event signature
    topic1=["0xabc..."],        # optional — list of first indexed parameter
    topic2=["0xabc..."],        # optional — list of second indexed parameter
    topic3=["0xabc..."],        # optional — list of third indexed parameter
    include_transactions=False,           # optional — include parent transaction
    include_transaction_logs=False,       # optional — include all logs from matching txs
    include_transaction_traces=False,     # optional — include traces from matching txs
    include_blocks=True,                  # optional — include block data
)

yaml

query:
  kind: evm
  logs:
    - address: "0xdabc..."                           # optional
      topic0: "Transfer(address,address,uint256)"    # optional — signature or 0x hex hash
      topic1: "0xabc..."                             # optional
      topic2: "0xabc..."                             # optional
      topic3: "0xabc..."                             # optional
      include_transactions: false                    # optional, default: false
      include_transaction_logs: false                # optional, default: false
      include_transaction_traces: false              # optional, default: false
      include_blocks: true                           # optional, default: false

Transaction Requests

Filter transactions by sender, recipient, function selector, or other fields. Filtering transaction data at the source in a request is not supported by standart ETH JSON-RPC calls of RPC providers.

All filter fields are combined with OR logic within a field and AND logic across fields.

Python

evm.TransactionRequest(
    from_=["0xabc..."],                   # optional — list of sender addresses
    to=["0xabc..."],                      # optional — list of recipient addresses
    sighash=["0xa9059cbb"],               # optional — list of 4-byte function selectors (hex)
    status=[1],                           # optional — list of status, 1=success, 0=failure
    type_=[2],                            # optional — list of type, 0=legacy, 1=access list, 2=EIP-1559
    contract_deployment_address=["0x..."],# optional — list of deployed contract addresses
    hash=["0xabc..."],                    # optional — list of specific transaction hashes
    include_logs=False,                   # optional — include emitted logs
    include_traces=False,                 # optional — include execution traces
    include_blocks=False,                 # optional — include block data
)

yaml

query:
  kind: evm
  transactions:
    - from: ["0xabc..."]                        # optional
      to: ["0xabc..."]                          # optional
      sighash: ["0xa9059cbb"]                   # optional
      status: [1]                               # optional
      type: [2]                                 # optional
      contract_deployment_address: ["0x..."]    # optional
      hash: ["0xabc..."]                        # optional
      include_logs: false                       # optional, default: false
      include_traces: false                     # optional, default: false
      include_blocks: false                     # optional, default: false

Trace Requests

Filter execution traces (internal transactions). Filtering trace data at the source in a request is not supported by standart ETH JSON-RPC calls of RPC providers.

All filter fields are combined with OR logic within a field and AND logic across fields.

Python

evm.TraceRequest(
    from_=["0xabc..."],                   # optional — list of caller addresses
    to=["0xabc..."],                      # optional — list of callee addresses
    address=["0xabc..."],                 # optional — list of ontract addresses in the trace
    call_type=["call"],                   # optional — list of call types, "call", "delegatecall", "staticcall"
    reward_type=["block"],                # optional — list of reward_type, "block", "uncle"
    type_=["call"],                       # optional — list of trace type, "call", "create", "suicide"
    sighash=["0xa9059cbb"],               # optional — list of 4-byte function selectors
    author=["0xabc..."],                  # optional — list of block reward author addresses
    include_transactions=False,           # optional — include parent transaction
    include_transaction_logs=False,       # optional — include logs from matching txs
    include_transaction_traces=False,     # optional — include all traces from matching txs
    include_blocks=False,                 # optional — include block data
)

yaml

query:
  kind: evm
  traces:
    - from: ["0xabc..."]                        # optional
      to: ["0xabc..."]                          # optional
      address: ["0xabc..."]                     # optional
      call_type: ["call"]                       # optional — call, delegatecall, staticcall
      reward_type: ["block"]                    # optional — block, uncle
      type: ["call"]                            # optional — call, create, suicide
      sighash: ["0xa9059cbb"]                   # optional
      author: ["0xabc..."]                      # optional
      include_transactions: false               # optional, default: false
      include_transaction_logs: false           # optional, default: false
      include_transaction_traces: false         # optional, default: false
      include_blocks: false                     # optional, default: false

EVM Field Selection

Select only the columns you need. All fields default to false.

Python

evm.Fields(
    block=evm.BlockFields(number=True, timestamp=True, hash=True),
    transaction=evm.TransactionFields(hash=True, from_=True, to=True, value=True),
    log=evm.LogFields(block_number=True, address=True, topic0=True, data=True),
    trace=evm.TraceFields(from_=True, to=True, value=True, call_type=True),
)

yaml

query:
  kind: evm
  fields:
    block: [number, timestamp, hash]
    transaction: [hash, from, to, value]
    log: [block_number, address, topic0, data]
    trace: [from, to, value, call_type]

Available Block Fields

number, hash, parent_hash, nonce, sha3_uncles, logs_bloom, transactions_root, state_root, receipts_root, miner, difficulty, total_difficulty, extra_data, size, gas_limit, gas_used, timestamp, uncles, base_fee_per_gas, blob_gas_used, excess_blob_gas, parent_beacon_block_root, withdrawals_root, withdrawals, l1_block_number, send_count, send_root, mix_hash

Available Transaction Fields

block_hash, block_number, from, gas, gas_price, hash, input, nonce, to, transaction_index, value, v, r, s, max_priority_fee_per_gas, max_fee_per_gas, chain_id, cumulative_gas_used, effective_gas_price, gas_used, contract_address, logs_bloom, type, root, status, sighash, y_parity, access_list, l1_fee, l1_gas_price, l1_fee_scalar, gas_used_for_l1, max_fee_per_blob_gas, blob_versioned_hashes, deposit_nonce, blob_gas_price, deposit_receipt_version, blob_gas_used, l1_base_fee_scalar, l1_blob_base_fee, l1_blob_base_fee_scalar, l1_block_number, mint, source_hash

Available Log Fields

removed, log_index, transaction_index, transaction_hash, block_hash, block_number, address, data, topic0, topic1, topic2, topic3

Available Trace Fields

from, to, call_type, gas, input, init, value, author, reward_type, block_hash, block_number, address, code, gas_used, output, subtraces, trace_address, transaction_hash, transaction_position, type, error, sighash, action_address, balance, refund_address

SVM Queries

Python

from tiders_core.ingest import svm

query = Query(
    kind=QueryKind.SVM,
    params=svm.Query(
        from_block=330_000_000,
        to_block=330_001_000,               # optional — defaults to chain head
        include_all_blocks=False,           # optional — include blocks with no matching data
        instructions=[svm.InstructionRequest(...)],     # optional
        transactions=[svm.TransactionRequest(...)],     # optional
        logs=[svm.LogRequest(...)],                     # optional
        balances=[svm.BalanceRequest(...)],             # optional
        token_balances=[svm.TokenBalanceRequest(...)],  # optional
        rewards=[svm.RewardRequest(...)],               # optional
        fields=svm.Fields(...),
    ),
)

yaml

query:
  kind: svm
  from_block: 330000000
  to_block: 330001000          # optional — defaults to chain head
  include_all_blocks: false    # optional — default: false
  instructions: [...]
  transactions: [...]
  logs: [...]
  balances: [...]
  token_balances: [...]
  rewards: [...]
  fields: {...}

SVM Table filters

The instructions, transactions, logs, balances , token_balances, rewards and fields params enable fine-grained row filtering through [table]Request objects. Each request individually filters for a subset of rows in the tables. You can combine multiple requests to build complex queries tailored to your needs. Except for blocks, table selection is made through explicit inclusion in a dedicated request or an include_[table] parameter.

Instruction Requests

Filter Solana instructions by program, discriminator, or account. Discriminator and account filters (d1–d8, a0–a9) use OR logic within a field and AND logic across fields.

Python

svm.InstructionRequest(
    program_id=["JUP6LkbZbjS1jKKwapdHNy74zcZ3tLUZoi5QNyVTaV4"],  # optional — list of program ids, base58
    discriminator=["0xe445a52e51cb9a1d40c6cde8260871e2"],           # optional — list of discriminators, bytes or hex
    d1=["0xe4"],          # optional — list of 1-byte data prefix filter
    d2=["0xe445"],        # optional — list of 2-byte data prefix filter
    d4=["0xe445a52e"],    # optional — list of 4-byte data prefix filter
    d8=["0xe445a52e51cb9a1d"],  # optional — list of 8-byte data prefix filter
    a0=["0xabc..."],      # optional — list of account at index 0 (base58)
    a1=["0xabc..."],      # optional — list of account at index 1
    # a2–a9 follow the same pattern
    is_committed=False,                         # optional — only committed instructions
    include_transactions=True,                  # optional — include parent transaction
    include_transaction_token_balances=False,    # optional — include token balance changes
    include_logs=False,                         # optional — include program logs
    include_inner_instructions=False,           # optional — include inner (CPI) instructions
    include_blocks=True,                        # optional — default: true
)

yaml

query:
  kind: svm
  instructions:
    - program_id: ["JUP6LkbZbjS1jKKwapdHNy74zcZ3tLUZoi5QNyVTaV4"]   # optional
      discriminator: ["0xe445a52e51cb9a1d40c6cde8260871e2"]            # optional
      d1: ["0xe4"]         # optional
      d2: ["0xe445"]       # optional
      d4: ["0xe445a52e"]   # optional
      d8: ["0xe445a52e51cb9a1d"]   # optional
      a0: ["TokenkegQfeZyiNwAJbNbGKPFXCWuBvf9Ss623VQ5DA"]   # optional
      is_committed: false                        # optional, default: false
      include_transactions: true                 # optional, default: false
      include_transaction_token_balances: false  # optional, default: false
      include_logs: false                        # optional, default: false
      include_inner_instructions: false          # optional, default: false
      include_blocks: true                       # optional, default: true

Transaction Requests (SVM)

Filter Solana transactions by fee payer.

Python

svm.TransactionRequest(
    fee_payer=["0xabc..."],       # optional — list of fee payer public keys (base58)
    include_instructions=False,   # optional — include all instructions
    include_logs=False,           # optional — include program logs
    include_blocks=False,         # optional — include block data
)

yaml

query:
  kind: svm
  transactions:
    - fee_payer: ["0xabc..."]       # optional
      include_instructions: false   # optional, default: false
      include_logs: false           # optional, default: false
      include_blocks: false         # optional, default: false

Log Requests (SVM)

Filter Solana program log messages by program ID and/or log kind.

Python

svm.LogRequest(
    program_id=["JUP6LkbZbjS1jKKwapdHNy74zcZ3tLUZoi5QNyVTaV4"],  # optional — list of program ids
    kind=[svm.LogKind.LOG],       # optional — list of kinds, log, data, other
    include_transactions=False,   # optional — include parent transaction
    include_instructions=False,   # optional — include the emitting instruction
    include_blocks=False,         # optional — include block data
)

yaml

query:
  kind: svm
  logs:
    - program_id: ["JUP6LkbZbjS1jKKwapdHNy74zcZ3tLUZoi5QNyVTaV4"]   # optional
      kind: [log]                    # optional — log, data, other
      include_transactions: false    # optional, default: false
      include_instructions: false    # optional, default: false
      include_blocks: false          # optional, default: false

Balance Requests

Filter native SOL balance changes by account.

Python

svm.BalanceRequest(
    account=["0xabc..."],                     # optional — list of account public keys (base58)
    include_transactions=False,               # optional — include parent transaction
    include_transaction_instructions=False,   # optional — include transaction instructions
    include_blocks=False,                     # optional — include block data
)

yaml

query:
  kind: svm
  balances:
    - account: ["0xabc..."]                       # optional — list of accounts
      include_transactions: false                 # optional, default: false
      include_transaction_instructions: false     # optional, default: false
      include_blocks: false                       # optional, default: false

Token Balance Requests

Filter SPL token balance changes. Pre/post filters match the state before and after the transaction.

Python

svm.TokenBalanceRequest(
    account=["0xabc..."],               # optional — list of token account public keys (base58)
    pre_program_id=["TokenkegQ..."],    # optional — list of token program ID before tx
    post_program_id=["TokenkegQ..."],   # optional — list of token program ID after tx
    pre_mint=["0xabc..."],              # optional — list of token mint address before tx
    post_mint=["0xabc..."],             # optional — list of token mint address after tx
    pre_owner=["0xabc..."],             # optional — list of token account owner before tx
    post_owner=["0xabc..."],            # optional — list of token account owner after tx
    include_transactions=False,                 # optional — include parent transaction
    include_transaction_instructions=False,     # optional — include transaction instructions
    include_blocks=False,                       # optional — include block data
)

yaml

query:
  kind: svm
  token_balances:
    - account: ["0xabc..."]                       # optional
      pre_mint: ["0xabc..."]                      # optional
      post_mint: ["0xabc..."]                     # optional
      pre_owner: ["0xabc..."]                     # optional
      post_owner: ["0xabc..."]                    # optional
      pre_program_id: ["TokenkegQ..."]            # optional
      post_program_id: ["TokenkegQ..."]           # optional
      include_transactions: false                 # optional, default: false
      include_transaction_instructions: false     # optional, default: false
      include_blocks: false                       # optional, default: false

Reward Requests

Filter Solana validator reward records by public key.

Python

svm.RewardRequest(
    pubkey=["0xabc..."],   # optional — list of validator public keys (base58)
    include_blocks=False,  # optional — include block data
)

yaml

query:
  kind: svm
  rewards:
    - pubkey: ["0xabc..."]   # optional
      include_blocks: false  # optional, default: false

SVM Field Selection

Select only the columns you need. All fields default to false.

Python

svm.Fields(
    instruction=svm.InstructionFields(block_slot=True, program_id=True, data=True),
    transaction=svm.TransactionFields(signature=True, fee=True),
    log=svm.LogFields(program_id=True, message=True),
    balance=svm.BalanceFields(account=True, pre=True, post=True),
    token_balance=svm.TokenBalanceFields(account=True, post_mint=True, post_amount=True),
    reward=svm.RewardFields(pubkey=True, lamports=True, reward_type=True),
    block=svm.BlockFields(slot=True, hash=True, timestamp=True),
)

yaml

query:
  kind: svm
  fields:
    instruction: [block_slot, program_id, data]
    transaction: [signature, fee]
    log: [program_id, message]
    balance: [account, pre, post]
    token_balance: [account, post_mint, post_amount]
    reward: [pubkey, lamports, reward_type]
    block: [slot, hash, timestamp]

Available Instruction Fields

block_slot, block_hash, transaction_index, instruction_address, program_id, a0–a9, rest_of_accounts, data, d1, d2, d4, d8, error, compute_units_consumed, is_committed, has_dropped_log_messages

Available Transaction Fields (SVM)

block_slot, block_hash, transaction_index, signature, version, account_keys, address_table_lookups, num_readonly_signed_accounts, num_readonly_unsigned_accounts, num_required_signatures, recent_blockhash, signatures, err, fee, compute_units_consumed, loaded_readonly_addresses, loaded_writable_addresses, fee_payer, has_dropped_log_messages

Available Log Fields (SVM)

block_slot, block_hash, transaction_index, log_index, instruction_address, program_id, kind, message

Available Balance Fields

block_slot, block_hash, transaction_index, account, pre, post

Available Token Balance Fields

block_slot, block_hash, transaction_index, account, pre_mint, post_mint, pre_decimals, post_decimals, pre_program_id, post_program_id, pre_owner, post_owner, pre_amount, post_amount

Available Reward Fields

block_slot, block_hash, pubkey, lamports, post_balance, reward_type, commission

Available Block Fields (SVM)

slot, hash, parent_slot, parent_hash, height, timestamp

Transformation Steps

Transformation steps are built-in processing operations that are applied sequentially during pipeline execution. They run in order and can decode, cast, encode, join, or apply custom logic.

Here’s an overview of how transformation steps work:

1. Select Step: Each transformation step defines a series of operations. This can range from data validation, decoding, encoding, and joining data, to custom transformations.
2. Step Configuration: Each step has a configuration object that defines input parameters and/or modifies behavior. For example, EvmDecodeEventsConfig requires an event_signature and has configs for horizontally stacking raw and decoded columns, not stopping on failed rows, and naming the output table.
3. Process Flow: Steps are executed in the order they are provided. After each step, the data is updated, and the transformed data is passed to the next step in the pipeline.

Built-in Steps

EVM Decode Events

Decodes raw log data and topic fields into typed columns using an ABI event signature.

Python

cc.Step(
    kind=cc.StepKind.EVM_DECODE_EVENTS,
    config=cc.EvmDecodeEventsConfig(
        event_signature="Transfer(address indexed from, address indexed to, uint256 amount)",
        output_table="token_transfers",   # optional — name of the output table for decoded results, default: "decoded_logs"
        input_table="logs",               # optional — name of the input table to decode, default: "logs"
        allow_decode_fail=True,           # optional — when True rows that fails are nulls values instead of raising an error, default: False
        filter_by_topic0=False,           # optional — when True only rows whose ``topic0`` matches the event topic0 are decoded, default: False
        hstack=True,                      # optional — when True decoded columns are horizontally stacked with the input columns, default: True input columns
    ),
)

yaml

- kind: evm_decode_events
  config:
    event_signature: "Transfer(address indexed from, address indexed to, uint256 amount)"
    output_table: transfers       # optional, default: decoded_logs
    input_table: logs             # optional, default: logs
    allow_decode_fail: false      # optional, default: false
    filter_by_topic0: false       # optional, default: false
    hstack: true                  # optional, default: true

SVM Decode Instructions

Decodes raw Solana instruction data into structured columns using an Anchor/Borsh instruction signature.

Python

from tiders_core.svm_decode import InstructionSignature

cc.Step(
    kind=cc.StepKind.SVM_DECODE_INSTRUCTIONS,
    config=cc.SvmDecodeInstructionsConfig(
        instruction_signature=InstructionSignature(...),
        output_table="decoded_instructions",  # optional — name of the input table to decode, default: "decoded_instructions"
        input_table="instructions",           # optional — name of the input table to decode, default: "instructions"
        allow_decode_fail=False,              # optional — when True, rows that fails are nulls values instead of raising an error, default: False
        filter_by_discriminator=False,        # optional — when True, only rows whose data starting bytes matches the event topic0 are decoded, default: False
        hstack=True,                          # optional — when True, decoded columns are horizontally stacked with the input columns, default: True
    ),
)

yaml

- kind: svm_decode_instructions
  config:
    instruction_signature: {...}               # required
    output_table: decoded_instructions         # optional, default: "decoded_instructions"
    input_table: instructions                  # optional, default: "instructions"
    allow_decode_fail: false                   # optional, default: false
    filter_by_discriminator: false             # optional, default: false
    hstack: true                               # optional, default: true

Instruction Signature

Signatures objects serve as decoding blueprints: they describe the expected structure of an instruction — including the name, type, and length of each parameter — allowing the pipeline to parse and interpret the raw byte data reliably.

Users can construct these signatures by gathering information from a variety of sources:

• Published IDLs (when available)
• Program source code (typically in Rust)
• Manual inspection of raw instructions on a Solana explorer

Here’s an example of an instruction signature for decoding Jupiter swap instructions. We’ll break down each part below:

instruction_signature = InstructionSignature(
    discriminator="0xe445a52e51cb9a1d40c6cde8260871e2",
    params=[
        ParamInput(
            name="Amm",
            param_type=FixedArray(DynType.U8, 32),
        ),
        ParamInput(
            name="InputMint",
            param_type=FixedArray(DynType.U8, 32),
        ),
        ParamInput(
            name="InputAmount",
            param_type=DynType.U64,
        ),
        ParamInput(
            name="OutputMint",
            param_type=FixedArray(DynType.U8, 32),
        ),
        ParamInput(
            name="OutputAmount",
            param_type=DynType.U64,
        ),
    ],
    accounts_names=[],
)

Discriminators

A discriminator is a fixed sequence of bytes at the beginning of serialized data that identifies which instruction, struct, or event the data represents. During decoding, the discriminator matches raw data to the correct signature definition.

Discriminators are one of the most challenging parts to reverse-engineer because Solana has no standard for defining them. Here are some common patterns observed in real-world programs:

• Sequential values: Some programs use simple, ordered values (e.g., 0x00, 0x01, 0x02, …) as discriminators.
• Anchor conventions: Anchor programs typically use the first 8 bytes of the SHA-256 hash of a struct name as the discriminator, ensuring uniqueness.
• Nested Anchor logs: Some Anchor-based programs use a two-level discriminator — the first 8 bytes identify a CPI log instruction, and the next 8 bytes identify a specific data structure inside the log (for a total of 16 bytes).
• Completely custom formats: Some programs define arbitrarily structured discriminators that don’t follow any public pattern.

If you can reliably identify a particular instruction from observed transactions, you may be able to deduce its discriminator by finding repeated byte sequences at the start of the instruction data.

Params

The params field in the signature defines the expected values within the instruction data — in the exact order they appear. Each param can include a name, a type, and in the case of composite types, a list of fields or variants. These parameters are ordered and interpreted sequentially during decoding.

Supported types include:

• Primitives: Uint, Int, and Bool
• Complex types:
  • FixedArray: A fixed-length array of another type (e.g., Public keys, for example, are 32 bytes (or u8) [u8; 32].)
  • Array: A dynamic-length array. Data are prefixed with a length indicator to determine how many elements to decode.
  • Struct: A composite of keys - value types (like a dictionary)
  • Enum: A type representing one of several variants. Variant may optionally carry its own nested value.
  • Option: A nullable value that either holds a nested type or is empty.

All complex types can be nested arbitrarily — for example, an array of structs, an option of an enum, or a struct containing other structs.

Accounts Names

In Solana, each instruction includes a list of accounts it interacts with, passed as a separate data structure from the instruction data itself. The accounts_names field allows you to assign meaningful names to these account indices, making decoded output easier to read and analyze.

While the decoder doesn’t interpret account data contents, having named accounts helps clarify the role each address plays in the instruction (e.g., “user”, “token_account”, “vault”, etc.).

SVM Decode Logs

Decodes raw Solana program log entries into structured columns using a log signature definition. Logs signatures works the same way as instructions signatures.

Python

from tiders_core.svm_decode import LogSignature

cc.Step(
    kind=cc.StepKind.SVM_DECODE_LOGS,
    config=cc.SvmDecodeLogsConfig(
        log_signature=LogSignature(...),
        output_table="decoded_logs",   # optional — when True rows that fails are nulls values instead of raising an error, default: False
        input_table="logs",            # optional — name of the input table to decode, default: "logs"
        allow_decode_fail=False,       # optional — when True rows that fails are nulls values instead of raising an error, default: False
        hstack=True,                   # optional — when True decoded columns are horizontally stacked with the input columns, default: True
    ),
)

yaml

- kind: svm_decode_logs
  config:
    log_signature: {...}               # required
    output_table: decoded_logs         # optional, optional, default: "decoded_logs"
    input_table: logs                  # optional, optional, default: "logs"
    allow_decode_fail: false           # optional, optional, default: false
    hstack: true                       # optional, optional, default: true

Cast

Casts specific columns in a table to new Arrow data types.

Python

import pyarrow as pa

cc.Step(
    kind=cc.StepKind.CAST,
    config=cc.CastConfig(
        table_name="transfers",  # required — the name of the table whose columns should be cast.
        mappings={               # required — a mapping of column name to target pyarrow.DataType
            "value": pa.decimal128(38, 0),
            "block_number": pa.int64(),
        },
        allow_cast_fail=False,   # optional — When True, values that cannot be cast are set to null instead of raising an error, default: False
    ),
)

yaml

- kind: cast
  config:
    table_name: transfers          # required
    mappings:                      # required — column name to Arrow type
      value: "decimal128(38,0)"
      block_number: int64
    allow_cast_fail: false         # optional, default: false

Supported type strings: int8–int64, uint8–uint64, float16–float64, string, binary, bool, date32, date64, null, decimal128(p,s), decimal256(p,s).

Cast By Type

Casts all columns of a given Arrow type to a different type, across every table.

Python

import pyarrow as pa

cc.Step(
    kind=cc.StepKind.CAST_BY_TYPE,
    config=cc.CastByTypeConfig(
        from_type=pa.decimal256(76, 0),
        to_type=pa.decimal128(38, 0),
        allow_cast_fail=False,   # default: false
    ),
)

yaml

- kind: cast_by_type
  config:
    from_type: "decimal256(76,0)"   # required — the source pyarrow.DataType to match.
    to_type: "decimal128(38,0)"     # required — the target pyarrow.DataType to cast
    allow_cast_fail: false          # optional, default: false

Supported type strings: int8–int64, uint8–uint64, float16–float64, string, binary, bool, date32, date64, null, decimal128(p,s), decimal256(p,s).

Hex Encode

Converts all binary columns to hex-encoded strings for readability.

Python

cc.Step(
    kind=cc.StepKind.HEX_ENCODE,
    config=cc.HexEncodeConfig(
        tables=["transfers"],   # default: None — apply to all tables
        prefixed=True,          # default: true — add 0x prefix
    ),
)

yaml

- kind: hex_encode
  config:
    tables: [transfers]   # optional — list of table names to process. When ``None``, all tables in the data dictionary are processed, default: None
    prefixed: true        # optional — When True, output strings are "0x"-prefixed, default: True

Base58 Encode

Converts all binary columns to Base58-encoded strings, used for Solana public keys and signatures.

Python

cc.Step(
    kind=cc.StepKind.BASE58_ENCODE,
    config=cc.Base58EncodeConfig(
        tables=["instructions"],   # optional — list of table names to process. When ``None``, all tables in the data dictionary are processed, default: None
    ),
)

yaml

- kind: base58_encode
  config:
    tables: [instructions]   # optional — apply to specific tables only; default: all tables

Join Block Data

Joins block fields into other tables using a left outer join. Column collisions are prefixed with <block_table_name>_.

Python

cc.Step(
    kind=cc.StepKind.JOIN_BLOCK_DATA,
    config=cc.JoinBlockDataConfig(
        tables=["logs"],                # optional — list of tables to join into; default: all tables except the block table
        block_table_name="blocks",      # optional — name of the blocks table, default: "blocks"
        join_left_on=["block_number"],  # optional — join key on the left (child) table, default: ["block_number"]
        join_blocks_on=["number"],      # optional — join key on the blocks table, default: ["number"]
    ),
)

yaml

- kind: join_block_data
  config:
    tables: [logs]               # optional — tables to join into; default: all tables except the block table
    block_table_name: blocks     # optional, default: "blocks"
    join_left_on: [block_number] # optional, default: ["block_number"]
    join_blocks_on: [number]     # optional, default: ["number"]

Join EVM Transaction Data

Joins EVM transaction fields into other tables using a left outer join. Column collisions are prefixed with <tx_table_name>_. Python

cc.Step(
    kind=cc.StepKind.JOIN_EVM_TRANSACTION_DATA,
    config=cc.JoinEvmTransactionDataConfig(
        tables=["logs"],                   # optional — list of tables to join into; default: all tables except the transactions table
        tx_table_name="transactions",      # optional — name of the transactions table, default: "transactions"
        join_left_on=["block_number", "transaction_index"],        # optional — join key on the left table, default: ["block_number", "transaction_index"]
        join_transactions_on=["block_number", "transaction_index"],# optional — join key on the transactions table, default: ["block_number", "transaction_index"]
    ),
)

yaml

- kind: join_evm_transaction_data
  config:
    tables: [logs]                                           # optional — tables to join into; default: all except the transactions table
    tx_table_name: transactions                              # optional, default: "transactions"
    join_left_on: [block_number, transaction_index]          # optional, default: ["block_number", "transaction_index"]
    join_transactions_on: [block_number, transaction_index]  # optional, default: ["block_number", "transaction_index"]

Join SVM Transaction Data

Joins SVM transaction fields into other tables using a left outer join. Column collisions are prefixed with <tx_table_name>_.

Python

cc.Step(
    kind=cc.StepKind.JOIN_SVM_TRANSACTION_DATA,
    config=cc.JoinSvmTransactionDataConfig(
        tables=["instructions"],           # optional — list of tables to join into; default: all tables except the transactions table
        tx_table_name="transactions",      # optional — name of the transactions table, default: "transactions"
        join_left_on=["block_slot", "transaction_index"],        # optional — join key on the left table, default: ["block_slot", "transaction_index"]
        join_transactions_on=["block_slot", "transaction_index"],# optional — join key on the transactions table, default: ["block_slot", "transaction_index"]
    ),
)

yaml

- kind: join_svm_transaction_data
  config:
    tables: [instructions]                                  # optional — tables to join into; default: all except the transactions table
    tx_table_name: transactions                             # optional, default: "transactions"
    join_left_on: [block_slot, transaction_index]           # optional, default: ["block_slot", "transaction_index"]
    join_transactions_on: [block_slot, transaction_index]   # optional, default: ["block_slot", "transaction_index"]

Set Chain ID

Adds (or replaces) a constant chain_id column on every table.

Python

cc.Step(
    kind=cc.StepKind.SET_CHAIN_ID,
    config=cc.SetChainIdConfig(
        chain_id=1,   # The chain identifier to set (e.g. 1 for Ethereum mainnet).
    ),
)

yaml

- kind: set_chain_id
  config:
    chain_id: 1   # required

Custom Steps with Polars (Python only)

The Polars step lets you plug any Python function directly into the pipeline. When the step runs, tiders converts every in-memory PyArrow table into a polars.DataFrame, calls your function with all of them at once, and then converts the results back to PyArrow tables so the rest of the pipeline can continue.

Your function receives two arguments:

• data — a dict[str, pl.DataFrame] mapping table names (e.g. "transfers", "blocks") to their current Polars DataFrames.
• context — any value (a dict, scalar, list, etc.) passed via context in the config , it exports this varible from the pipeline to the callable python function. Useful for parameterizing the function without hard-coding values. It is None when not set.

The function must return a dict[str, pl.DataFrame] mapping table name to tables. You can return the same tables with modifications, drop tables, or add new ones — whatever is in the returned dict becomes the new state of the pipeline’s data for subsequent steps.

Requires pip install tiders[polars].

import polars as pl
import tiders as cc

def my_transform(data: dict[str, pl.DataFrame], context) -> dict[str, pl.DataFrame]:
    threshold = context["threshold"] if context else 0
    transfers = data["transfers"]
    # filter low-value transfers and add a normalized column
    data["transfers"] = (
        transfers
        .filter(pl.col("value") > threshold)
        .with_columns((pl.col("value") / 1e18).alias("value_eth"))
    )
    data.pop("raw_logs")
    return data # returned data dict will include the original inputs tables, except "raw_logs" (popped) and the add (or updated) table "transfer"

cc.Step(
    kind=cc.StepKind.POLARS,
    config=cc.PolarsStepConfig(
        runner=my_transform,
        context={"threshold": 1_000_000},   # optional — variable passed as context to the function, in this case a dict.
    ),
)

Custom Steps with Pandas (Python only)

The Pandas step lets you plug any Python function directly into the pipeline. When the step runs, tiders converts every in-memory PyArrow table into a pandas.DataFrame, calls your function with all of them at once, and then converts the results back to PyArrow tables so the rest of the pipeline can continue.

Your function receives two arguments:

• data — a dict[str, pd.DataFrame] mapping table names (e.g. "transfers", "blocks") to their current Pandas DataFrames.
• context — any value (a dict, scalar, list, etc.) passed via context in the config, it exports this variable from the pipeline to the callable python function. Useful for parameterizing the function without hard-coding values. It is None when not set.

The function must return a dict[str, pd.DataFrame] mapping table name to tables. You can return the same tables with modifications, drop tables, or add new ones — whatever is in the returned dict becomes the new state of the pipeline’s data for subsequent steps.

Requires pip install tiders[pandas].

import pandas as pd
import tiders as cc

def my_transform(data: dict[str, pd.DataFrame], context) -> dict[str, pd.DataFrame]:
    threshold = context["threshold"] if context else 0
    transfers = data["transfers"]
    # filter low-value transfers and add a normalized column
    transfers = transfers[transfers["value"] > threshold].copy()
    transfers["value_eth"] = transfers["value"] / 1e18
    data["transfers"] = transfers
    data.pop("raw_logs")
    return data  # returned data dict will include the original inputs tables, except "raw_logs" (popped) and the added (or updated) table "transfers"

cc.Step(
    kind=cc.StepKind.PANDAS,
    config=cc.PandasStepConfig(
        runner=my_transform,
        context={"threshold": 1_000_000},   # optional — variable passed as context to the function, in this case a dict.
    ),
)

Custom Steps with DataFusion (Python only)

The DataFusion step uses Apache DataFusion as the execution engine, which lets you write SQL queries against the pipeline tables within your custom function.

When the step runs, tiders creates a fresh datafusion.SessionContext, registers every in-memory PyArrow table as a DataFusion DataFrame inside it, and calls your function. Your function can run SQL queries through session_ctx.sql(...), transform DataFrames using DataFusion’s DataFrame API, or combine both. The returned DataFrames are converted back to PyArrow tables for the next step.

Your function receives three arguments:

• session_ctx — the datafusion.SessionContext with all current tables already registered by name, so you can query them directly with SQL.
• data — a dict[str, datafusion.DataFrame] mapping table names to their DataFusion DataFrames. It’s a convenience shortcut for the DataFrame API — equivalent to calling session_ctx.table(name) for each table and useful to construct the returning data.
• context — any value (a dict, scalar, list, etc.) passed via context in the config , it exports this varible from the pipeline to the callable python function. Useful for parameterizing the function without hard-coding values. It is None when not set.

The function must return a dict[str, datafusion.DataFrame] mapping table name to tables. User is responsible to manage the returning dict. You can return the same tables with modifications, drop tables, or add new ones — whatever is in the returned dict becomes the new state of the pipeline’s data for subsequent steps.

Requires pip install tiders[datafusion].

import datafusion
import tiders as cc

def my_sql_transform(session_ctx, data, context):
    min_block = context["min_block"] if context else 0
    data["transfers"] = session_ctx.sql(f"""
        SELECT t.*, b.timestamp
        FROM transfers t
        JOIN blocks b ON b.number = t.block_number
        WHERE t.block_number >= {min_block}
    """)
    data.pop("raw_logs")
    return data # returned data dict will include the original inputs tables, except "raw_logs" (popped) and the added (or updated) table "transfer"

cc.Step(
    kind=cc.StepKind.DATAFUSION,
    config=cc.DataFusionStepConfig(
        runner=my_sql_transform,
        context={"min_block": 18_500_000},   # optional — variable passed as context to the function, in this case a dict.
    ),
)

Python File (yaml only)

The python_file kind is the yaml equivalent of the Polars and DataFusion custom steps. It imports a .py file as a module, looks up the named function, and runs it as either a Polars or DataFusion step depending on step_type.

The file path is resolved relative to the yaml config file’s directory. The function must be defined at the module level (not nested inside another function or class).

Polars

Set step_type: polars. The function must use the (data, context) signature described in Custom Steps with Polars.

./steps/my_step.py:

import polars as pl

def my_transform(data: dict[str, pl.DataFrame], context) -> dict[str, pl.DataFrame]:
    threshold = context["threshold"] if context else 0
    transfers = data["transfers"]
    transfers = (
        transfers
        .filter(pl.col("value") > threshold)
        .with_columns((pl.col("value") / 1e18).alias("value_eth"))
    )
    data["transfers"] = transfers
    return data

- kind: python_file
  config:
    file: ./steps/my_step.py    # required — path to the .py file, relative to the yaml config
    function: my_transform      # required — module-level function name to call
    step_type: polars           # required — "polars", "pandas", or "datafusion" (default: datafusion)
    context:                    # optional — any yaml value, passed as context to the function
      threshold: 1000000

Pandas

Set step_type: pandas. The function must use the (data, context) signature described in Custom Steps with Pandas.

./steps/my_step.py:

import pandas as pd

def my_transform(data: dict[str, pd.DataFrame], context) -> dict[str, pd.DataFrame]:
    threshold = context["threshold"] if context else 0
    transfers = data["transfers"]
    transfers = transfers[transfers["value"] > threshold].copy()
    transfers["value_eth"] = transfers["value"] / 1e18
    data["transfers"] = transfers
    return data

- kind: python_file
  config:
    file: ./steps/my_step.py    # required — path to the .py file, relative to the yaml config
    function: my_transform      # required — module-level function name to call
    step_type: pandas           # required — "polars", "pandas", or "datafusion" (default: datafusion)
    context:                    # optional — any yaml value, passed as context to the function
      threshold: 1000000

DataFusion

Set step_type: datafusion (or omit it, since datafusion is the default). The function must use the (session_ctx, data, context) signature described in Custom Steps with DataFusion.

./steps/my_step.py:

def my_sql_transform(session_ctx, data, context):
    min_block = context["min_block"] if context else 0
    data["transfers"] = session_ctx.sql(f"""
        SELECT t.*, b.timestamp
        FROM transfers t
        JOIN blocks b ON b.number = t.block_number
        WHERE t.block_number >= {min_block}
    """)
    return data

- kind: python_file
  config:
    file: ./steps/my_step.py      # required — path to the .py file, relative to the yaml config
    function: my_sql_transform    # required — module-level function name to call
    step_type: datafusion         # optional — default when step_type is omitted
    context:                      # optional — any yaml value, passed as context to the function
      min_block: 18500000

SQL (yaml only)

The sql kind is a yaml-only convenience step that lets you write DataFusion SQL directly in your config file without needing a separate Python file. Under the hood it builds a DataFusion runner and executes each query sequentially against the current in-memory tables.

All in-memory tables are available by name in every query. Queries run in order, and the output of one query is available to the next. Each query’s result is stored back into the pipeline data:

• CREATE TABLE name AS SELECT ... — stores the result under name, making it available for subsequent steps and for the writer. CREATE OR REPLACE TABLE and CREATE TABLE IF NOT EXISTS are also supported.
• CREATE VIEW name AS SELECT ... — same as above; the view is registered in the session and stored under name.
• Plain SELECT ... — stores the result under the key sql_result, overwriting the previous value if multiple plain selects are used.

Existing tables not referenced in any query are preserved unchanged.

yaml

- kind: sql
  config:
    queries:                        # required — one or more SQL strings, run in order
      - >
        CREATE TABLE transfers AS
        SELECT *
        FROM logs
        WHERE topic0 = '0xddf252ad...'
      - >
        CREATE OR REPLACE TABLE enriched AS
        SELECT t.*, b.timestamp
        FROM transfers t
        JOIN blocks b ON b.number = t.block_number

Writers

Writers define where the processed data is stored after each pipeline batch. Each writer adapts the Arrow RecordBatch output to a specific storage format.

Available Writers

Each table in the pipeline data is written as a separate table or directory named after its key (e.g. "transfers" → transfers table or transfers/ directory).

All writers support automatic table creation. tiders infers the output schema from the Arrow data and creates tables accordingly. No manual schema definition is needed.

A pipeline can write to more than one backend simultaneously by passing a list of writers. All writers receive the same processed data in parallel on each batch.

DuckDB

Inserts Arrow tables into a DuckDB database. Tables are auto-created on the first push using the Arrow schema. Decimal256 columns are automatically downcast to Decimal128(38, scale) since DuckDB does not support 256-bit decimals — use a cast_by_type step beforehand if you need control over overflow behavior.

Requires: pip install "tiders[duckdb]"

Python

import tiders as cc

# Option 1: plain path
writer = cc.Writer(
    kind=cc.WriterKind.DUCKDB,
    config=cc.DuckdbWriterConfig(
        path="./data/output.duckdb",   # path to create or connect to a DuckDB database
    ),
)

# Option 2: pre-built connection
import duckdb

duckdb_client=duckdb.connect("./data/output.duckdb")

writer = cc.Writer(
    kind=cc.WriterKind.DUCKDB,
    config=cc.DuckdbWriterConfig(
        connection=duckdb_client,   # optional — pre-built DuckDB connection
    ),
)

yaml

writer:
  kind: duckdb
  config:
    path: data/output.duckdb   # required

ClickHouse

Inserts Arrow tables into ClickHouse using the clickhouse-connect async client. Tables are auto-created on the first insert using the inferred Arrow schema. All tables except the anchor_table are inserted in parallel.

Requires: pip install "tiders[clickhouse]"

Python

import tiders as cc
from tiders.config import ClickHouseSkipIndex

# Option 1: plain connection parameters
writer = cc.Writer(
    kind=cc.WriterKind.CLICKHOUSE,
    config=cc.ClickHouseWriterConfig(
        host="localhost",               # optional, default: "localhost"
        port=8123,                      # optional, default: 8123
        username="default",             # optional, default: "default"
        password="",                    # optional, default: ""
        database="default",             # optional, default: "default"
        secure=False,                   # optional, default: False
        engine="MergeTree()",           # optional, default: "MergeTree()"
        order_by={"transfers": ["block_number", "log_index"]},   # optional, per-table ordering key columns.
        codec={"transfers": {"data": "ZSTD(3)"}},   # optional, per-table, per-column compression codecs.
        skip_index={"transfers": [ClickHouseSkipIndex(name="idx_value", val="value", type_="minmax", granularity=1)]},   # optional, per-table list of data-skipping indexes added after table creation.
        create_tables=True,             # optional, default: True.
        anchor_table="transfers",       # optional, default: None.
    ),
)

# Option 2: pre-built async client
import clickhouse_connect

clickhouse_client = await clickhouse_connect.get_async_client(
    host="localhost", port=8123, username="default", password="", database="default",
)

writer = cc.Writer(
    kind=cc.WriterKind.CLICKHOUSE,
    config=cc.ClickHouseWriterConfig(
        client=clickhouse_client,       # optional — pre-built async ClickHouse client
    ),
)

yaml

writer:
  kind: clickhouse
  config:                         # include params to create the ClickHouseClient.
    host: localhost               # required
    port: 8123                    # optional, default: 8123
    username: default             # optional, default: default
    password: ${CH_PASSWORD}      # optional, default: ""
    database: default             # optional, default: default
    secure: false                 # optional, default: false
    engine: MergeTree()           # optional, default: MergeTree()
    order_by:                     # optional — per-table list of ORDER BY columns
      transfers: [block_number, log_index]
    codec:                        # optional — per-table, per-column compression codec
      transfers:
        data: ZSTD(3)
    create_tables: true           # optional, default: true
    anchor_table: transfers       # optional — written last after all other tables

skip_index (Python only): ClickHouseSkipIndex takes name, val (index expression), type_ (e.g. "minmax", "bloom_filter"), and granularity. Indexes are added after table creation via ALTER TABLE ... ADD INDEX.

Iceberg

Writes Arrow tables into an Apache Iceberg catalog. Each table is created in the specified namespace if it does not already exist.

Requires: pip install "tiders[iceberg]"

Python

import tiders as cc

# Option 1: plain catalog parameters
writer = cc.Writer(
    kind=cc.WriterKind.ICEBERG,
    config=cc.IcebergWriterConfig(
        namespace="my_namespace",                          # required — Iceberg namespace (database) to write tables into
        catalog_uri="sqlite:///catalog.db",                # required — URI for the Iceberg catalog
        warehouse="s3://my-bucket/iceberg/",               # required — warehouse root URI for the catalog
        catalog_type="sql",                                # optional, default: "sql"
        write_location="s3://my-bucket/iceberg/",          # optional — storage URI for data files, default: warehouse
    ),
)

# Option 2: pre-built pyiceberg catalog
from pyiceberg.catalog import load_catalog

catalog = load_catalog(
    "my_catalog",
    type="sql",
    uri="sqlite:///catalog.db",
    warehouse="s3://my-bucket/iceberg/",
)

writer = cc.Writer(
    kind=cc.WriterKind.ICEBERG,
    config=cc.IcebergWriterConfig(
        namespace="my_namespace",                          # required
        catalog=catalog,                                   # optional — pre-built pyiceberg Catalog instance
        write_location="s3://my-bucket/iceberg/",          # optional — default: warehouse
    ),
)

yaml

writer:
  kind: iceberg
  config:
    namespace: my_namespace                          # required
    catalog_uri: sqlite:///catalog.db                # required
    warehouse: s3://my-bucket/iceberg/               # required
    catalog_type: sql                                # optional, default: sql
    write_location: s3://my-bucket/iceberg/          # optional, default: warehouse

Delta Lake

Appends Arrow tables to Delta tables using deltalake.write_deltalake with schema merging enabled. Each table is stored at /<table_name>/. All tables except the anchor_table are written in parallel.

Requires: pip install "tiders[delta_lake]"

Python

import tiders as cc

writer = cc.Writer(
    kind=cc.WriterKind.DELTA_LAKE,
    config=cc.DeltaLakeWriterConfig(
        data_uri="s3://my-bucket/delta/",               # required — base URI; each table is written to /<table_name>/
        partition_by={"transfers": ["block_number"]},   # optional — per-table list of partition columns
        storage_options={"AWS_REGION": "us-east-1"},    # optional — cloud storage credentials passed to deltalake
        anchor_table="transfers",                       # optional — written last after all other tables
    ),
)

yaml

writer:
  kind: delta_lake
  config:
    data_uri: s3://my-bucket/delta/    # required
    partition_by:                       # optional — per-table list of partition columns
      transfers: [block_number]
    storage_options:                    # optional — cloud storage credentials
      AWS_REGION: us-east-1
      AWS_ACCESS_KEY_ID: ${AWS_KEY}
    anchor_table: transfers             # optional — written last after all other tables

PyArrow Dataset (Parquet)

Writes Arrow tables as Parquet files using pyarrow.dataset.write_dataset. Each table is stored under <base_dir>/<table_name>/. A monotonic counter is appended to the file name to avoid collisions across successive pushes. All tables except the anchor_table are written in parallel.

Python

import tiders as cc

writer = cc.Writer(
    kind=cc.WriterKind.PYARROW_DATASET,
    config=cc.PyArrowDatasetWriterConfig(
        base_dir="./data/output",                       # required — root directory; each table is written to <base_dir>/<table_name>/
        partitioning={"transfers": ["block_number"]},   # optional — per-table list of partition columns or pyarrow.dataset.Partitioning
        partitioning_flavor={"transfers": "hive"},      # optional — per-table partitioning flavor
        basename_template="part-{i}.parquet",           # optional — output file name template, default: "part-{i}.parquet"
        max_rows_per_file=1_000_000,                    # optional — max rows per output file, default: 0 (unlimited)
        min_rows_per_group=0,                           # optional — min rows per Parquet row group, default: 0
        max_rows_per_group=1024 * 1024,                 # optional — max rows per Parquet row group, default: 1048576
        max_partitions=1024,                            # optional — max number of partitions, default: 1024
        max_open_files=1024,                            # optional — max files open simultaneously, default: 1024
        use_threads=True,                               # optional — use threads for writing, default: True
        create_dir=True,                                # optional — create output directory if missing, default: True
        anchor_table="transfers",                       # optional — written last after all other tables
    ),
)

yaml

writer:
  kind: pyarrow_dataset
  config:
    base_dir: data/output                # required
    partitioning:                        # optional — per-table list of partition columns
      transfers: [block_number]
    partitioning_flavor:                 # optional — per-table flavor (e.g. "hive")
      transfers: hive
    basename_template: part-{i}.parquet  # optional — output file name template
    max_rows_per_file: 1000000           # optional, default: 0 (unlimited)
    min_rows_per_group: 0                # optional, default: 0
    max_rows_per_group: 1048576          # optional, default: 1048576
    max_partitions: 1024                 # optional, default: 1024
    max_open_files: 1024                 # optional, default: 1024
    use_threads: true                    # optional, default: true
    create_dir: true                     # optional, default: true
    anchor_table: transfers              # optional — written last after all other tables

CSV

Writes Arrow tables as CSV files using pyarrow.csv.write_csv. Each table is written to <base_dir>/<table_name>.csv. On successive pushes the file is appended to. All tables except the anchor_table are written in parallel.

Python

import tiders as cc

writer = cc.Writer(
    kind=cc.WriterKind.CSV,
    config=cc.CsvWriterConfig(
        base_dir="./data/output",       # required — root directory; each table is written to <base_dir>/<table_name>.csv
        delimiter=",",                  # optional — field delimiter character, default: ","
        include_header=True,            # optional — write a header row, default: True
        create_dir=True,                # optional — create output directory if missing, default: True
        anchor_table="transfers",       # optional — written last after all other tables
    ),
)

yaml

writer:
  kind: csv
  config:
    base_dir: data/output        # required
    delimiter: ","               # optional, default: ","
    include_header: true         # optional, default: true
    create_dir: true             # optional, default: true
    anchor_table: transfers      # optional — written last after all other tables

PostgreSQL

Inserts Arrow tables into PostgreSQL using the COPY protocol via psycopg v3. Tables are auto-created on the first push using CREATE TABLE IF NOT EXISTS derived from the Arrow schema. All tables except the anchor_table are inserted in parallel.

Requires: pip install "tiders[postgresql]"

Unsupported raw blockchain fields

The PostgreSQL writer does not support List, Struct, or Map Arrow columns. Writing raw EVM or SVM data directly will fail unless you use a step to flatten or drop the affected columns first.

EVM fields that require preprocessing:

SVM fields that require preprocessing:

Python

import tiders as cc

# Option 1: plain connection parameters (recommended)
writer = cc.Writer(
    kind=cc.WriterKind.POSTGRESQL,
    config=cc.PostgresqlWriterConfig(
        host="localhost",              # optional, default: "localhost"
        port=5432,                     # optional, default: 5432
        user="postgres",               # optional, default: "postgres"
        password="secret",             # optional, default: "postgres"
        dbname="mydb",                 # optional, default: "postgres"
        schema="public",               # optional — PostgreSQL schema (namespace), default: "public"
        create_tables=True,            # optional — auto-create tables on first push, default: True
        anchor_table="transfers",      # optional — written last after all other tables, default: None
    ),
)

# Option 2: pre-built async connection
import psycopg
import asyncio

connection = asyncio.get_event_loop().run_until_complete(
    psycopg.AsyncConnection.connect(
        "host=localhost port=5432 dbname=mydb user=postgres password=secret",
        autocommit=False,
    )
)

writer = cc.Writer(
    kind=cc.WriterKind.POSTGRESQL,
    config=cc.PostgresqlWriterConfig(
        connection=connection,         # optional — pre-built psycopg.AsyncConnection
        schema="public",               # optional, default: "public"
        create_tables=True,            # optional, default: True
        anchor_table="transfers",      # optional, default: None
    ),
)

yaml

writer:
  kind: postgresql
  config:
    host: localhost               # required
    dbname: postgres              # optional, default: postgres
    port: 5432                    # optional, default: 5432
    user: postgres                # optional, default: postgres
    password: ${PG_PASSWORD}      # optional, default: postgres
    schema: public                # optional, default: public
    create_tables: true           # optional, default: true
    anchor_table: transfers       # optional — written last after all other tables

Examples

Complete working examples that demonstrate different tiders features. Each example is designed to highlight a different aspect of the framework — start with the one closest to your use case.

What each example teaches

• rETH Transfer — The simplest starting point. Uses a YAML config with no Python code, showing how to index a single event from a single contract. Also demonstrates the SQL step for joining decoded data with block timestamps.

• Jupiter Swaps — Use Tiders’ Python SDK and switches to Solana (SVM). Shows how to decode instruction data, use a custom Polars step for joining tables, and run post-pipeline SQL in DuckDB to enrich data with external metadata.

• Uniswap V3 — Demonstrates the factory + children two-stage indexing pattern, the most common multi-pipeline pattern in DeFi. Shows how to chain two pipelines where the first discovers contracts and the second indexes their events, how to dynamically generate decode steps from an ABI, and how to use table aliases to give descriptive names to raw ingested tables.

All examples are available in the examples/ directory of the tiders repository.

rETH Transfers (No-Code)

This example indexes Transfer events from the Rocket Pool rETH token contract using a YAML config — no Python required.

Source: examples/reth_transfer_nocode/

Run

cd examples/reth_transfer_nocode
cp .env.example .env
# Fill PROVIDER_URL and HYPERSYNC_BEARER_TOKEN in .env
tiders start

What it does

1. Queries logs matching the Transfer event from the rETH contract via HyperSync
2. Decodes the raw log data into typed from, to, and amount columns
3. Casts decimal256 to decimal128, unnecessary for PyArrow dataset (Parquet), but include if writer doesn’t support decimal256 values
4. Hex-encodes binary fields for readability
5. Joins decoded transfer data with block timestamps via SQL step, so the transfer table include a timestamp
6. Writes to a PyArrow dataset

Full config

project:
  name: rETH_transfer
  description: Index rETH Transfer events from the Rocket Pool rETH token contract.
  repository: https://github.com/yulesa/tiders/example/reth_transfer_nocode

provider:
  kind: hypersync
  url: ${PROVIDER_URL}
  bearer_token: ${HYPERSYNC_BEARER_TOKEN}

contracts:
  - name: RocketTokenRETH
    address: 0xae78736Cd615f374D3085123A210448E74Fc6393
    abi: ./RocketTokenRETH.abi.json

query:
  kind: evm
  from_block: 13325304
  to_block: 13325404
  logs:
    - address: RocketTokenRETH.address
      topic0: "Transfer(address,address,uint256)"
      include_blocks: true
  fields:
    log: [address, topic0, topic1, topic2, topic3, data, block_number, transaction_hash, log_index]
    block: [number, timestamp]

steps:
  - kind: evm_decode_events
    config:
      event_signature: "Transfer(address indexed from, address indexed to, uint256 amount)"
      output_table: transfers
      allow_decode_fail: true

  - kind: cast_by_type
    name: i256_to_i128
    config:
      from_type: "decimal256(76,0)"
      to_type: "decimal128(38,0)"
      allow_cast_fail: true

  - kind: hex_encode

  - kind: sql
    config:
      queries:
        - >
          CREATE OR REPLACE TABLE transfers AS
          SELECT transfers.*, blocks.timestamp AS block_timestamp
          FROM transfers
          INNER JOIN blocks ON blocks.number = transfers.block_number

writer:
  kind: pyarrow_dataset
  config:
    base_dir: data/pyarrow

Key concepts

Contract references — the contracts: section loads RocketTokenRETH.abi.json and makes the contract available by name. topic0: "Transfer(address,address,uint256)" is a human-readable event signature; the CLI converts it to a Keccak-256 hash automatically.

Decode step — evm_decode_events reads the raw topic1, topic2, and data columns and produces typed from, to, and amount columns in a new transfers table.

Cast step — EVM uint256 values are decoded as decimal256(76,0). The cast_by_type step downcasts them to decimal128(38,0). This is step is dispensable in PyArrow datasets (Parquet), but necessary in databases that don’t support decimal256(76,0).

SQL step — the sql step runs DataFusion SQL against the in-memory tables. Both transfers and blocks are available because include_blocks: true was set on the log request.

Environment variables — credentials stay in .env and are referenced as ${PROVIDER_URL} in the YAML. The CLI loads .env automatically from the config directory.

Jupiter Swaps (SVM)

This example builds a Solana indexer that fetches Jupiter DEX swap instructions, decodes them into typed fields, joins with block and transaction data using Polars, and writes enriched swap analytics to DuckDB.

Source: examples/jup_swap/jup_swap.py

Run

uv run jup_swap.py --from_block 330447751 --to_block 330448751

Pipeline Overview

1. Queries Solana instructions filtered by Jupiter program ID (JUP6LkbZbjS1jKKwapdHNy74zcZ3tLUZoi5QNyVTaV4) and swap event discriminator (0xe445a52e51cb9a1d40c6cde8260871e2), along with related blocks and transactions
2. Decodes instruction data into typed swap fields (AMM, input/output mints, amounts) using InstructionSignature
3. Joins block and transaction data into the decoded instructions table via a custom Polars step
4. Base58-encodes binary fields (public keys, hashes) into human-readable strings
5. Writes to DuckDB
6. Enriches with token metadata and AMM names via DuckDB post-processing SQL

Provider

Connects to the SQD portal network for Solana mainnet data:

provider = ProviderConfig(
    kind=ProviderKind.SQD,
    url="https://portal.sqd.dev/datasets/solana-mainnet",
)

Query

The query fetches instructions, blocks, and transactions for a given block range. Field selection controls which columns are returned per table, minimizing bandwidth. The InstructionRequest filters rows to only Jupiter swap events by program_id=["JUP…4"] and discriminator=["0xe4…e2"]. Setting include_transactions=True pulls in the transaction table for matching instructions, and include_all_blocks=True returns all blocks in the range regardless of matches.

query = IngestQuery(
    kind=QueryKind.SVM,
    params=Query(
        from_block=from_block,
        to_block=actual_to_block,
        include_all_blocks=True,
        fields=Fields(
            instruction=InstructionFields(
                block_slot=True,
                block_hash=True,
                transaction_index=True,
                instruction_address=True,
                program_id=True,
                a0=True, a1=True, a2=True, a3=True, a4=True,
                a5=True, a6=True, a7=True, a8=True, a9=True,
                data=True,
                error=True,
            ),
            block=BlockFields(
                slot=True,
                hash=True,
                timestamp=True,
            ),
            transaction=TransactionFields(
                block_slot=True,
                block_hash=True,
                transaction_index=True,
                signature=True,
            ),
        ),
        instructions=[
            InstructionRequest(
                program_id=["JUP6LkbZbjS1jKKwapdHNy74zcZ3tLUZoi5QNyVTaV4"],
                discriminator=["0xe445a52e51cb9a1d40c6cde8260871e2"],
                include_transactions=True,
            )
        ],
    ),
)

Instruction Signature

Defines the layout of the Jupiter Aggregator v6 SwapEvent instruction data. The discriminator identifies the instruction type, and params describes the binary layout: three 32-byte public keys (AMM, InputMint, OutputMint) and two u64 amounts. Signatures can be sourced from IDLs, SolScan, or contract source code.

For more information read the instructions signatures documentation.

from tiders_core.svm_decode import InstructionSignature, ParamInput, DynType, FixedArray

instruction_signature = InstructionSignature(
    discriminator="0xe445a52e51cb9a1d40c6cde8260871e2",
    params=[
        ParamInput(name="Amm", param_type=FixedArray(DynType.U8, 32)),
        ParamInput(name="InputMint", param_type=FixedArray(DynType.U8, 32)),
        ParamInput(name="InputAmount", param_type=DynType.U64),
        ParamInput(name="OutputMint", param_type=FixedArray(DynType.U8, 32)),
        ParamInput(name="OutputAmount", param_type=DynType.U64),
    ],
    accounts_names=[],
)

Transformation Steps

Steps are executed sequentially. Each step receives the transformed output of the previous one.

1. SVM_DECODE_INSTRUCTIONS — Decodes raw instruction data using the signature above. hstack=True appends decoded columns alongside the original fields. allow_decode_fail=True keeps rows that fail to decode. The output is written to a new table jup_swaps_decoded_instructions.
2. POLARS (custom step) — Runs a user-defined function that joins block timestamps and transaction signatures into the decoded instructions table using Polars DataFrames.
3. BASE58_ENCODE — Converts all binary columns (public keys, hashes) to base58 strings.

obs: Tiders has available JOIN_BLOCK_DATA and JOIN_SVM_TRANSACTION_DATA steps to make joins effortlessly. We are using custom polars funtions to set an example.

steps = [
    cc.Step(
        kind=cc.StepKind.SVM_DECODE_INSTRUCTIONS,
        config=cc.SvmDecodeInstructionsConfig(
            instruction_signature=instruction_signature,
            hstack=True,
            allow_decode_fail=True,
            output_table="jup_swaps_decoded_instructions",
        ),
    ),
    cc.Step(
        kind=cc.StepKind.POLARS,
        config=cc.PolarsStepConfig(runner=process_data),
    ),
    cc.Step(
        kind=cc.StepKind.BASE58_ENCODE,
        config=cc.Base58EncodeConfig(),
    ),
]

Custom Polars Step

The process_data function joins block and transaction tables into the decoded instructions table. The blocks join brings in timestamp; the transactions join brings in signature.

def process_data(
    data: dict[str, pl.DataFrame], ctx: Optional[Any]
) -> dict[str, pl.DataFrame]:
    table = data["jup_swaps_decoded_instructions"]
    table = table.join(data["blocks"], left_on="block_slot", right_on="slot")
    table = table.join(data["transactions"], on=["block_slot", "transaction_index"])
    return {"jup_swaps_decoded_instructions": table}

Writer

Writes the pipeline output to a local DuckDB database:

connection = duckdb.connect("data/solana_swaps.db")

writer = cc.Writer(
    kind=cc.WriterKind.DUCKDB,
    config=cc.DuckdbWriterConfig(
        connection=connection.cursor(),
    ),
)

Running the Pipeline

pipeline = cc.Pipeline(
    provider=provider,
    query=query,
    writer=writer,
    steps=steps,
)
await run_pipeline(pipeline_name="jup_swaps", pipeline=pipeline)

Post-Pipeline Analytics

After the pipeline writes jup_swaps_decoded_instructions to DuckDB, a SQL post-processing step enriches the data with token metadata and AMM names from CSV lookup tables, producing a jup_swaps table similar to a dex.trades analytics table:

CREATE OR REPLACE TABLE solana_amm AS
    SELECT * FROM read_csv('./solana_amm.csv');
CREATE OR REPLACE TABLE solana_tokens AS
    SELECT * FROM read_csv('./solana_tokens.csv');
CREATE OR REPLACE TABLE jup_swaps AS
    SELECT
        di.amm AS amm,
        sa.amm_name AS amm_name,
        case when di.inputmint > di.outputmint
            then it.token_symbol || '-' || ot.token_symbol
            else ot.token_symbol || '-' || it.token_symbol
            end as token_pair,
        it.token_symbol as input_token,
        di.inputmint AS input_token_address,
        di.inputamount AS input_amount_raw,
        it.token_decimals AS input_token_decimals,
        di.inputamount / 10^it.token_decimals AS input_amount,
        ot.token_symbol as output_token,
        di.outputmint AS output_token_address,
        di.outputamount AS output_amount_raw,
        ot.token_decimals AS output_token_decimals,
        di.outputamount / 10^ot.token_decimals AS output_amount,
        di.block_slot AS block_slot,
        di.transaction_index AS transaction_index,
        di.instruction_address AS instruction_address,
        di.timestamp AS block_timestamp
    FROM jup_swaps_decoded_instructions di
    LEFT JOIN solana_amm sa ON di.amm = sa.amm_address
    LEFT JOIN solana_tokens it ON di.inputmint = it.token_address
    LEFT JOIN solana_tokens ot ON di.outputmint = ot.token_address;

Output

SELECT * FROM jup_swaps_decoded_instructions LIMIT 3;
SELECT * FROM jup_swaps LIMIT 3;

Uniswap V3 (Factory + Children Pattern)

This example demonstrates the factory + children indexing pattern — one of the most common patterns in crypto data engineering. A Factory contract deploys child contracts (pools) on-chain. To index them you first index the factory to discover the children and then index their events. This pattern applies to Uniswap (V2/V3), Aave, Compound, Curve, and many other DeFi protocols.

Source: examples/uniswap_v3/

Run

cd examples/uniswap_v3
cp .env.example .env
uv run uniswap_v3.py --provider <hypersync|sqd|rpc> --from_block 12369621 --to_block 12370621
#  [--rpc_url URL]    \  # only needed with --provider rpc
#  [--database BACKEND]  # default: pyarrow. Options: pyarrow, duckdb, delta_lake, clickhouse, iceberg

Pipeline Overview

This example runs two pipelines sequentially:

Stage 1 — Discover pools from the Factory:

1. Queries PoolCreated logs from the Uniswap V3 Factory contract
2. Decodes raw log data into typed columns (token0, token1, fee, tickSpacing, pool)

Stage 2 — Index events from discovered pools:

1. Reads pool addresses from the Stage 1 output (plain Python, not a Tiders feature)
2. Queries ALL logs emitted by those pool addresses (no topic filter — fetches every event type)
3. Decodes each known pool event (Swap, Mint, Burn, Flash, etc.) into its own output table
4. Writes all decoded event tables to the chosen backend

ABI Setup

evm_abi_events() parses an ABI JSON file and returns event descriptors. We build a dict keyed by event name for easy access to events params.

The same pattern is used for pool events. Both ABI JSON files are included in the example directory.

factory_events = {
    ev.name: {
        "topic0": ev.topic0,
        "signature": ev.signature,
        "name_snake_case": ev.name_snake_case,
        "selector_signature": ev.selector_signature,
    }
    for ev in evm_abi_events(_FACTORY_ABI_JSON)
}

Stage 1 — Pool Discovery Pipeline

Query

Fetches logs from the Factory contract address, filtered by the PoolCreated topic0 hash. The fields parameter selects which log columns to include.

query = ingest.Query(
    kind=ingest.QueryKind.EVM,
    params=ingest.evm.Query(
        from_block=from_block,
        to_block=to_block,
        logs=[
            ingest.evm.LogRequest(
                address=[UNISWAP_V3_FACTORY],
                topic0=[factory_events["PoolCreated"]["topic0"]],
            )
        ],
        fields=ingest.evm.Fields(
            log=ingest.evm.LogFields(
                block_number=True, block_hash=True,
                transaction_hash=True, log_index=True,
                address=True, topic0=True, topic1=True,
                topic2=True, topic3=True, data=True,
            ),
        ),
    ),
)

Steps

1. EVM_DECODE_EVENTS — Decodes the raw PoolCreated log into typed columns: token0, token1, fee, tickSpacing, and pool. The decoded output goes into a new uniswap_v3_pool_created table.
2. HEX_ENCODE — Converts all binary columns to 0x… hex strings.

steps = [
    cc.Step(
        kind=cc.StepKind.EVM_DECODE_EVENTS,
        config=cc.EvmDecodeEventsConfig(
            event_signature=factory_events["PoolCreated"]["signature"],
            input_table=POOL_CREATED_LOGS_TABLE,
            output_table=POOL_CREATED_TABLE,
            allow_decode_fail=False,
        ),
    ),
    cc.Step(
        kind=cc.StepKind.HEX_ENCODE,
        config=cc.HexEncodeConfig(),
    ),
]

Tiders ingests raw EVM logs into a default logs table. table_aliases renames it so the decode step can reference it by a descriptive name

Bridging Stages

After Stage 1 writes the decoded pool data, we read back the pool addresses using plain Python. Tiders doesn’t prescribe how you connect pipeline stages — use whatever method fits your storage backend:

pool_addresses = await load_pool_addresses(database)

Stage 2 — Pool Events Pipeline

Query

Re-queries the same block range, but now filtered to the discovered pool addresses. No topic filter — we want ALL events from these pools:

query = ingest.Query(
    kind=ingest.QueryKind.EVM,
    params=ingest.evm.Query(
        from_block=from_block,
        to_block=to_block,
        logs=[
            ingest.evm.LogRequest(
                address=pool_addresses,
            )
        ],
        ...
    ),
)

Steps

A decode step is created dynamically for each event in the pool contracts. Each event gets decoded from the shared raw logs table into its own output table (e.g., uniswap_v3_pool_swap, uniswap_v3_pool_mint).

for _, event in pool_events.items():
    output_table = f"uniswap_v3_pool_{event['name_snake_case']}"
    steps.append(
        cc.Step(
            kind=cc.StepKind.EVM_DECODE_EVENTS,
            config=cc.EvmDecodeEventsConfig(
                event_signature=event["signature"],
                input_table=POOL_EVENTS_TABLE,
                output_table=output_table,
                allow_decode_fail=True,
                filter_by_topic0=True,
            ),
        ),
    )

• filter_by_topic0=True — only attempts to decode logs whose topic0 matches this event’s signature, since the raw table contains mixed event types.
• allow_decode_fail=True — skips logs that don’t match the expected format without raising errors.

After decoding, a CAST_BY_TYPE step downcasts decimal256 to decimal128, and HEX_ENCODE converts binary fields to hex strings.

Key Concepts

Factory + children pattern — The most common multi-stage indexing pattern in DeFi. Stage 1 reads the factory to discover child contracts; Stage 2 indexes the children. This same approach works for any protocol with factory-deployed contracts (Uniswap V2 pairs, Aave markets, Compound cTokens, etc.).

Table aliases — Tiders names raw ingested tables generically (logs, blocks, etc.). Use table_aliases to give them descriptive names per pipeline, which is especially important when running multiple pipelines that write to the same database.

Dynamic decode steps — Instead of hardcoding a decode step for each event, this example loops over all events in the pool ABI and creates a step for each. This keeps the pipeline maintainable as ABIs evolve.

Cast step — EVM int256/uint256 values are decoded as decimal256(76,0). The cast_by_type step downcasts to decimal128(38,0) for databases that don’t support 256-bit decimals. This step is dispensable in PyArrow datasets (Parquet).

Core Libraries Overview

tiders-core is a Rust workspace that provides the high-performance engine behind tiders. It handles data ingestion, ABI decoding, type casting, encoding, and schema definitions.

The Python SDK calls into these libraries via PyO3 bindings.

Crates

Dependency Graph

tiders-core (re-exports)
├── tiders-ingest
│   ├── tiders-evm-schema
│   ├── tiders-svm-schema
│   └── tiders-rpc-client (optional, for RPC provider)
├── tiders-evm-decode
├── tiders-svm-decode
├── tiders-cast
└── tiders-query

Rust API Reference

Auto-generated API documentation for all crates:

• tiders-core rustdoc

Python API

The tiders_core Python module exposes these functions directly:

Ingest

The tiders-ingest crate handles data fetching from blockchain data providers. It provides a unified streaming interface regardless of the backend (HyperSync, SQD, or RPC).

Python Usage

from tiders_core.ingest import (
    ProviderConfig,
    ProviderKind,
    Query,
    QueryKind,
    start_stream,
)

Provider Configuration

provider = ProviderConfig(
    kind=ProviderKind.HYPERSYNC,   # or SQD, RPC
    url="https://eth.hypersync.xyz",
    stop_on_head=False,            # keep polling for new blocks
    batch_size=100,                # blocks per batch
)

EVM Query

from tiders_core.ingest import evm

query = Query(
    kind=QueryKind.EVM,
    params=evm.Query(
        from_block=18_000_000,
        to_block=18_001_000,
        logs=[evm.LogRequest(...)],
        transactions=[evm.TransactionRequest(...)],
        fields=evm.Fields(...),
    ),
)

SVM Query

from tiders_core.ingest import svm

query = Query(
    kind=QueryKind.SVM,
    params=svm.Query(
        from_block=330_000_000,
        instructions=[svm.InstructionRequest(...)],
        fields=svm.Fields(...),
    ),
)

Rust API

See the tiders_ingest rustdoc for the full Rust API.

The main entry point is start_stream(), which returns an async stream of Result<RecordBatch> items.

EVM Decode

The tiders-evm-decode crate decodes EVM smart contract data (events and function calls) from raw bytes into typed Arrow columns.

Python Usage

Decode Events

from tiders_core import evm_decode_events, evm_signature_to_topic0

# Compute topic0 for filtering
topic0 = evm_signature_to_topic0("Transfer(address,address,uint256)")

# Decode log records into typed columns
decoded = evm_decode_events(
    record_batch,
    "Transfer(address indexed from, address indexed to, uint256 amount)",
)

Decode Function Calls

from tiders_core import evm_decode_call_inputs, evm_decode_call_outputs

inputs = evm_decode_call_inputs(record_batch, "transfer(address to, uint256 amount)")
outputs = evm_decode_call_outputs(record_batch, "balanceOf(address) returns (uint256)")

Get Arrow Schema

from tiders_core import evm_event_signature_to_arrow_schema

schema = evm_event_signature_to_arrow_schema(
    "Transfer(address indexed from, address indexed to, uint256 amount)"
)

How It Works

1. Parses the Solidity ABI signature string
2. Uses alloy DynSolEvent/DynSolCall for ABI decoding
3. Maps Solidity types to Arrow types (addresses, uint256, bytes, nested structs, arrays)
4. Returns an Arrow RecordBatch with decoded columns

Rust API

See the tiders_evm_decode rustdoc for the full API.

SVM Decode

The tiders-svm-decode crate decodes Solana program instructions and logs from raw bytes into typed Arrow columns.

Python Usage

Decode Instructions

from tiders_core.svm_decode import InstructionSignature, ParamInput, DynType, FixedArray

# Define the instruction layout
signature = InstructionSignature(
    discriminator="0xe445a52e51cb9a1d40c6cde8260871e2",
    params=[
        ParamInput(name="Amm", param_type=FixedArray(DynType.U8, 32)),
        ParamInput(name="InputMint", param_type=FixedArray(DynType.U8, 32)),
        ParamInput(name="InputAmount", param_type=DynType.U64),
        ParamInput(name="OutputMint", param_type=FixedArray(DynType.U8, 32)),
        ParamInput(name="OutputAmount", param_type=DynType.U64),
    ],
    accounts_names=[],
)

from tiders_core import svm_decode_instructions

decoded = svm_decode_instructions(record_batch, signature)

Available Types

• DynType.U8, U16, U32, U64, U128
• DynType.I8, I16, I32, I64, I128
• DynType.BOOL, DynType.STRING
• FixedArray(inner_type, length) — fixed-size arrays
• DynArray(inner_type) — variable-length arrays

Get Arrow Schema

from tiders_core import instruction_signature_to_arrow_schema

schema = instruction_signature_to_arrow_schema(signature)

Rust API

See the tiders_svm_decode rustdoc for the full API.

Cast

The tiders-cast crate provides blockchain-aware type casting for Arrow columns. It extends standard Arrow casting with support for types common in blockchain data.

Python Usage

Cast by Column Name

import pyarrow as pa
from tiders_core import cast

# Cast specific columns to target types
casted_batch = cast(
    [("block_number", pa.int64()), ("value", pa.decimal128(38, 0))],
    record_batch,
)

Cast by Type

Convert all columns of one Arrow type to another:

from tiders_core import cast_by_type

# Convert all decimal256 columns to decimal128
casted_batch = cast_by_type(
    pa.decimal256(76, 0),
    pa.decimal128(38, 0),
    record_batch,
    allow_cast_fail=True,
)

This is useful when a downstream system (like DuckDB) doesn’t support certain types.

Encoding Utilities

The cast module also provides encoding/decoding functions:

from tiders_core import hex_encode, prefix_hex_encode, base58_encode

# Encode binary columns
hex_batch = hex_encode(record_batch)           # "0a1b2c..."
prefixed = prefix_hex_encode(record_batch)     # "0x0a1b2c..."
b58_batch = base58_encode(record_batch)        # Base58 format

Rust API

See the tiders_cast rustdoc for the full API.

Schemas

The tiders-evm-schema and tiders-svm-schema crates define the canonical Arrow schemas for blockchain data tables.

EVM Schemas

Tables produced by EVM data sources:

All binary fields (hashes, addresses) are stored as Arrow Binary type by default. Use the HEX_ENCODE step to convert them to readable strings.

SVM Schemas

Tables produced by SVM (Solana) data sources:

Field Selection

You don’t fetch all columns by default. Use the Fields types in your query to select only the columns you need:

fields = ingest.evm.Fields(
    block=ingest.evm.BlockFields(number=True, timestamp=True),
    log=ingest.evm.LogFields(block_number=True, address=True, data=True),
)

This reduces network transfer, memory usage, and processing time.

Rust API

• tiders_evm_schema rustdoc
• tiders_svm_schema rustdoc

RPC Client Overview

tiders-rpc-client is a Rust library for fetching EVM blockchain data from any standard JSON-RPC endpoint and converting it to Apache Arrow format.

Unlike specialized providers (HyperSync, SQD), the RPC client works with any Ethereum-compatible JSON-RPC endpoint — Alchemy, Infura, QuickNode, local nodes, or any other provider.

Data Types and Pipelines

The client fetches blockchain data through three independent pipelines, each wrapping a specific RPC method:

The block pipeline also handles an internal transaction receipts pipeline via eth_getBlockReceipts. When the query requests receipt fields (e.g. status, gas_used, effective_gas_price), the block pipeline automatically fetches receipts and merges them into the transaction data.

When a query requires data from more than one pipeline, the client uses a multi-pipeline stream that runs all needed pipelines over the same block range in each batch and merges the results into a single response.

See Pipelines for details on each pipeline, the multi-pipeline stream, and the historical/live phases.

Key Features

• Streaming — data is returned as a stream of Arrow RecordBatches
• Adaptive concurrency — automatically adjusts parallelism based on provider response times
• Retry logic — built-in error recovery with exponential backoff
• Block range fallback — splits large eth_getLogs ranges when providers reject them
• Field selection — fetch only the columns you need

Usage via tiders (Python)

The simplest way to use the RPC client is through the tiders Python SDK:

from tiders_core.ingest import ProviderConfig, ProviderKind

provider = ProviderConfig(
    kind=ProviderKind.RPC,
    url="https://eth-mainnet.g.alchemy.com/v2/YOUR_KEY",
    stop_on_head=True,
    batch_size=10,
)

See the RPC pipeline example for a complete working example.

Usage as a Rust Library

#![allow(unused)]
fn main() {
use tiders_rpc_client::{Client, ClientConfig, Query};

let config = ClientConfig::new("https://eth-mainnet.g.alchemy.com/v2/YOUR_KEY");
let client = Client::new(config);
let mut stream = client.stream(query);

while let Some(response) = stream.next().await {
    let response = response?;
    // response.blocks, response.transactions, response.logs, response.traces
}
}

Rust API Reference

See the tiders_rpc_client rustdoc for the full API.

Pipelines

The RPC client organizes data fetching into three independent pipelines, each targeting a specific JSON-RPC method. When a query needs more than one pipeline, they are coordinated through a multi-pipeline stream.

Historical and Live Phases

Each pipeline operates in two phases:

1. Historical — fetches all data from from_block to the chain head (or to_block if specified), using concurrent tasks for throughput
2. Live — after catching up, polls for new blocks at the interval set by head_poll_interval_millis and fetches data sequentially

If stop_on_head is set to true, the stream ends after the historical phase without entering live mode.

Block Pipeline

Fetches blocks and transactions using eth_getBlockByNumber.

• Sends batch RPC calls for a range of block numbers
• Transactions are extracted from the block response — no separate RPC call is needed
• Concurrency is managed by the block adaptive concurrency controller

eth_getBlockByNumber returns all transactions in a block with no server-side filtering. Setting filter fields (e.g. from_, to, sighash, status) on a TransactionRequest will produce an error. This functionality is only supported on other Tiders’ clients (sqd, hypersync). Ingest all transactions and filter post-indexing in your Tiders (python) pipeline or database instead.

Transaction Receipts

When the query requests transaction receipts fields (e.g. status, gas_used, effective_gas_price), the block pipeline automatically fetches transaction receipts via eth_getBlockReceipts and merges them into the transaction data. This runs as a sub-step inside the block pipeline, not as a separate pipeline.

Each block’s receipts are fetched individually in parallel, bounded by the single-block adaptive concurrency controller.

Log Pipeline

Fetches event logs using eth_getLogs.

• Constructs filters from the query’s log requests (addresses and topics)
• Concurrency is managed by the log adaptive concurrency controller
• Automatically splits large address lists into groups of 1000 per request
• When a provider rejects a block range as too large, the pipeline automatically limits the block range and retries

Log filters (addresses and topics) cannot be combined with include_* flags on the same LogRequest. When include_* flags activate additional pipelines, those pipelines return unfiltered data for the full block range — combining that with filtered logs would produce an inconsistent response. To use cross-pipeline coordination, remove the log filters and filter post-indexing.

Trace Pipeline

Fetches internal call traces using trace_block or debug_traceBlockByNumber.

• The trace method is auto-detected from the provider or can be overridden via trace_method in the configuration
• Each block is traced individually in parallel, bounded by the single-block adaptive concurrency controller
• Each block is retried independently up to max_num_retries times

trace_block and debug_traceBlockByNumber return all traces in a block with no server-side filtering. Setting filter fields (e.g. from_, to, call_type, sighash) on a TraceRequest will produce an error. This functionality is only supported on other Tiders’ clients (sqd, hypersync). Ingest all transactions and filter post-indexing in your Tiders (python) pipeline or database instead.

Note: Tracing requires a provider that supports block-level trace methods.

Multi-Pipeline Stream

When a query requires data from more than one pipeline, the client automatically switches to a coordinated multi-pipeline stream instead of running individual pipeline streams.

The coordinator:

1. Divides the block range into fixed-size batches (sized by batch_size). Unlike single-pipeline mode, the batch size does not adapt — it stays fixed throughout the run, so each response covers the same number of blocks.
2. Runs all needed pipelines for each batch over the same block range. Pipelines run sequentially within a batch to avoid interference between their adaptive concurrency controllers. Concurrency parameters carry over from one batch to the next.
3. Merges the results into a single response containing blocks, transactions, logs, and traces for the entire batch.

This ensures that all data types in a response correspond to the same set of blocks.

Pipeline Selection

Which pipelines run is determined by the query:

• Block pipeline runs if the query requests block or transaction fields, or uses include_all_blocks
• Log pipeline runs if the query has log requests or selects log fields
• Trace pipeline runs if the query has trace requests or selects trace fields

Cross-pipeline include_* flags (e.g. include_transactions on a log request) can also activate additional pipelines.

If a query selects fields from multiple pipelines (e.g. both log and block fields) without setting include_* flags on any request, the client will return an error. This prevents accidental multi-pipeline queries. Either use include_* flags to opt in to cross-pipeline coordination, or split into separate queries.

RPC Client Configuration

The ClientConfig struct controls how the RPC client connects to the provider and manages request behavior. In Python, configuration is done through ProviderConfig with ProviderKind.RPC.

Basic Configuration

Rust

#![allow(unused)]
fn main() {
use tiders_rpc_client::ClientConfig;

let config = ClientConfig::new("https://eth-mainnet.g.alchemy.com/v2/YOUR_KEY");
}

Python

from tiders_core.ingest import ProviderConfig, ProviderKind

provider = ProviderConfig(
    kind=ProviderKind.RPC,
    url="https://eth-mainnet.g.alchemy.com/v2/YOUR_KEY",
)

Configuration Options

Rust API Reference

See the ClientConfig rustdoc for all fields and methods.

RPC Client Querying

The Query type defines what data to fetch from the RPC endpoint.

Query Structure

Rust

#![allow(unused)]
fn main() {
use tiders_rpc_client::{Query, LogRequest, TransactionRequest, TraceRequest};
use tiders_rpc_client::{Fields, BlockFields, TransactionFields, LogFields, TraceFields};

let query = Query {
    from_block: 18_000_000,
    to_block: Some(18_001_000),
    include_all_blocks: false,
    logs: vec![LogRequest { .. }],
    transactions: vec![TransactionRequest { .. }],
    traces: vec![TraceRequest { .. }],
    fields: Fields {
        block: BlockFields { number: true, timestamp: true, ..Default::default() },
        transaction: TransactionFields { hash: true, ..Default::default() },
        log: LogFields { address: true, data: true, ..Default::default() },
        trace: TraceFields::default(),
    },
};
}

Python

from tiders_core import ingest

query = ingest.Query(
    kind=ingest.QueryKind.EVM,
    params=ingest.evm.Query(
        from_block=18_000_000,
        to_block=18_001_000,
        include_all_blocks=False,
        logs=[ingest.evm.LogRequest(...)],
        transactions=[ingest.evm.TransactionRequest(...)],
        traces=[ingest.evm.TraceRequest(...)],
        fields=ingest.evm.Fields(
            block=ingest.evm.BlockFields(number=True, timestamp=True),
            transaction=ingest.evm.TransactionFields(hash=True),
            log=ingest.evm.LogFields(address=True, data=True),
            trace=ingest.evm.TraceFields(),
        ),
    ),
)

Log Requests

Filter logs by address and/or topics. Multiple addresses and topics are OR’d together by the provider.

Rust

#![allow(unused)]
fn main() {
LogRequest {
    address: vec![Address::from_str("0xdAC17F958D2ee523a2206206994597C13D831ec7")?],
    topic0: vec![topic0_hash],
    include_blocks: true,
    ..Default::default()
}
}

Python

ingest.evm.LogRequest(
    address=["0xdAC17F958D2ee523a2206206994597C13D831ec7"],
    topic0=[topic0_hash],
    include_blocks=True,
)

Log filters (addresses and topics) cannot be combined with include_* flags on the same LogRequest. When include_* flags activate additional pipelines, those pipelines return unfiltered data for the full block range. To use multi-pipeline coordination remove the log filters and filter post-indexing.

Transaction Requests

Activates the block pipeline to fetch blocks and transactions via eth_getBlockByNumber.

Rust

#![allow(unused)]
fn main() {
TransactionRequest {
    include_logs: true,
    include_blocks: true,
    ..Default::default()
}
}

Python

ingest.evm.TransactionRequest(
    include_logs=True,
    include_blocks=True,
)

TransactionRequest also exposes filter fields (from_, to, sighash, status, type_, contract_deployment_address, hash), but these are not supported by the RPC client. eth_getBlockByNumber returns all transactions in a block with no server-side filtering. Setting any of these fields will produce an error. This functionality is only supported on other tiders clients (SQD, HyperSync). Ingest all transactions and filter post-indexing in your tiders (Python) pipeline or database instead.

Trace Requests

Activates the trace pipeline to fetch internal call traces.

Rust

#![allow(unused)]
fn main() {
use tiders_rpc_client::TraceMethod;

TraceRequest {
    trace_method: TraceMethod::TraceBlock,  // or DebugTraceBlockByNumber
    include_blocks: true,
    ..Default::default()
}
}

Python

ingest.evm.TraceRequest(
    include_blocks=True,
)

TraceRequest also exposes filter fields (from_, to, address, call_type, reward_type, type_, sighash, author), but these are not supported by the RPC client. trace_block and debug_traceBlockByNumber return all traces in a block with no server-side filtering. Setting any of these fields will produce an error. This functionality is only supported on other tiders clients (SQD, HyperSync). Ingest all traces and filter post-indexing in your tiders (Python) pipeline or database instead.

Field Selection

Select only the fields you need to minimize data transfer. When all flags are false (the default), the full schema is returned.

Rust

#![allow(unused)]
fn main() {
Fields {
    block: BlockFields { number: true, hash: true, timestamp: true, ..Default::default() },
    transaction: TransactionFields { hash: true, from: true, to: true, value: true, ..Default::default() },
    log: LogFields { address: true, data: true, topic0: true, ..Default::default() },
    trace: TraceFields::default(),
}
}

Python

ingest.evm.Fields(
    block=ingest.evm.BlockFields(number=True, hash=True, timestamp=True),
    transaction=ingest.evm.TransactionFields(hash=True, from_=True, to=True, value=True),
    log=ingest.evm.LogFields(address=True, data=True, topic0=True),
    trace=ingest.evm.TraceFields(),
)

Block Fields

Transaction Fields

Fields marked with (receipt) require eth_getBlockReceipts — the block pipeline fetches receipts automatically when any of these fields are selected.

Log Fields

Trace Fields

Response Format

The stream yields ArrowResponse items containing Arrow RecordBatches:

Rust

#![allow(unused)]
fn main() {
let mut stream = client.stream(query);

while let Some(response) = stream.next().await {
    let response = response?;
    // response.blocks — Arrow RecordBatch
    // response.transactions — Arrow RecordBatch
    // response.logs — Arrow RecordBatch
    // response.traces — Arrow RecordBatch
}
}

Rust API Reference

See the Query rustdoc for all fields.

Adaptive Concurrency

The RPC client automatically adjusts its parallelism and request pacing based on provider responses, maximizing throughput without overwhelming the provider or hitting rate limits.

There are three independent adaptive concurrency controllers, one for each type of RPC call pattern. Each controller’s value determines how many parallel tasks run concurrently for that pipeline.

The single-block controller is shared between traces and transaction receipts requests. It starts more aggressively because per-block calls are smaller and faster than batch calls.

How It Works

All controllers use the same adaptive algorithm, implemented lock-free with atomics.

Scaling Up

On each successful RPC call:

1. Backoff delay is reduced by 25%
2. A consecutive success counter increments
3. After reaching the scale-up threshold, concurrency increases:
   • Block and log controllers: +20% after 10 consecutive successes
   • Single-block controller: +33% after 50 consecutive successes

The single-block controller requires more consecutive successes before scaling up because it runs many more concurrent calls.

Scaling Down on Rate Limits

When a rate-limit error is detected (HTTP 429 or provider rate-limit message):

1. Consecutive success counter resets to 0
2. Backoff delay doubles (starting from 500 ms, capped at 30 s)
3. Concurrency is halved (down to the minimum)

Scaling Down on General Errors

When a non-rate-limit error occurs:

1. Consecutive success counter resets to 0
2. Concurrency is reduced by 10% (gentler than rate limits)

Chunk Size Adaptation

The block and log controllers also adapt the chunk size — the number of blocks per RPC call.

The initial chunk size can be set by the batch_size configuration option.

Block Range Fallback

When a provider rejects a request because the block range is too large, the log controller tries to parse the error to extract a suggested range. It understands error formats from many providers:

• Alchemy — "this block range should work: [0x..., 0x...]"
• Infura / Thirdweb / zkSync / Tenderly — "try with this block range [0x..., 0x...]"
• Ankr — "block range is too wide"
• QuickNode / 1RPC / zkEVM / Blast / BlockPI — "limited to a 10,000"
• Base — "block range too large"

When no provider hint is available, the pipeline falls through a tiered fallback: 5000 → 500 → 75 → 50 → 45 → 40 → 35 → 30 → 25 → 20 → 15 → 10 → 5 → 1 blocks.

The block controller uses a simpler strategy: on block-range errors it halves the range.

Chunk Size Recovery

After chunk size has been reduced due to errors, the block and log controllers periodically attempt to reset it to the original value. On each successful call, there is a 10% probability of resetting the chunk size back to the configured (or default) value. This allows the system to recover from temporary provider issues without permanently degrading throughput.

Backoff

Each controller maintains a backoff delay that is applied before every RPC call:

• Starts at 0 ms (no delay)
• On rate limit: doubles, starting from 500 ms, capped at 30 s
• On success: reduced by 25% per call
• Backoff applies to all concurrent calls sharing the same controller

Rust API Reference

The Rust API documentation is auto-generated from source code using rustdoc. It provides detailed documentation for every public type, function, and module.

Browse the API

• tiders-core — re-exports all core crates
  • tiders-ingest — data ingestion and streaming
  • tiders-evm-decode — EVM ABI decoding
  • tiders-svm-decode — Solana instruction decoding
  • tiders-cast — type casting
  • tiders-evm-schema — EVM Arrow schemas
  • tiders-svm-schema — SVM Arrow schemas
  • tiders-query — query execution
• tiders-rpc-client — EVM RPC data fetcher

Building the API Docs Locally

To generate the rustdoc output locally:

# tiders-core (all crates)
cd tiders-core
cargo doc --no-deps --workspace

# tiders-rpc-client
cd tiders-rpc-client/rust
cargo doc --no-deps

# Open in browser
open target/doc/tiders_core/index.html

Architeture

This section walks you through how is tiders architecture.

Dependency Graph

tiders-evm-schema        (no deps)
tiders-svm-schema         (no deps)
tiders-cast               (no deps)
tiders-query              (no deps)
tiders-evm-decode         (no deps)
tiders-svm-decode         (no deps)
    │
    ├──► tiders-rpc-client
    │        └── tiders-evm-schema
    │
    ├──► tiders-ingest
    │        ├── tiders-evm-schema
    │        ├── tiders-svm-schema
    │        ├── tiders-cast
    │        ├── tiders-query
    │        └── tiders-rpc-client
    │
    ├──► tiders-core
    │        ├── tiders-evm-schema
    │        ├── tiders-svm-schema
    │        ├── tiders-cast
    │        ├── tiders-query
    │        ├── tiders-evm-decode
    │        ├── tiders-svm-decode
    │        └── tiders-ingest
    │
    └──► tiders-core-python
             └── tiders-core (aliased as "baselib")