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Custom Indexer

You can build custom indexers using the IOTA micro-data ingestion framework. To create an indexer, you subscribe to a checkpoint stream with full checkpoint content. This stream can be one of the publicly available streams from IOTA, one that you set up in your local environment, or a combination of the two.

Establishing a custom indexer helps improve latency, allows pruning the data of your IOTA Full node, and provides efficient assemblage of checkpoint data.

Interface and Data Format​

To use the framework, implement a basic interface:

#[async_trait]
trait Worker: Send + Sync {
    async fn process_checkpoint(&self, checkpoint: CheckpointData) -> Result<()>;
}

In this example, the CheckpointData struct represents full checkpoint content. The struct contains checkpoint summary and contents, as well as detailed information about each individual transaction. The individual transaction data includes events and input/output objects. The full definition for this content is in the full_checkpoint_content.rs file of the iota-types crate.

Checkpoint Stream Sources​

Data ingestion for your indexer supports several checkpoint stream sources.

Remote Reader​

The most straightforward stream source is to subscribe to a remote store of checkpoint contents. IOTA provides the following buckets:

  • Testnet: https://indexer.testnet.iota.cafe
  • Devnet: https://indexer.devnet.iota.cafe

The checkpoint files are stored in the following format: https://indexer.testnet.iota.cafe/<checkpoint_id>.chk. You can download the checkpoint file by sending an HTTP GET request to the relevant URL. Try it yourself for checkpoint 1 at https://indexer.testnet.iota.cafe/1.chk.

The IOTA data ingestion framework provides a helper function to quickly bootstrap an indexer workflow.

// Copyright (c) Mysten Labs, Inc.
// Modifications Copyright (c) 2024 IOTA Stiftung
// SPDX-License-Identifier: Apache-2.0

use anyhow::Result;
use async_trait::async_trait;
use iota_types::full_checkpoint_content::CheckpointData;
use iota_data_ingestion_core::{Worker, setup_single_workflow};

struct CustomWorker;

#[async_trait]
impl Worker for CustomWorker {
    async fn process_checkpoint(&self, checkpoint: CheckpointData) -> Result<()> {
        // custom processing logic
        // print out the checkpoint number
        println!("Processing checkpoint: {}", checkpoint.checkpoint_summary.to_string());
        Ok(())
    }
}
                    
#[tokio::main]
async fn main() -> Result<()> {
    let (executor, term_sender) = setup_single_workflow(
        CustomWorker,
        "https://checkpoints.testnet.iota.io".to_string(),
        0, /* initial checkpoint number */
        5, /* concurrency */
        None, /* extra reader options */
    ).await?;
    executor.await?;
    Ok(())
}

This is suitable for setups with a single ingestion pipeline where progress tracking is managed outside of the framework.

Local Reader​

Colocate the data ingestion daemon with a Full node and enable checkpoint dumping on the latter to set up a local stream source. After enabling, the Full node starts dumping executed checkpoints as files to a local directory, and the data ingestion daemon subscribes to changes in the directory through an inotify-like mechanism. This approach allows minimizing ingestion latency (checkpoint are processed immediately after a checkpoint executor on a Full node) and getting rid of dependency on an externally managed bucket.

To enable, add the following to your Full node configuration file:

checkpoint-executor-config:
  checkpoint-execution-max-concurrency: 200
  local-execution-timeout-sec: 30
  data-ingestion-dir: <path to a local directory>
// Copyright (c) Mysten Labs, Inc.
// Modifications Copyright (c) 2024 IOTA Stiftung
// SPDX-License-Identifier: Apache-2.0

use tokio::sync::oneshot;
use anyhow::Result;
use async_trait::async_trait;
use iota_types::full_checkpoint_content::CheckpointData;
use iota_data_ingestion_core as sdic;
use sdic::{Worker, WorkerPool, ReaderOptions};
use sdic::{DataIngestionMetrics, FileProgressStore, IndexerExecutor};
use prometheus::Registry;
use std::path::PathBuf;
use std::env;

struct CustomWorker;

#[async_trait]
impl Worker for CustomWorker {
    async fn process_checkpoint(&self, checkpoint: CheckpointData) -> Result<()> {
        // custom processing logic
        println!("Processing Local checkpoint: {}", checkpoint.checkpoint_summary.to_string());
        Ok(())
    }
}
        
#[tokio::main]
async fn main() -> Result<()> {
    let concurrency = 5;
    let (exit_sender, exit_receiver) = oneshot::channel();
    let metrics = DataIngestionMetrics::new(&Registry::new());
    let backfill_progress_file_path =
        env::var("BACKFILL_PROGRESS_FILE_PATH").unwrap_or("/tmp/local_reader_progress".to_string());
    let progress_store = FileProgressStore::new(PathBuf::from(backfill_progress_file_path));
    let mut executor = IndexerExecutor::new(progress_store, 1 /* number of workflow types */, metrics);
    let worker_pool = WorkerPool::new(CustomWorker, "local_reader".to_string(), concurrency);

    executor.register(worker_pool).await?;
    executor.run(
        PathBuf::from("./chk".to_string()), // path to a local directory
        None,
        vec![], // optional remote store access options
        ReaderOptions::default(),       /* remote_read_batch_size */
        exit_receiver,
        ).await?;
    Ok(())
}

Let's highlight a couple lines of code:

let worker_pool = WorkerPool::new(CustomWorker, "local_reader".to_string(), concurrency);
executor.register(worker_pool).await?;

The data ingestion executor can run multiple workflows simultaneously. For each workflow, you need to create a separate worker pool and register it in the executor. The WorkerPool requires an instance of the Worker trait, the name of the workflow (which is used for tracking the progress of the flow in the progress store and metrics), and concurrency.

The concurrency parameter specifies how many threads the workflow uses. Having a concurrency value greater than 1 is helpful when tasks are idempotent and can be processed in parallel and out of order. The executor only updates the progress/watermark to a certain checkpoint when all preceding checkpoints are processed.

Hybrid Mode​

Specify both a local and remote store as a fallback to ensure constant data flow. The framework always prioritizes locally available checkpoint data over remote data. It's useful when you want to start utilizing your own Full node for data ingestion but need to partially backfill historical data or just have a failover.

executor.run(
    PathBuf::from("./chk".to_string()), // path to a local directory
    Some("https://indexer.testnet.iota.cafe".to_string()), // Remote Checkpoint Store
    vec![], // optional remote store access options
    ReaderOptions::default(),
    exit_receiver,
    ).await?;

Manifest​

Code for the cargo.toml manifest file for the custom indexer.

[package]
name = "custom-indexer"
version = "0.1.0"
edition = "2021"

[dependencies]
# external dependencies
anyhow = "1.0.86"
async-trait = "0.1.81"
prometheus = "0.13.3"
tokio = { version = "1.38.0", features = ["full"] }

# internal dependencies
iota_data_ingestion_core = { git = "https://github.com/iotaledger/iota", package = "iota-data-ingestion-core" }
iota_types = { git = "https://github.com/iotaledger/iota", package = "iota-types" }

[[bin]]
name = "local_reader"
path = "local_reader.rs"

[[bin]]
name = "remote_reader"
path = "remote_reader.rs"

[workspace]

Source Code​

Find the following source code in the IOTA repo.

Question 1/3

Which trait must be implemented to process checkpoint data in a custom indexer?