How to Monitor Data Availability Health

Data availability ensures that for any block proposed, the underlying transaction data is published to the network. This allows nodes to independently verify the block's validity. Without this guarantee, malicious validators could hide data, potentially committing invalid transactions. This is a core challenge addressed by Ethereum's danksharding roadmap and specialized data availability layers like Celestia, EigenDA, and Avail. Monitoring DA health involves tracking metrics that confirm data is being correctly propagated and stored.

Key health indicators include block inclusion rates, data sampling success rates, and node participation. For example, on a network using data availability sampling (DAS), light clients perform random checks to see if data is retrievable. A drop in sampling success rates signals a potential availability failure. Tools like Chainscore's Data Availability Dashboard aggregate these metrics across multiple layers, providing a real-time view of system health. Monitoring these signals helps developers and users assess the reliability of the chain they are building on or transacting with.

For developers, integrating DA monitoring is essential for cross-chain applications and rollup security. An optimistic rollup must ensure its transaction data is posted to a secure DA layer during its dispute window. A zk-rollup similarly relies on data availability for operational data like withdrawal proofs. By programmatically checking DA status via provider APIs or RPC endpoints, applications can trigger fallback mechanisms or alert users during outages. Proactive monitoring shifts the paradigm from reacting to failures to preventing them, creating more resilient decentralized systems.

Data availability (DA) is the guarantee that all transaction data for a new block is published and accessible to network participants. It's a critical security property for rollups and other Layer 2 scaling solutions. Without it, validators cannot reconstruct the chain's state or verify transaction correctness, leading to potential censorship or fraud. Key protocols in this space include Celestia, EigenDA, and Avail, each with unique architectures for publishing and verifying data blobs.

To monitor DA health, you'll need a basic understanding of how these systems work. For example, Celestia uses Data Availability Sampling (DAS), where light nodes download small random samples of a block to probabilistically verify its availability. You should be familiar with core components like blobs, data roots, KZG commitments, and the role of full nodes versus light clients. A grasp of the data publishing lifecycle—from sequencer to DA network to verifier—is essential for identifying failure points.

Your technical setup requires access to blockchain data. You will need an RPC endpoint for the target chain (e.g., an Ethereum rollup) and, if monitoring directly, access to the DA layer's node (like a Celestia light node). Familiarity with a programming language like JavaScript (Node.js) or Python is necessary for writing scripts. Essential tools include a package manager (npm or pip), the Ethers.js or web3.py library for blockchain interaction, and potentially the DA protocol's specific SDK, such as @celestiaorg/js-celestia-client.

You should also understand the key metrics for health monitoring. These include block inclusion latency (time for data to be posted), data root availability (can the root be retrieved?), sampling success rate for light clients, and gas costs for DA posting transactions. Setting up alerts for thresholds on these metrics is a core monitoring objective. For practical testing, you can use public testnets like Celestia's Mocha or EigenDA's Holesky testnet before moving to mainnet.

Finally, ensure you have a clear objective. Are you monitoring for personal research, a dApp's dependency, or validator security? Your goal dictates the tools' complexity—from simple scripted checks to a dedicated dashboard using services like Chainscore, Dune Analytics, or Grafana. Start by querying basic data, such as fetching the latest data root from a rollup contract or checking a DA layer's block explorer API, to confirm your setup works.

Data Availability (DA) refers to the guarantee that the data for a new block is published and accessible to all network participants, allowing them to independently verify state transitions. A data availability failure occurs when a block producer withholds transaction data, making it impossible to detect invalid transactions. This is a critical security concern for scaling solutions like rollups, which rely on posting data to a base layer. Monitoring DA health involves tracking whether data is successfully posted, stored, and remains retrievable within expected timeframes and cost parameters.

Key health indicators vary by DA layer. For Ethereum as a DA layer (using calldata or blobs via EIP-4844), monitor the blobGasUsed and excessBlobGas to assess network congestion and costs. For Celestia, track the square size of blocks and the rate of PayForBlob transactions. EigenDA operators should monitor the quorum attestations and the disperser service latency. Universal metrics include data posting success rate, time-to-confirmation for data inclusion, and data retrieval latency from archival nodes or services like the Celestia Node API or EigenDA's disperser.

To implement monitoring, you need to query specific RPC endpoints and parse chain data. For example, to check Ethereum blob data submission, you can call eth_getBlockByNumber and inspect the blobGasUsed field. For Celestia, use the blob.Submit API response to verify inclusion. Setting up alerts for anomalies—such as a sustained drop in posting success rate below 99.9% or a spike in confirmation time above the 95th percentile—is crucial. Tools like Prometheus for metrics collection and Grafana for dashboards are commonly used, alongside custom scripts that poll DA layer nodes.

Beyond basic availability, consider economic security and decentralization metrics. The cost to attack the DA layer, often measured by the cost to withhold data for a challenge period, is a key security parameter. Monitor the number of active DA nodes or validators and their geographic distribution to assess censorship resistance. For modular stacks, you must also verify the data commitment (like a Merkle root) posted on the settlement layer correctly corresponds to the data batches available on the DA layer, ensuring the integrity bridge between layers is intact.

Proactive monitoring involves simulating failure scenarios. Regularly test data retrieval from multiple light clients or archive nodes to ensure no single point of failure. For rollup operators, implement fraud proof or validity proof readiness checks that depend on DA. The ultimate test is whether a full node can sync from genesis using only the data published to the DA layer. By tracking these technical and economic metrics, developers and network participants can ensure the liveness and security guarantees of their application's data availability layer are met.

Effective monitoring of Celestia's Data Availability (DA) layer is essential for developers building rollups, light clients, or infrastructure services. The network provides two primary interfaces for querying real-time state: a REST API for high-level network data and a gRPC/JSON-RPC endpoint for low-level chain information and transaction submission. This guide focuses on the REST API, which offers a straightforward way to check core health metrics like node sync status, network height, and data availability sampling (DAS) completion without running a full node.

The foundational endpoint for health checks is GET /node/health. A simple curl command can verify if your connected node is synced and operational. For example, querying a local Celestia node might look like this:

bashCopy
curl -s http://localhost:26657/health | jq

A successful response will return {"result": {}}. An empty result object indicates the node is healthy. Any error or non-200 status code suggests a connectivity or syncing issue. For more detailed status, use GET /status to retrieve the node's network ID, latest block height, and validator public key.

To monitor the progress of Data Availability Sampling—the core protocol that ensures data is published and available—you can query the GET /das/stats endpoint. This returns metrics crucial for rollup operators, including the total sampled headers, current sampling rate, and any failed samples. Here is a Python example using the requests library to fetch and parse this data:

pythonCopy
import requests
import time

NODE_REST_URL = "http://localhost:26658"

while True:
    try:
        response = requests.get(f"{NODE_REST_URL}/das/stats")
        stats = response.json()
        print(f"Sampled Headers: {stats.get('head_of_sampled_chain')}")
        print(f"Sampling Rate: {stats.get('sampling_rate')}")
        print(f"Failed Samples: {stats.get('failed_samples')}")
    except Exception as e:
        print(f"Query failed: {e}")
    time.sleep(30)

This script creates a simple live monitor that logs DAS health every 30 seconds.

For comprehensive network monitoring, you should also track block production and peer connectivity. Use GET /net_info to see the number of connected peers (n_peers) and GET /block?height=<height> to inspect specific block data, including the list of transactions in the Data Square. Combining these checks gives you a full picture: a healthy node is synced to the latest network height, maintains multiple peer connections, and successfully samples data from new blocks. Setting up alerts based on thresholds—like a sampling rate drop below 0.9 or peer count falling to zero—can help preempt service disruptions.

Integrate these API calls into your existing monitoring stack, such as Prometheus or Datadog, by creating custom exporters. The key metrics to expose are: celestia_node_synced (boolean), celestia_network_height (gauge), celestia_das_sampling_rate (gauge), and celestia_peer_count (gauge). By programmatically tracking these signals, you ensure your application built on Celestia remains responsive to network conditions, data availability lags, or node failures, providing a reliable experience for end-users.

Monitoring EigenDA's health programmatically is essential for developers building on the network or integrating it as a data availability layer. The system exposes several key data points through its public API endpoints and on-chain contracts. This guide covers how to query for blob availability status, operator performance metrics, and network state using simple scripts. We'll use curl for API calls and ethers.js for on-chain interactions, providing concrete examples you can adapt.

The primary source of truth for blob status is EigenDA's Disperser service. You can query the status of a specific batch or blob using its unique identifier. For example, to check the confirmation status of a batch, you can call the /v1/disperser/data/blobs/status/{batch_header_hash} endpoint. A successful response will include fields like status (e.g., CONFIRMED), confirmation_info, and the batch_id. This is crucial for applications that need to verify data was successfully posted before proceeding.

For a broader view of network health, you should monitor the operator set. Key metrics include the total number of active operators, their stake amounts, and performance scores (like uptime and challenge success rate). These are stored on the EigenDA AVS smart contracts on Ethereum. Using ethers.js, you can connect to the contract and call view functions like getOperatorState() to retrieve this data. Correlating high stake with good performance helps assess network security.

Another critical check is verifying the quorum configurations. EigenDA uses multiple quorums for redundancy, each with its own security parameters and operator set. You should periodically query the Blobstream contract (EigenDA's data commitment bridge to Ethereum) to ensure commitments are being posted regularly for each quorum. A lapse in commitments could indicate an availability issue. Scripts can listen for the BatchConfirmed event or poll the latestBatchId function.

Here is a basic Node.js script using ethers to fetch the latest operator state:

javascriptCopy
const { ethers } = require('ethers');
const provider = new ethers.JsonRpcProvider('YOUR_RPC_URL');
const operatorStateContract = new ethers.Contract(
  '0x...OperatorStateAddress...',
  ['function getOperatorState(uint8 quorumNumber) external view returns (tuple(address, uint96, uint32)[])'],
  provider
);
async function getOps(quorumId) {
  const state = await operatorStateContract.getOperatorState(quorumId);
  console.log(`Quorum ${quorumId} has ${state.length} operators`);
}
getOps(0);

This returns an array of operator addresses, stakes, and scores.

For production monitoring, you should set up alerts based on these queries. Key thresholds to watch include a drop in the number of active operators below a quorum's minimum, a significant decrease in the total stake securing a quorum, or a failure to retrieve a confirmed status for a blob within an expected timeframe. Combining API checks with on-chain verification creates a robust monitoring suite, ensuring your application can react promptly to any degradation in EigenDA's data availability guarantees.

Effective monitoring of the Avail network requires querying specific endpoints to assess data availability (DA) health and node status. The primary interface is the Avail REST API, typically accessible via a local node or a public RPC endpoint like https://api.avail.tools. Key metrics include the latest block height, the number of blob transactions (data submissions), and the network's sync status. For real-time alerts, developers should also connect to the WebSocket endpoint to subscribe to new block headers and transaction events.

To check basic network health, query the /health and /blocks/head endpoints. The health endpoint returns a simple status, while the head endpoint provides details about the latest block, including its data root—a cryptographic commitment to all data in the block. Monitoring the growth of this root's associated data is crucial for verifying that information is available for sampling and reconstruction. Here's a Python example using the requests library to fetch the latest block:

pythonCopy
import requests
API_URL = 'https://api.avail.tools'
response = requests.get(f'{API_URL}/blocks/head')
block_data = response.json()
print(f'Block #{block_data["number"]} | Data Root: {block_data["data_root"]}')

For tracking data submission volume, you need to count blob transactions. These are distinct from regular value transfers and are identified by their transaction type. You can filter transactions in a block or query a dedicated endpoint if available. A drop in blob count may indicate reduced network usage or a submission issue. Additionally, monitoring the peer count via the /system/peers endpoint helps gauge network connectivity and decentralization. A sudden loss of peers could signal isolation or a network partition affecting data availability guarantees.

Implementing a robust monitor involves setting up a WebSocket listener for instant notifications. Subscribing to new block events allows your application to react immediately instead of polling. The following Node.js snippet uses the ws library to log new block headers:

javascriptCopy
const WebSocket = require('ws');
const ws = new WebSocket('wss://api.avail.tools/ws');
ws.on('open', () => {
  ws.send(JSON.stringify({
    "id": 1,
    "method": "chain_subscribeNewHeads",
    "params": []
  }));
});
ws.on('message', (data) => {
  console.log('New block header:', JSON.parse(data));
});

To automate health checks and alerting, structure your monitor as a daemon or cron job. It should periodically check API responsiveness, block production time (ensuring new blocks are created at expected intervals), and the success rate of data submission transactions. Log these metrics to a time-series database like Prometheus for visualization with Grafana. Set up alerts for critical failures: no new blocks for 2 minutes, zero blob transactions for multiple blocks, or health endpoint returning an error. This proactive approach is essential for applications relying on Avail for secure data availability.

Always refer to the official Avail documentation for the most current API specifications and endpoint URLs. The network is under active development, and endpoints may evolve. By integrating these monitoring practices, developers can ensure their applications maintain awareness of the Avail network's state, guaranteeing the data availability their systems depend on for security and operation.

Data availability health refers to the continuous, reliable access to on-chain data required by your dApp or protocol. Monitoring this health is critical because failures in data sources like RPC nodes, indexers, or subgraphs can lead to silent application failures, incorrect state, and poor user experience. A health alert system acts as an early warning mechanism, notifying you of issues like high latency, error rates, or complete unavailability before they impact your users. This is a core component of operational reliability in Web3.

To set up effective alerts, you must first identify your critical data dependencies. These typically include your primary and fallback RPC endpoints for the chains you support (e.g., Ethereum Mainnet, Arbitrum, Base), any third-party indexers like The Graph, and specialized data oracles. For each dependency, define specific, measurable health metrics. Key metrics are latency (response time), success rate (percentage of successful requests), and block height lag (how many blocks behind the chain tip your node is).

Implementing monitoring requires a tool that can periodically query these endpoints. You can use infrastructure like Prometheus with the web exporter to scrape metrics, or a specialized Web3 monitoring service. A simple script can use the eth_blockNumber RPC call to check latency and block height. For a more robust check, also call a method like eth_getBalance for a known address to verify state queries work. The script should log metrics and trigger an alert (via PagerDuty, Slack, or email) when thresholds are breached—for example, if latency exceeds 2000ms or the success rate drops below 95%.

Here is a basic Python example using the Web3.py library to check an RPC endpoint's block height and latency, which you can run as a cron job:

pythonCopy
from web3 import Web3
import time
ALERT_WEBHOOK_URL = 'YOUR_SLACK_WEBHOOK'
w3 = Web3(Web3.HTTPProvider('https://your-rpc-endpoint'))
start = time.time()
try:
    block = w3.eth.block_number
    latency = (time.time() - start) * 1000  # ms
    if latency > 2000:
        send_alert(f"High Latency: {latency}ms")
    print(f"Block: {block}, Latency: {latency:.0f}ms")
except Exception as e:
    send_alert(f"RPC Failed: {e}")

Beyond basic endpoint monitoring, consider semantic health checks. This means verifying the correctness of the data, not just its availability. For an indexer, query for a recent, specific transaction and validate the returned data matches what's on-chain. For a price oracle, check that the reported price is within a reasonable deviation from other sources. Setting up alerts for these logical inconsistencies can catch subtler bugs or attacks, such as a manipulated oracle feed or a misconfigured indexer.

Finally, integrate your alerts into an incident response workflow. Ensure your team knows who is on-call and has documented procedures for common failures, like switching to a backup RPC provider. Regularly review and test your alerting system, including conducting failure drills. Effective monitoring transforms data availability from a hidden variable into a managed resource, giving you the confidence that your application's foundational data layer is sound.