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Fault-Tolerant Realtime Data Ingestion with QuestDB Write-Ahead Logs

#questdb #realtime #kafka #webdev

When you’re tracking thousands of moving objects — drivers, delivery agents, or devices — reliability matters just as much as speed.

You can’t afford to lose a single GPS ping.

That’s where QuestDB’s wal_tables() come in.

questdb title image

What is wal_tables()?

In QuestDB, a Write-Ahead Log (WAL) table is a durable, crash-resilient ingestion mode.

Normally, when data is inserted into a non-WAL table, QuestDB writes it directly to disk in columnar format. That’s blazing fast — but if something crashes mid-write, that data might be lost.

With WAL enabled, all incoming writes are first recorded in a write-ahead log (a transaction log). This ensures that:

  • Writes are durable — no data loss even on crashes.
  • Parallel ingestion is supported.
  • Replication and recovery are faster.

You can list all WAL-enabled tables using:

SELECT * FROM wal_tables();
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This query returns metadata about all active write-ahead log tables — perfect for monitoring ingestion health and consistency.

wal transactions

Example: Kafka + QuestDB for Realtime Location Tracking

Let’s imagine you’re building a location tracking service.
Each user periodically sends GPS updates to a Kafka topic named locations.

A small Kafka consumer script reads these messages and inserts them into a WAL-enabled QuestDB table.

Table Schema Example:

CREATE TABLE locations (
    id INT,
    latitude DOUBLE,
    longitude DOUBLE,
    timestamp TIMESTAMP
) timestamp(timestamp)
PARTITION BY DAY
WAL;
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This single WAL; line at the end turns your table into a write-ahead log table.

Location data are now safely stored in the WAL before being committed — ensuring zero data loss, even if your ingestion script restarts or your server crashes.

Monitoring and Debugging WAL Tables

wal tables debugging

  • Inspect all WAL tables and their ingestion state: 
SELECT * FROM wal_tables();
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This helps you confirm ingestion is live, and no logs are stuck uncommitted — a key part of real-time reliability monitoring.

  • To resume suspended WAL tables: 
ALTER TABLE table_name RESUME WAL;
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  • Check Writer Transactions, ensure writerTxn < sequencerTxn 
ALTER TABLE table_name RESUME WAL FROM TXN writerTxn;
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These quick checks can help you unstick ingestion pipelines without losing data integrity.

Why It Matters

In a realtime system:

  • Kafka ensures scalable pub/sub messaging.
  • QuestDB with WAL ensures fault-tolerant storage.

Together, they deliver speed + reliability — a reliable realtime data systems.

In summary

  • wal_tables() lists all QuestDB tables using Write-Ahead Logging
  • WAL = durability + parallel ingestion + easy recovery
  • a reliable real-time systems like location tracking with Kafka ingestion

References

“Real-time systems aren’t just about speed — they’re about trusting every update you receive.”

What’s your go-to tech stack for handling real-time data ingestion?

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