Taming ReorderBufferWrite - Boost Logical Decoding in Postgres
Performance bottlenecks in Postgres logical replication or Change Data Capture (CDC) stream can be subtle, but one specific wait event, ReorderBufferWrite, often points directly at how your application interacts with the database during these processes. Let's unpack this wait-event and see how your application's workload patterns can trigger it.
Core Concepts: Logical Decoding and WAL
To understand the problem, we first need some context:
Write-Ahead Log (WAL) is PostgreSQL's transaction log and every change is written here first (to ensure durability and enable recovery/replication)
Logical Decoding is a powerful feature that reads this WAL. Instead of just replaying physical changes (like physical replication), it translates the WAL records into a logical, easy-to-understand stream of changes, carefully taking care of rows and transactions, which is then consumed by logical replication subscribers or CDC systems.
The Reorder Buffer: Ensuring Transactional Order
Logical decoding needs to present changes in the exact order transactions were committed. However, changes from different concurrent transactions are interleaved within the WAL. To solve this, PostgreSQL uses an in-memory area called the Reorder Buffer. Its job is to collect decoded changes belonging to transactions that are still in progress. Only when a transaction commits are its changes released from the buffer in the correct sequence.
Analogy: The Assembly Line QC Station
For an analogy, imagine an assembly line where components (WAL entries) arrive continuously where workers assemble different products (transactions). Some products are quick builds, others complex multi-step assemblies. Completed products move to a Quality Control (QC) holding station (Reorder Buffer). The QC inspector (logical decoding process) ships out batches of products, but only when all products belonging to a specific batch (committed transaction) have passed inspection. The batches must be shipped in the strict order they were completed.
The Bottleneck: ReorderBufferWrite
The Reorder Buffer uses memory allocated by the logical_decoding_work_mem setting (often defaulting to 64MB). What happens if this QC holding station (Reorder Buffer) gets completely filled up with products waiting for their batch-mates (changes from active transactions), especially for those complex, slow builds (long-running transactions)?
The system can't just discard these waiting items and the best it can do is to move them temporarily to an overflow warehouse (spilling to disk). This action – writing the buffer's contents to slower disk storage – is precisely the ReorderBufferWrite wait event.
Waiting for disk I/O is significantly slower than operating in memory. Frequent ReorderBufferWrite events directly translate to increased replication lag and reduced throughput for the logical change stream. Retrieving items from the overflow warehouse (disk) drastically slows down the QC inspector's (logical decoding) shipping rate.
Workloads That Cause Spills to Disk
ReorderBufferWrite waits are primarily triggered by workload patterns that overwhelm the in-memory Reorder Buffer. Key culprits include:
Very Large Transactions: A single transaction modifying hundreds of thousands or millions of rows generates a massive volume of changes that must sit in the Reorder Buffer until the final commit. This can rapidly exhaust the available memory. Think of assembling a huge, complex product requiring many components – it occupies a lot of space at the QC station while being built.
Long-Running Transactions: A transaction might not change that much data, but if it stays open for a long time (perhaps due to complex calculations, waiting for external input, or slow queries within it), its changes linger in the Reorder Buffer. Meanwhile, other transactions complete, adding their changes. The buffer fills up with changes from many transactions, bottlenecked by the one(s) taking a long time to commit. This is like one single (inactive / idling) product assembly holding up the shipment of multiple completed batches at the QC station.
High Volume of Concurrent Changes: Even with reasonably sized and timed transactions, a very high rate of change across many concurrent sessions can collectively generate data faster than the logical decoding process can handle within the allocated Reorder Buffer memory, leading to spills. Imagine hundreds of small, quick products arriving at the QC station so fast it still gets overwhelmed.
Sudden workload spike: A related reasoning is when there is a sudden surge of application workload (writes) and they cause a rate of change higher than the logical decoding process can generally handle. In terms of workload pattern, to an experienced DBA, this may appear as:
connection spike => followed by replica-lag => and thereafter a slow recovery of that replica lag.
Insufficient logical_decoding_work_mem: If this setting is too low for your workload's typical peak demands, spills will occur more readily, even for moderately busy workloads.
What need be done?
As an application developer, you have significant control over the transaction patterns if ReorderBufferWrite waits are causing issues:
Audit & Split Large Transactions: Review bulk DML operations performed within a single transaction and refactor these into smaller batches. For e.g. Instead of one transaction for say 100k rows, use 10 transactions of 10k rows each. This drastically reduces the peak Reorder Buffer usage.
Minimize Transaction Duration: Identify code paths where transactions are held open unnecessarily long. This could be from end-user applications waiting for user-input, or doing non-database work, or calling slow external services inside a database transaction that modifies replicated tables. Try to keep database transactions short and focused. Perform reads, calculations, or external calls before starting the write transaction, or after it commits.
Optimize Queries Within Transactions: Slow SELECT, INSERT, UPDATE, or DELETE queries will naturally extend transaction duration. Use query analysis tools (EXPLAIN ANALYZE) to find and optimize slow queries involved in your transactions.
Isolate Read-Only Work: If long-running processes only read data, ensure they aren't unnecessarily starting read-write transactions on the primary where logical decoding is happening. Similarly use read-only transactions or more permissive isolation levels where possible.
Collaborate on Configuration: If application-side optimizations aren't sufficient, discuss the observed workload with your DBA or platform team. It would help to provide them with information about your transaction patterns. This could help them review whether an increase in logical_decoding_work_mem is warranted and safe within the server's overall resource constraints. Tuning memory should usually follow workload optimization.
Conclusion
ReorderBufferWrite isn't just an obscure PostgreSQL internal wait-event. It's a performance indicator directly linked to application workload patterns during logical decoding. By designing your application to use shorter, smaller, and more efficient transactions, especially on tables involved in replication, you can minimize these disk spill events and ensure a performant database.
