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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallA read replica gives you more places to run reads. It does not make any single read cheaper. If a query scans far more data than it should, picks a poor join order, or relies on misleading planner estimates, it can do the same wasteful work on a replica. The difference is per-query efficiency versus workload capacity, and the two need different fixes.
What a replica changes and what it leaves alone
AWS describes routing application reads to Amazon RDS read replicas as a way to reduce load on the source database and scale read-heavy workloads. Its feature comparison names scalability as the main purpose of read replicas, and says replication is asynchronous for non-Aurora read replicas. That is a capacity claim, and it only applies to reads your application actually sends to the replica.
A replica does not, by itself:
- rewrite the SQL or reduce the work one execution needs;
- create the index the query needs;
- improve stale or inadequate planner statistics;
- change an inefficient access path.
One caution runs the other way: don’t assume the plan on the replica is identical to the plan on the primary. Engine, statistics, configuration and service architecture all matter, so capture the plan on the instance that actually serves the query.
How the planner fits in
The PostgreSQL 17 documentation (section 14.1, “Using EXPLAIN”) puts it plainly: “PostgreSQL devises a query plan for each query it receives.” EXPLAIN shows that plan as a tree. It has scan nodes at the bottom and, where needed, join, aggregation, sort or other nodes above them. If the tree is wasteful, moving the query to another server changes where the waste happens, not whether it happens.
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Diagnose before you add capacity
1. Pin down the statement and where it runs
Identify the exact slow statement, its parameter values, how often it runs, its concurrency, and the instance that serves it. Write traffic is a separate workload that a replica does not absorb.
2. Capture the plan on representative data
EXPLAIN SELECT ...;
EXPLAIN ANALYZE SELECT ...;EXPLAIN ANALYZE executes the statement and adds observed row counts and timings. Three limits apply, per the PostgreSQL documentation:
- it does not send result rows to the client, so its time is not end-to-end application latency;
- measurement can add its own overhead;
- estimates vary with sampled statistics and platform conditions.
Because it runs the query, use it with care on statements that modify data or are very expensive.
3. Read the tree from the scans upward
- Estimated versus actual rows. A large gap usually means the planner is choosing on bad information.
- Scan type. A sequential scan is not inherently bad. PostgreSQL notes that on a small table it can be the sensible choice even when indexes exist. It is suspect when the table is large and the predicate is selective.
- Join, sort and aggregation work. Check that it matches the shape of what the query is meant to return.
4. Check statistics and index usability
Ask whether statistics reflect the current data, and whether the query’s predicates and joins can use existing indexes. Don’t add an index blindly. Its value depends on the query, the data distribution, the write cost it imposes and the competing workload.
5. Change one thing and compare
Compare plan and latency before and after any SQL, statistics, schema, index, configuration or version change. Only once the query is reasonably efficient and the remaining problem is read concurrency should you test replica capacity. Then measure both response time and lag, and state your read-after-write and freshness requirements explicitly.
Replica lag is a separate problem
Freshness and plan quality are independent. AWS’s RDS for PostgreSQL documentation describes native PostgreSQL replication to read-only replicas. It also notes that the reported lag value can climb to five minutes when no transactions run on the source, because the default WAL segment switch interval is five minutes. That is documented reporting behavior for that product, not a guarantee of how stale your data really is.
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Aurora works differently. Aurora replicas share a cluster volume with the writer, and ReplicaLag refers to the reader’s page-cache lag relative to the writer. AWS describes it as usually much less than 100 milliseconds. Treat that as a description, not a promise, because workload and write rate affect it.
Choosing among the real options
| Option | Use it when | Compare on |
|---|---|---|
| Query, statistics, schema or index changes | The plan shows excess work in this statement | Actual vs. estimated rows, latency, write overhead, storage, effect on other statements |
| Read replicas | The limit is aggregate read throughput or contention on the source | Capacity gained, routing and application changes, lag, freshness tolerance, operating cost |
| Plan stability controls | A plan regressed after a change such as new statistics or a version upgrade | Control gained vs. the feature’s maintenance and version constraints |
| Larger instance or a different architecture | The plan is reasonably efficient but CPU, memory or I/O is the limit, or the workload suits another system | Workload-specific measurements; no universal threshold is established |
Replica count is not a measure of query efficiency. Ten replicas running a bad plan are ten places to run it.
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When plan regression is the actual problem
Sometimes a query was fine and then got worse. AWS defines plan regression as the optimizer choosing a less optimal plan after an environmental change, such as changed statistics or a PostgreSQL version change. Aurora PostgreSQL query plan management can constrain the optimizer to a set of known plans. It is a proprietary Aurora capability with its own supported statements and configuration requirements, so check the current AWS documentation before relying on it. It does not apply to vanilla PostgreSQL or other vendors.
A practical rule
If one execution is slow, fix the plan. If many acceptable executions together overload the source, add read capacity. If both are true, fix the plan first. A replica then multiplies efficient work instead of expensive work.
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