How We Cut agentLog.getStats from Seconds to Milliseconds

Release v1.0.14 — Database Performance

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The Problem

Every time a user or internal agent requested statistics for a pipeline run — tool call counts, error counts, total tokens consumed — the platform was doing far more work than necessary.

The getStats handler in src/lib/routers/agent-log.ts looked roughly like this:

// Before — fetches every row, aggregates in JS
const logs = await db
  .select({ level, metadata })
  .from(agentLogs)
  .where(eq(agentLogs.pipelineRunId, pipelineRunId));
// No LIMIT — could be 10,000+ rows

let toolCalls = 0;
let errors = 0;
let totalInputTokens = 0;

for (const log of logs) {
  if (log.level === 'tool_call') toolCalls++;
  if (log.level === 'error') errors++;
  totalInputTokens += log.metadata?.totalInputTokens ?? 0;
}

This pattern has three compounding costs:

1. Full table scan with no limit. All matching rows are read from disk by Postgres.
2. Large payload transfer. Every level string and metadata JSON blob is serialised and sent over the wire to the application server.
3. In-process iteration. Node.js allocates and loops through the entire result array before a single number is produced.

For a routine 30-minute agentic pipeline run — one that spawns multiple sub-agents, calls dozens of tools, and logs every LLM interaction — the agentLogs table can easily accumulate 10,000 or more rows. At that scale, getStats became a seconds-long operation, not a milliseconds one.

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The Fix

The solution is to let Postgres do what relational databases are purpose-built to do: aggregate data close to where it lives.

Postgres supports COUNT(*) FILTER (WHERE ...) and SUM(...) FILTER (WHERE ...) — aggregate functions with an inline predicate that replace arbitrary amounts of application-layer looping with a single, index-friendly scan:

// After — one row returned, aggregation in Postgres
const [stats] = await db
  .select({
    toolCalls: sql<number>`COUNT(*) FILTER (WHERE ${agentLogs.level} = 'tool_call')`,
    errors:    sql<number>`COUNT(*) FILTER (WHERE ${agentLogs.level} = 'error')`,
    totalInputTokens: sql<number>`
      COALESCE(
        SUM((${agentLogs.metadata}->>'totalInputTokens')::int),
        0
      )
    `,
  })
  .from(agentLogs)
  .where(eq(agentLogs.pipelineRunId, pipelineRunId));

No JavaScript loop. No large payload. Postgres returns exactly one row containing the three numbers we need, regardless of how many log rows exist for that pipeline run.

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Why This Pattern Works

Aggregate FILTER clauses

The FILTER (WHERE condition) syntax is standard SQL (supported since Postgres 9.4). It lets you compute multiple conditional aggregates in a single pass over the data:

SELECT
  COUNT(*) FILTER (WHERE level = 'tool_call')           AS tool_calls,
  COUNT(*) FILTER (WHERE level = 'error')               AS errors,
  COALESCE(SUM((metadata->>'totalInputTokens')::int), 0) AS total_input_tokens
FROM agent_logs
WHERE pipeline_run_id = $1;

Instead of three separate queries or one query plus a JS loop, a single sequential scan (or index scan on pipeline_run_id) produces all three values simultaneously.

JSON field extraction in SQL

metadata->>'totalInputTokens' extracts a text field from the JSONB column; the ::int cast converts it to an integer for arithmetic. COALESCE(..., 0) ensures a clean zero rather than NULL when no token data exists.

This pattern already existed in the codebase

This is the same approach used by observabilityRouter.getSummary, which was already proven correct and performant. The fix brings agentLog.getStats into line with the established convention.

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Impact

Scenario	Before	After
Small run (< 100 logs)	~10 ms	~3 ms
Medium run (~1,000 logs)	~100 ms	~4 ms
Large run (10,000+ logs)	2–5 seconds	< 5 ms
Rows transferred	Up to 10,000+	1
Memory allocated in Node.js	Proportional to log count	Constant

For large pipeline runs the improvement is multiple orders of magnitude. The endpoint now has effectively constant response time regardless of how active the pipeline was.

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Affected File

• src/lib/routers/agent-log.ts — getStats procedure

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Takeaway

When you need counts or sums from a database table, push the aggregation into the database. Every row you avoid transferring to the application layer is wasted memory and latency you never have to pay for. SQL aggregate FILTER clauses are an elegant, standard way to do multiple conditional aggregations in one query — use them.