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Telemetry
Integration guide

Prisma ORM Database Telemetry

Measure Prisma operation fingerprints, model and method latency, failures, result counts, releases, and database-dependent workflows without collecting raw query parameters.

Reviewed by the Telemetry product team on . Instrumentation contract, privacy boundaries, and implementation guidance. Review standards and ownership

Useful for
  • Prisma operation latency
  • Release regression analysis
  • Database-dependent workflow debugging
Implementation evidence

Prisma ORM Database Telemetry: from boundary to verified row

Use Prisma ORM Database Telemetry at a controlled application boundary, keep the event contract small, and verify a known outcome before building aggregate views.

  1. 1

    Choose the outcome

    Prisma operation latency

  2. 2

    Define the contract

    query_fingerprint, prisma_model, prisma_operation, and workflow

  3. 3

    Instrument the boundary

    Wrap product-level Prisma calls with an explicit fingerprint such as User.findMany.active instead of forwarding emitted SQL.

  4. 4

    Verify the evidence

    Exercise a known fixture, then inspect database_query_completed for one correctly typed terminal row.

Before you start

Prerequisites and boundaries

  • Prisma Client initialized on the server
  • Stable operation names chosen at application call sites
  • A server-side TELEMETRY_API_KEY

Delivery setup

Install and initialize server-side

Import telemetry-sh in server-only code and initialize it once with process.env.TELEMETRY_API_KEY. Keep ingestion credentials out of browser bundles, client-visible environment variables, source control, logs, and exception messages.

prisma-install

npm installation

bash
npm install telemetry-sh
  1. 1Prepare one reusable server-side delivery client with bounded network behavior.
  2. 2Add the outcome event at the success, failure, retry, or timeout boundary.
  3. 3Send controlled fixtures and inspect the stored rows before enabling an alert.

Snippet

Start with one structured event

Add this shape where the workflow completes, fails, or retries. Then build the dashboard from real fields.

prisma

Prisma ORM Database Telemetry event

javascript
import telemetry from "telemetry-sh";

const SAFE_DATABASE_ERROR_CODES = new Set(["P2024", "P2034"]);

function classifyDatabaseError(error) {
  return error && typeof error === "object" &&
    "code" in error &&
    SAFE_DATABASE_ERROR_CODES.has(String(error.code))
    ? String(error.code)
    : "database_error";
}

async function observePrismaOperation({
  queryFingerprint,
  model,
  operation,
  workflow,
  run,
}) {
  const startedAt = performance.now();
  try {
    const result = await run();
    await telemetry.log("database_query_completed", {
      query_fingerprint: queryFingerprint,
      prisma_model: model,
      prisma_operation: operation,
      workflow,
      database_name: "app_production",
      status: "success",
      duration_ms: Math.round(performance.now() - startedAt),
      rows_returned: Array.isArray(result) ? result.length : 1,
      release: process.env.APP_RELEASE,
      environment: process.env.NODE_ENV,
    });
    return result;
  } catch (error) {
    await telemetry.log("database_query_completed", {
      query_fingerprint: queryFingerprint,
      prisma_model: model,
      prisma_operation: operation,
      workflow,
      database_name: "app_production",
      status: "failed",
      duration_ms: Math.round(performance.now() - startedAt),
      error_type: classifyDatabaseError(error),
      release: process.env.APP_RELEASE,
      environment: process.env.NODE_ENV,
    });
    throw error;
  }
}

const users = await observePrismaOperation({
  queryFingerprint: "User.findMany.active",
  model: "User",
  operation: "findMany",
  workflow: "account_directory",
  run: () => prisma.user.findMany({ where: { active: true } }),
});

Event contract

query_fingerprint, prisma_model, prisma_operation, and workflow

status, duration_ms, rows_returned, controlled error_type, and account_id

database_name, release, environment, and safe correlation identifiers

Implementation checkpoints

Checkpoint 1

Wrap product-level Prisma calls with an explicit fingerprint such as User.findMany.active instead of forwarding emitted SQL.

Checkpoint 2

Keep model, operation, and workflow separate so a database regression can be connected to customer-visible behavior.

Checkpoint 3

Prisma query events expose SQL and parameters; do not send either field to Telemetry.

Verification

Prove the event arrived

Run this after exercising known success and failure cases. Replace the fallback table name if your final event contract differs from the snippet.

prisma-verification

Prisma ORM Database Telemetry verification query

sql
SELECT *
FROM database_query_completed
ORDER BY timestamp_utc DESC
LIMIT 20;
Confirm one terminal row per logical outcome, with the expected status, identifiers, units, and UTC time.
Inspect the inferred schema and verify that retries do not change field types or generate a new logical event ID.
Search the stored fields for credentials, raw payloads, prompts, private content, and unbounded error messages.
Exercise a provider timeout, ingestion rejection, and process shutdown before treating the dashboard as complete.

Implementation references

Review the event contract, data-safety guidance, and upstream primary documentation before enabling a new production path.

Production boundary

Keep the outcome event small and recoverable

This pattern provides

  • A bounded, SQL-ready outcome beside the upstream workflow.
  • Stable fields for dashboards, alerts, and cross-event correlation.
  • A fixture-driven path for validating success, failure, retry, and timeout behavior.

This pattern does not provide

  • An OTLP exporter, automatic collection pipeline, or replacement for detailed traces and diagnostic logs.
  • Exactly-once delivery merely because the payload contains an event ID.
  • Permission to collect raw provider payloads, user content, credentials, or regulated data.

Event schema starting points

Review the row grain, emit boundary, required types, privacy classes, example payload, and validation checklist before adapting a query or snippet to production.

Related product capability

Continue this workflow in SQL query API

Run read-only DataFusion SQL over structured-event tables and reuse the result.

Related SQL recipes

Answer the next question with SQL

Run the query against the structured fields from this workflow, inspect the example result, and turn a useful answer into a dashboard or alert.

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