Memory Leak in Adonisjs with Cockroachdb

A memory leak in an AdonisJS application using CockroachDB typically arises when query results or client/pool references are retained unintentionally, preventing garbage collection. Because CockroachDB drivers manage their own connection pooling and result streams, improper handling of query streams or ORM record sets can accumulate objects in memory over time. For example, iterating a large result set with Lucid models without disabling implicit persistence or without streaming rows can materialize the entire dataset in memory. If the application attaches query results to long-lived objects (e.g., module-level caches or request-scoped state that is never released), the heap grows with each request. This is especially risky under sustained load where many large queries execute concurrently.

AdonisJS’s default Lucid ORM may implicitly keep references to query rows in identity maps or eager-loaded relations if relationships are traversed without limits. CockroachDB’s wire protocol and transaction semantics can exacerbate this when multiple prepared statements or session-bound transactions are used without explicit cleanup. For instance, failing to finalize a transaction block or not closing a cursor can leave server-side cursors and client-side buffers alive. In a black-box scan, such leaks might not be directly visible, but they surface as steady increases in heap usage and, eventually, degraded performance. Memory-related findings often map to the Unsafe Consumption and Data Exposure checks in middleBrick, which highlight risky data handling patterns that can lead to exposure of sensitive information through memory inspection.

To detect this combination early, use middleBrick to scan your API endpoints. The tool runs 12 parallel checks, including Unsafe Consumption, and returns a security risk score with prioritized findings and remediation guidance. Because middleBrick requires no agents or credentials, you can validate that your AdonisJS endpoints do not exhibit obvious memory-sensitive behaviors without changing your deployment. Note that middleBrick detects and reports—it does not fix or block—so treat its output as actionable input for deeper profiling and code review.

Remediation focuses on releasing resources promptly, limiting result set sizes, and avoiding long-lived references to query results. Use streaming or pagination for large datasets, explicitly manage transactions, and ensure ORM queries release references after use. Below are concrete, realistic patterns for AdonisJS with CockroachDB.

1. Stream large result sets instead of loading all rows

Avoid await User.all() for tables with many rows. Instead, use a readable stream to process rows incrementally.

const { Builder } = require('@adonisjs/lucid')
const stream = await db.raw('SELECT id, name FROM users')
// stream is a node.js stream; consume it row by row to keep memory low
stream.on('row', (row) => {
  // process row without accumulating
  console.log(row.id, row.name)
})
stream.on('end', () => {
  // cleanup complete
})

2. Explicitly close transactions and release connections

Always ensure transactions are rolled back or committed and that client sessions are returned to the pool.

const trx = await db.transaction(async (trx) => {
  const user = await User.query().transacting(trx).where('id', 1).first()
  // perform operations
  await Account.query().transacting(trx).insert({ uid: user.id, balance: 0 })
  // commit is automatic if no exception; rollback on error is handled by the driver
})
// trx is released here; do not retain references to trx or its models

3. Disable implicit model persistence and limit eager loading

Prevent Lucid from caching models in the identity map when you do not need it, and avoid loading large relations by default.

const users = await User
  .query()
  .select('id', 'email')
  .withScopes((scopes) => {
    scopes.dontLoadRelations() // avoid accidental large joins
  })
  .limit(100)
  .timeout(10000)
  .forShare()
  .exec()
// users is a plain array; do not attach to global cache

4. Use pagination for user-facing endpoints

Serve data in pages and avoid deep offsets which can be expensive; prefer keyset pagination.

const page = 1
const limit = 50
const users = await User
  .query()
  .select('id', 'name')
  .orderBy('id', 'asc')
  .limit(limit)
  .offset((page - 1) * limit)
  .exec()
// send users to response and release

5. Monitor and profile in production

While middleBrick can highlight risky patterns via its Unsafe Consumption and Data Exposure checks, use runtime profiling (e.g., heap snapshots) alongside CockroachDB’s statement metrics to correlate query patterns with memory growth. Remediation guidance from such scans should prioritize fixing data handling before scaling infrastructure.