SQL Batching

SQL Batching Pattern

CRITICAL: D1 has a 100 bound parameter limit per query (NOT 999 like standard SQLite). See: https://developers.cloudflare.com/d1/platform/limits/

Use @/utils/batch-query.ts utilities to batch queries.

The 100 Parameter Limit

D1's limit is 100 bound parameters per query, not SQLite's typical 999. This affects:

• inArray() queries: each ID = 1 param
• Bulk inserts: each column value = 1 param (including auto-generated columns!)
• Combined queries: all WHERE conditions + array items must be < 100

Error message: D1_ERROR: too many SQL variables at offset N: SQLITE_ERROR

When to Batch

Always batch when:

• Using inArray() with arrays from user data or prior queries
• Array size is unbounded (team memberships, registrations, results)
• Bulk inserts with dynamic row counts
• Any operation where total params could exceed 100

Safe to skip when:

• Array is hardcoded/constant AND small (< 50 items to leave headroom)
• Single-row inserts
• Array has guaranteed small upper bound AND you've calculated total params

Usage

import { autochunk, autochunkFirst } from "@/utils/batch-query"

// findMany - returns flattened results
const results = await autochunk(
  { items: ids, otherParametersCount: 1 }, // count other WHERE params
  async (chunk) => db.query.table.findMany({
    where: and(
      eq(table.field, value), // this counts as 1 param
      inArray(table.id, chunk),
    ),
  }),
)

// findFirst - stops on first match
const result = await autochunkFirst(
  { items: ids },
  async (chunk) => db.query.table.findFirst({
    where: inArray(table.id, chunk),
  }),
)

For Parallel Execution

Use chunk() + Promise.all when parallel is preferred:

import { chunk, SQL_BATCH_SIZE } from "@/utils/batch-query"

const results = (await Promise.all(
  chunk(ids, SQL_BATCH_SIZE).map((batch) =>
    db.select().from(table).where(inArray(table.id, batch))
  )
)).flat()

Parameters

• items: Array to batch (IDs, objects)
• otherParametersCount: Number of other bound params in query (eq conditions, etc.)
• SQL_BATCH_SIZE: 100 (D1's actual limit)

For Bulk Inserts

For bulk inserts, Drizzle includes ALL columns (including auto-generated ones). You MUST count every column in the table schema, not just the fields you're explicitly setting.

Counting Columns

Check the table schema and count:

1. commonColumns (if used): createdAt, updatedAt, updateCounter = 3 columns
2. All explicit columns: id, fieldA, fieldB, etc.
3. Even nullable columns without defaults get a param (Drizzle sends null)

Calculate Batch Size

// Formula: floor(100 / totalColumns)
// Always subtract 1-2 for safety margin

// Example: judgeHeatAssignmentsTable has 12 columns
// commonColumns (3) + id, heatId, membershipId, rotationId, versionId, 
// laneNumber, position, instructions, isManualOverride (9) = 12 total
// Max rows: floor(100 / 12) = 8 rows
const INSERT_BATCH_SIZE = 8

const chunks: Item[][] = []
for (let i = 0; i < items.length; i += INSERT_BATCH_SIZE) {
  chunks.push(items.slice(i, i + INSERT_BATCH_SIZE))
}

for (const chunk of chunks) {
  await db.insert(table).values(chunk.map(item => ({ ... })))
}

Real-World Examples from Codebase

Common Gotchas

1. Don't trust old comments: Previous code assumed 999 limit - always verify
2. Nullable columns still count: Even if you don't set them, Drizzle may send null
3. Auto-generated columns count: $defaultFn() columns still use a param slot
4. The error is cryptic: too many SQL variables at offset N means you hit 100

Debugging

If you hit the limit, count params in the SQL output:

Query: insert into "table" ("col1", "col2", ...) values (?, ?, ...), (?, ?, ...)

Count the ? marks - that's your actual param count.