This error occurs when SQLite cannot allocate the memory needed to complete an operation. It indicates an internal call to malloc() or realloc() has failed, preventing SQLite from continuing the requested operation.
The SQLITE_NOMEM error code indicates that SQLite was unable to allocate all the memory it needed to complete the operation. More specifically, an internal call to sqlite3_malloc() or sqlite3_realloc() has failed in a case where the memory being allocated was required in order to continue the operation. This is one of SQLite's primary result codes that signals a critical resource allocation failure. Unlike soft memory pressure situations where SQLite might free cache pages and retry, SQLITE_NOMEM represents a hard failure where the requested memory simply cannot be obtained from the system. The error can occur during any operation that requires memory allocation, including query execution, index creation, transaction processing, or even simple SELECT statements on large datasets. SQLite uses dynamic memory allocation extensively for query planning, temporary storage, sorting operations, and maintaining internal data structures.
Ensure all database connections and prepared statements are properly closed. Resource leaks are the most common cause of SQLITE_NOMEM errors.
For JavaScript/Node.js:
const sqlite3 = require('sqlite3');
const db = new sqlite3.Database('mydb.sqlite');
try {
// Your database operations
db.all('SELECT * FROM users', (err, rows) => {
if (err) console.error(err);
// Process rows
});
} finally {
// Always close the database
db.close((err) => {
if (err) console.error('Error closing database:', err);
});
}For Python:
import sqlite3
conn = sqlite3.connect('mydb.sqlite')
try:
cursor = conn.cursor()
cursor.execute('SELECT * FROM users')
rows = cursor.fetchall()
# Process rows
finally:
cursor.close()
conn.close()For C/C++:
sqlite3 *db;
sqlite3_stmt *stmt;
// Always finalize statements and close connections
if (stmt) sqlite3_finalize(stmt);
if (db) sqlite3_close(db);For CREATE INDEX or large sorting operations, temporarily reduce the cache_size setting to decrease memory requirements.
-- Check current cache size
PRAGMA cache_size;
-- Reduce cache size before creating index
PRAGMA cache_size = 2000; -- Default is usually -2000 (2MB)
-- Create your index
CREATE INDEX idx_users_email ON users(email);
-- Restore original cache size if needed
PRAGMA cache_size = -2000;The cache_size pragma accepts either a positive number (pages) or negative number (KB). For example, PRAGMA cache_size = -4000 sets a 4MB cache.
Instead of loading entire result sets into memory, use pagination with LIMIT and OFFSET to process data in manageable chunks.
const PAGE_SIZE = 1000;
let offset = 0;
let hasMore = true;
while (hasMore) {
const rows = db.prepare(
'SELECT * FROM large_table LIMIT ? OFFSET ?'
).all(PAGE_SIZE, offset);
if (rows.length === 0) {
hasMore = false;
} else {
// Process this chunk
processRows(rows);
offset += PAGE_SIZE;
}
}This approach prevents loading millions of rows into memory simultaneously.
Set appropriate soft and hard heap limits for your application's needs. Be aware that overly restrictive limits can trigger SQLITE_NOMEM errors.
Soft heap limit (advisory, SQLite tries to stay under but will exceed if necessary):
// Set soft heap limit to 64MB
sqlite3_soft_heap_limit64(64 * 1024 * 1024);Hard heap limit (strict, allocations fail when reached):
// Set hard heap limit to 128MB
sqlite3_hard_heap_limit64(128 * 1024 * 1024);For in-memory databases, configure max size:
// Before sqlite3_initialize()
sqlite3_config(SQLITE_CONFIG_MEMDB_MAXSIZE, 2147483648LL); // 2GBRemove or increase limits if they're causing legitimate operations to fail.
For large binary or text data, consider alternative storage strategies to reduce memory pressure.
Option 1: Store large files externally
-- Instead of storing the entire file:
-- CREATE TABLE documents (id INTEGER, content BLOB);
-- Store only the file path:
CREATE TABLE documents (
id INTEGER PRIMARY KEY,
file_path TEXT NOT NULL,
file_size INTEGER,
content_hash TEXT
);Option 2: Use compression for TEXT fields
import sqlite3
import zlib
conn = sqlite3.connect('mydb.sqlite')
cursor = conn.cursor()
# Compress before storing
large_text = "..." * 10000
compressed = zlib.compress(large_text.encode())
cursor.execute('INSERT INTO logs (data) VALUES (?)', (compressed,))
# Decompress when reading
cursor.execute('SELECT data FROM logs WHERE id = ?', (1,))
compressed_data = cursor.fetchone()[0]
original_text = zlib.decompress(compressed_data).decode()Option 3: Use streaming for large BLOBs
// Incremental BLOB I/O
sqlite3_blob *blob;
sqlite3_blob_open(db, "main", "images", "data", rowid, 0, &blob);
// Read in chunks
char buffer[4096];
int offset = 0;
while (offset < sqlite3_blob_bytes(blob)) {
sqlite3_blob_read(blob, buffer, sizeof(buffer), offset);
// Process chunk
offset += sizeof(buffer);
}
sqlite3_blob_close(blob);If experiencing memory allocation failures with FTS4 full-text search, migrate to FTS5 which has significantly improved memory management.
FTS4 (problematic with large datasets):
CREATE VIRTUAL TABLE documents_fts4 USING fts4(title, content);FTS5 (improved memory handling):
CREATE VIRTUAL TABLE documents_fts5 USING fts5(title, content);Migration script:
-- Create new FTS5 table
CREATE VIRTUAL TABLE documents_fts5 USING fts5(title, content);
-- Copy data in batches to avoid memory issues
INSERT INTO documents_fts5(title, content)
SELECT title, content FROM documents_fts4 LIMIT 1000 OFFSET 0;
-- Repeat with increasing offsets until all data is migrated
-- Drop old table
DROP TABLE documents_fts4;FTS5 is more efficient with memory allocation during bulk inserts and indexing operations.
Ensure you're using the latest stable version of SQLite, as newer versions include memory management improvements and bug fixes.
Check current version:
SELECT sqlite_version();For Node.js (better-sqlite3):
npm update better-sqlite3For Python:
pip install --upgrade pysqlite3-binaryFor system-wide SQLite (Linux):
# Ubuntu/Debian
sudo apt update
sudo apt install --only-upgrade sqlite3
# Download and compile latest version
wget https://www.sqlite.org/2025/sqlite-autoconf-XXXXXXX.tar.gz
tar xvfz sqlite-autoconf-XXXXXXX.tar.gz
cd sqlite-autoconf-XXXXXXX
./configure
make
sudo make installReview the SQLite release notes for memory-related improvements in recent versions.
Memory Allocation Architecture: SQLite uses a pluggable memory allocator system. By default, it uses the system's malloc()/free(), but you can provide custom allocators via sqlite3_config(SQLITE_CONFIG_MALLOC, ...). This is useful in embedded systems or when you need precise control over memory allocation behavior.
Page Cache vs General Heap: SQLite distinguishes between page cache memory (configured via SQLITE_CONFIG_PAGECACHE) and general-purpose heap allocations. The page cache is used for database pages, while general heap handles query planning, temporary tables, and other operations. SQLITE_NOMEM can occur in either subsystem.
Temporary Storage: Complex queries may create temporary B-trees, temporary tables, or use the temp_store setting. By default, temp_store=FILE writes to disk, but temp_store=MEMORY keeps everything in RAM, which can exhaust memory faster. Check your temp_store setting:
PRAGMA temp_store; -- 0=default, 1=file, 2=memory
PRAGMA temp_store = FILE; -- Use disk for temp storageTransaction Memory: Long-running transactions hold locks and may accumulate changes in memory (especially with large batch inserts). Use smaller transaction batches to reduce memory pressure:
BEGIN TRANSACTION;
-- Insert 1000 rows at a time instead of 100,000
COMMIT;Soft vs Hard Limits: The soft heap limit (sqlite3_soft_heap_limit64) is advisory—SQLite will try to stay under it but will exceed it rather than fail. The hard heap limit (sqlite3_hard_heap_limit64) is strict—allocations fail when reached. In production, use soft limits for normal operation and hard limits only in severely constrained environments.
Memory-Mapped I/O: SQLite supports memory-mapped I/O via the mmap_size pragma, which can reduce memory pressure by mapping database files directly into virtual memory:
PRAGMA mmap_size = 268435456; -- 256MB mmapHowever, this isn't suitable for all workloads and may not help with SQLITE_NOMEM if the issue is query-related rather than I/O-related.
Debugging Memory Issues: Enable SQLite's memory allocation logging by compiling with SQLITE_DEBUG and SQLITE_MEMDEBUG flags, or use tools like Valgrind to detect memory leaks in your application code. The sqlite3_status64() function can report current memory usage statistics.
SQLITE_CORRUPT_VTAB: Content in virtual table is corrupt
Content in virtual table is corrupt
SQLITE_IOERR_WRITE: Disk I/O error during write
Disk I/O error during write operation
SQLITE_READONLY: Attempt to write a readonly database
How to fix "SQLITE_READONLY: Attempt to write a readonly database" in SQLite
SQLITE_CONSTRAINT_PRIMARYKEY: PRIMARY KEY constraint failed
How to fix "SQLITE_CONSTRAINT_PRIMARYKEY" in SQLite
SQLITE_READONLY_DBMOVED: Database file has been moved since opened
How to fix 'SQLITE_READONLY_DBMOVED: Database file has been moved since opened'