INSERT

The INSERT statement adds rows to Cassandra tables. Unlike SQL databases, Cassandra's INSERT performs an upsert operation—inserting a new row if the primary key doesn't exist, or updating the existing row if it does.

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Behavioral Guarantees

What INSERT Guarantees

• INSERT performs an upsert—no "duplicate key" errors occur
• Each column value is written atomically
• Higher timestamps win in conflict resolution
• Data with TTL becomes invisible after the specified duration
• Write is acknowledged by the specified number of replicas

What INSERT Does NOT Guarantee

Undefined Behavior

The following behaviors are undefined and must not be relied upon:

• Write visibility timing: After INSERT returns, the data may not immediately be visible to reads at lower consistency levels
• Cross-column atomicity: Columns in a single INSERT are written as separate cells; partial visibility is possible during read
• Order of concurrent writes: Two INSERTs with the same timestamp have undefined conflict resolution (implementation-dependent tie-breaker)
• Exactly-once without LWT: Without IF NOT EXISTS, the same INSERT may be applied multiple times (idempotent, but no uniqueness)

Failure Semantics

Failure Mode	Outcome	Client Action
WriteTimeoutException	Undefined - may or may not have been applied	Query to verify, retry if needed
UnavailableException	Not applied	Safe to retry
WriteFailureException	Partially applied to some replicas	Data may be inconsistent; repair may be needed

Version-Specific Behavior

Version	Behavior
2.0+	IF NOT EXISTS (LWT) supported (CASSANDRA-5062)
2.1+	JSON INSERT syntax supported (CASSANDRA-7970)
3.0+	DEFAULT UNSET for JSON inserts (CASSANDRA-9651)

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Overview

Upsert Semantics

Cassandra does not distinguish between INSERT and UPDATE at the storage layer. Both operations write cells (column values) with timestamps:

Key implications:

• No "duplicate key" errors—second INSERT simply overwrites
• No read-before-write needed to check existence
• Conflict resolution based solely on timestamp

INSERT vs UPDATE

Aspect	INSERT	UPDATE
Syntax	Specifies column list and VALUES	Uses SET assignments
Behavior	Upsert	Upsert
Null handling	Missing columns unchanged	Explicit null creates tombstone
Storage	Identical	Identical

Functional Equivalence

These statements produce identical storage:

INSERT INTO users (id, name) VALUES (1, 'Alice');
UPDATE users SET name = 'Alice' WHERE id = 1;

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Synopsis

INSERT INTO [ *keyspace_name*. ] *table_name*
    ( *column_name* [, *column_name* ... ] )
    VALUES ( *term* [, *term* ... ] )
    [ IF NOT EXISTS ]
    [ USING *update_parameter* [ AND *update_parameter* ... ] ]

JSON syntax:

INSERT INTO [ *keyspace_name*. ] *table_name*
    JSON '*json_string*'
    [ DEFAULT ( NULL | UNSET ) ]
    [ IF NOT EXISTS ]
    [ USING *update_parameter* [ AND *update_parameter* ... ] ]

update_parameter:

TTL *seconds*
| TIMESTAMP *microseconds*

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Parameters

Column List and VALUES

All primary key columns must be specified. Non-primary key columns are optional:

-- Primary key columns required
INSERT INTO users (user_id, email, name)
VALUES (uuid(), '[email protected]', 'Alice');

-- Partial insert (only primary key + some columns)
INSERT INTO users (user_id, email)
VALUES (uuid(), '[email protected]');
-- name column remains null (or unchanged if row exists)

IF NOT EXISTS

Converts INSERT to a lightweight transaction using Paxos consensus:

INSERT INTO users (user_id, username, email)
VALUES (uuid(), 'alice', '[email protected]')
IF NOT EXISTS;

Result when applied:

 [applied]
-----------
      True

Result when not applied (row exists):

 [applied] | user_id                              | username | email
-----------+--------------------------------------+----------+------------------
     False | 550e8400-e29b-41d4-a716-446655440000 | alice    | [email protected]

LWT Performance

IF NOT EXISTS uses Paxos consensus, adding ~4x latency compared to regular inserts. Use sparingly and only when uniqueness is critical.

USING TTL

Sets Time-To-Live in seconds for non-primary-key columns:

-- Session expires in 24 hours
INSERT INTO sessions (session_id, user_id, token)
VALUES (uuid(), 123, 'abc123')
USING TTL 86400;

TTL behavior:

• Applied to non-primary-key columns only
• Primary key columns cannot expire (would leave orphaned row marker)
• TTL countdown starts at write time
• Expired data becomes tombstone until gc_grace_seconds passes

-- Check remaining TTL
SELECT session_id, TTL(token) FROM sessions WHERE session_id = ?;

USING TIMESTAMP

Specifies the write timestamp in microseconds since Unix epoch:

INSERT INTO users (user_id, name)
VALUES (123, 'Alice')
USING TIMESTAMP 1705315800000000;

Use cases:

• Replaying events from external systems
• Ensuring specific conflict resolution order
• Data migration with preserved timestamps

Timestamp Pitfalls

• Future timestamps can make data "invisible" until that time
• Past timestamps may be ignored if newer data exists
• Clock skew between application servers causes inconsistencies

USING TTL AND TIMESTAMP

Both can be combined:

INSERT INTO cache (key, value)
VALUES ('user:123', 'cached_data')
USING TTL 3600 AND TIMESTAMP 1705315800000000;

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JSON INSERT

Insert rows using JSON object syntax:

INSERT INTO users JSON '{
    "user_id": "550e8400-e29b-41d4-a716-446655440000",
    "username": "alice",
    "email": "[email protected]",
    "created_at": "2024-01-15T10:30:00.000Z"
}';

JSON Type Mapping

CQL Type	JSON Representation
TEXT, VARCHAR	"string"
INT, BIGINT	123
FLOAT, DOUBLE	1.23
BOOLEAN	true / false
UUID, TIMEUUID	"550e8400-e29b-41d4-a716-446655440000"
TIMESTAMP	"2024-01-15T10:30:00.000Z"
DATE	"2024-01-15"
TIME	"10:30:00.000000000"
BLOB	"0x48656c6c6f" (hex)
LIST<T>	[value1, value2]
SET<T>	[value1, value2]
MAP<K,V>	{"key": "value"}
TUPLE<...>	[value1, value2]
UDT	{"field1": "value1"}

DEFAULT NULL vs UNSET

Controls handling of missing JSON keys:

-- DEFAULT NULL: missing keys become null (creates tombstones)
INSERT INTO users JSON '{"user_id": "...", "username": "alice"}';
-- email column set to null (tombstone if row existed)

-- DEFAULT UNSET: missing keys unchanged
INSERT INTO users JSON '{"user_id": "...", "username": "alice"}'
DEFAULT UNSET;
-- email column unchanged (no tombstone)

Prefer DEFAULT UNSET

For partial updates via JSON, use DEFAULT UNSET to avoid creating tombstones for unchanged columns.

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Collection Inserts

Lists

-- Insert list
INSERT INTO users (user_id, phone_numbers)
VALUES (uuid(), ['+1-555-0100', '+1-555-0101']);

-- Empty list
INSERT INTO users (user_id, phone_numbers)
VALUES (uuid(), []);

Sets

-- Insert set
INSERT INTO users (user_id, roles)
VALUES (uuid(), {'admin', 'user', 'moderator'});

-- Empty set
INSERT INTO users (user_id, roles)
VALUES (uuid(), {});

Maps

-- Insert map
INSERT INTO users (user_id, preferences)
VALUES (uuid(), {
    'theme': 'dark',
    'language': 'en',
    'timezone': 'UTC'
});

-- Empty map
INSERT INTO users (user_id, preferences)
VALUES (uuid(), {});

Frozen Collections

Frozen collections are stored as single blob values:

-- Table with frozen collection
CREATE TABLE events (
    event_id UUID PRIMARY KEY,
    tags FROZEN<SET<TEXT>>
);

-- Insert (entire collection replaced atomically)
INSERT INTO events (event_id, tags)
VALUES (uuid(), {'important', 'urgent', 'system'});

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User-Defined Types

Insert UDT values using object literal syntax:

-- Define UDT
CREATE TYPE address (
    street TEXT,
    city TEXT,
    zip TEXT
);

-- Insert frozen UDT
INSERT INTO users (user_id, home_address)
VALUES (uuid(), {
    street: '123 Main St',
    city: 'Boston',
    zip: '02101'
});

For non-frozen UDTs (rare), fields can be individually updated:

-- Insert with null fields
INSERT INTO users (user_id, profile)
VALUES (uuid(), {name: 'Alice', bio: null});

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Null Values and UNSET

Null Creates Tombstones

Explicitly inserting null creates a tombstone:

-- Creates tombstone for email column
INSERT INTO users (user_id, username, email)
VALUES (uuid(), 'alice', null);

UNSET Avoids Tombstones

In prepared statements, use UNSET to leave columns unchanged:

// Java driver example
PreparedStatement ps = session.prepare(
    "INSERT INTO users (user_id, username, email) VALUES (?, ?, ?)");

// UNSET leaves email unchanged (no tombstone)
session.execute(ps.bind(userId, "alice", UNSET));

Tombstone Accumulation

Repeated inserts with null values accumulate tombstones, degrading read performance. Design applications to avoid null inserts:

• Use UNSET in prepared statements
• Use DEFAULT UNSET for JSON inserts
• Omit columns that don't need updating

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Restrictions

Restrictions

Primary Key:

• All primary key columns must be specified
• Primary key values cannot be null
• Primary key columns cannot have TTL

Collections:

• Cannot INSERT null into collection (use empty collection instead)
• Collection elements must match declared type
• Frozen collections replaced entirely (no partial updates)

Conditional Inserts:

• IF NOT EXISTS cannot be combined with USING TIMESTAMP
• IF NOT EXISTS adds significant latency (Paxos)
• Cannot use IF condition (only IF NOT EXISTS for INSERT)

General:

• Counter columns cannot be inserted (must use UPDATE with increment)
• Cannot insert computed/derived values directly

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Performance Considerations

Prepared Statements

Always use prepared statements for repeated inserts:

// Prepare once
PreparedStatement ps = session.prepare(
    "INSERT INTO users (user_id, username, email) VALUES (?, ?, ?)");

// Execute many times
for (User user : users) {
    session.execute(ps.bind(user.getId(), user.getName(), user.getEmail()));
}

Benefits:

• Query parsed once, reused many times
• Reduced network overhead (only send values)
• Type validation at prepare time

Batching Considerations

For multiple inserts to the same partition, use unlogged batches:

BEGIN UNLOGGED BATCH
    INSERT INTO user_events (user_id, event_id, type) VALUES (123, uuid(), 'login');
    INSERT INTO user_events (user_id, event_id, type) VALUES (123, uuid(), 'page_view');
    INSERT INTO user_events (user_id, event_id, type) VALUES (123, uuid(), 'click');
APPLY BATCH;

Batch Anti-Pattern

Do not use batches to "improve performance" for inserts to different partitions. This adds coordinator overhead without benefit. See BATCH for details.

Async Inserts

For high throughput, use asynchronous execution:

// Java driver async example
List<CompletionStage<AsyncResultSet>> futures = new ArrayList<>();
for (User user : users) {
    futures.add(session.executeAsync(ps.bind(user.getId(), user.getName())));
}
// Wait for all to complete
CompletableFuture.allOf(futures.toArray(new CompletableFuture[0])).join();

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Examples

Basic Insert

INSERT INTO users (user_id, username, email, created_at)
VALUES (
    uuid(),
    'johndoe',
    '[email protected]',
    toTimestamp(now())
);

Insert with TTL

-- Password reset token expires in 1 hour
INSERT INTO password_resets (token_id, user_id, created_at)
VALUES (uuid(), 123, toTimestamp(now()))
USING TTL 3600;

Conditional Insert (Unique Username)

INSERT INTO usernames (username, user_id)
VALUES ('desired_name', 123)
IF NOT EXISTS;

JSON Insert for API Data

INSERT INTO api_logs JSON '{
    "request_id": "550e8400-e29b-41d4-a716-446655440000",
    "timestamp": "2024-01-15T10:30:00.000Z",
    "method": "POST",
    "path": "/api/users",
    "status_code": 201,
    "response_time_ms": 45
}';

Insert with Collections

INSERT INTO products (
    product_id,
    name,
    tags,
    attributes,
    images
)
VALUES (
    uuid(),
    'Wireless Keyboard',
    {'electronics', 'computer', 'accessories'},
    {'color': 'black', 'connectivity': 'bluetooth', 'battery': 'AA'},
    ['front.jpg', 'side.jpg', 'back.jpg']
);

Insert with UDT

INSERT INTO orders (
    order_id,
    customer_id,
    shipping_address,
    billing_address
)
VALUES (
    uuid(),
    123,
    {street: '123 Main St', city: 'Boston', state: 'MA', zip: '02101'},
    {street: '456 Oak Ave', city: 'Cambridge', state: 'MA', zip: '02139'}
);

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Related Documentation

• UPDATE - Modifying existing data
• BATCH - Atomic multi-statement operations
• Lightweight Transactions - IF NOT EXISTS details
• Data Types - Type reference for INSERT values