Cassandra Query Language (CQL) for Astra DB

Because Astra DB Serverless is powered by Apache Cassandra®, you can send Cassandra Query Language (CQL) queries to your Astra DB Serverless databases with the CQL shell (cqlsh) or Cassandra drivers.

To use CQL for Astra DB, you need an active Astra account and an active Astra DB Serverless database.

Familiarity with CQL is helpful but not required.

For more information about CQL for Astra DB, including command examples and specifications, see the CQL for Astra DB reference.

CQL for Astra DB limitations

To ensure high availability and optimum performance, Astra DB Serverless databases have guardrails on underlying Apache Cassandra® functionality, including commands and functionality available through CQL for Astra DB.

User-defined types aren’t supported

CQL for Astra DB does not support user-defined functions (UDFs) or user-defined aggregate functions (UDAs).

Unsupported values are ignored

If you pass a CQL statement with unsupported data definition language (DDL) properties, the statement is executed without the unsupported values. Astra DB ignores the unsupported values and throws a warning.

For example, the following command creates a table with the given schema, and it attempts to pass an additional nodesync property:

sql

CREATE TABLE IF NOT EXISTS library.books
  (
    id UUID PRIMARY KEY,
    lastname text,
    firstname text)
  WITH nodesync={'enabled': 'true'};

If you execute this statement against an Astra DB database, the table is created but the nodesync property is ignored. The response includes a warning with the ignored unsupported values:

sql

Warnings :
Ignoring provided values [nodesync] as they are not supported for Table
Properties(ignored values are: [additional_write_policy,
bloom_filter_fp_chance, caching, cdc, compaction, compression,
crc_check_chance, dse_edge_label_property, dse_vertex_label_property,
gc_grace_seconds, id, max_index_interval, memtable,
memtable_flush_period_in_ms, min_index_interval, nodesync, read_repair,
speculative_retry])

Keyspace, table, and column names are case sensitive

If you create a table in the Astra Portal, the table name, keyspace name, and column names become case-sensitive. Make sure your CQL commands use case sensitivity for these values. For more information, see How do I fix a "table does not exist" error in CQL.

Unsupported CQL commands

Operations concerning keyspaces, materialized views, functions, aggregates, and search indexes are generally not supported. As a result, the following CQL commands aren’t supported for Astra DB:

CQL commands not supported for Astra DB

CREATE KEYSPACE
ALTER KEYSPACE
DROP KEYSPACE
CREATE MATERIALIZED VIEW
ALTER MATERIALIZED VIEW
DROP MATERIALIZED VIEW
CREATE AGGREGATE
DESCRIBE AGGREGATE
DROP AGGREGATE
CREATE FUNCTION
DESCRIBE FUNCTION
DROP FUNCTION
CREATE TRIGGER
DROP TRIGGER
CREATE ROLE
ALTER ROLE
DROP ROLE
LIST ROLES
LIST PERMISSIONS
RESTRICT
RESTRICT ROWS
UNRESTRICT
UNRESTRICT ROWS
CREATE SEARCH INDEX
COMMIT SEARCH INDEX
REBUILD SEARCH INDEX
RELOAD SEARCH INDEX
ALTER SEARCH INDEX SCHEMA
ALTER SEARCH INDEX CONFIG
DROP SEARCH INDEX CONFIG

For a list of supported CQL commands, see the CQL for Astra DB quick reference.

Use CQL for Astra DB

To send CQL commands to your databases, you can use the CQL shell, a driver, or the Data API.

Data API and clients

You can use the Data API and clients to manage tables in Serverless (Vector) databases, including tables you created through the CQL shell or a driver. For more information, see Get started with the Data API and About tables with the Data API.

Drivers

Astra DB is compatible with the following drivers:

Language	Driver	Documentation
C++	cpp-driver	Connect with the C++ driver
C#	csharp-driver	Connect with the C# driver
Go	gocql-astra (with cassandra-gocql-driver)	Connect with the Go driver
Java	cassandra-java-driver	Connect with the Java driver
Node.js	nodejs-driver	Connect with the Node.js driver
Python	python-driver	Connect with the Python driver
Scala	spark-cassandra-connector	Connect to Astra DB Serverless with the Apache Spark Cassandra connector

Language

Driver

Version

Documentation

C++

cpp-driver

Connect with the C++ driver

csharp-driver

Connect with the C# driver

gocql-astra (with cassandra-gocql-driver)

Connect with the Go driver

Java

cassandra-java-driver

Connect with the Java driver

Node.js

nodejs-driver

Connect with the Node.js driver

Python

python-driver

Connect with the Python driver

Scala

spark-cassandra-connector

Connect to Astra DB Serverless with the Apache Spark Cassandra connector

For more information, see databases:connection-methods-comparison.adoc#drivers.

CQL shell

The Cassandra Query Language Shell (cqlsh) is a utility that you can use to issue CQL commands to your Astra DB Serverless database. You can use the embedded CQL shell in the Astra Portal or the standalone CQL shell.

Embedded CQL shell
Standalone CQL shell

The Astra Portal provides an embedded CQL shell instance, known as the CQL Console, for each Astra DB Serverless database. You can use the CQL Console to run CQL commands on your databases directly from your browser.

In the Astra Portal navigation menu, select your database.
Click CQL Console to open the CQL Console in a new tab.
Wait for the token@cqlsh> prompt to appear.

This prompt indicates that the CQL Console is connected to your database, and you can begin issuing CQL commands to your database.
(Optional) For multi-region databases, you can use the region menu to access data from a secondary region. However, due to Astra DB’s eventual consistency model, changes to data in any region are eventually replicated to the database’s other regions.

The standalone CQL shell is a separate, lightweight utility that you can use to interact with your database.

You can use the Astra CLI to download and configure the standalone CQL shell, including the Secure Connect Bundle (SCB) and the token for your Astra account. For more information, see Use cqlsh in the Astra CLI documentation.

Install Python version 2.7.12 or later with TLS support.
Download the SCB for your database.

For multi-region databases, if you want to access data from a secondary region, you must download the SCB for that specific region. However, due to Astra DB’s eventual consistency model, changes to data in any region are eventually replicated to the database’s other regions.
Download the CQL shell for your Astra DB Serverless database:
- DataStax Astra
- DataStax Astra with support for Vector Type.
Change to the directory where you downloaded the CQL shell package:
shell
```
$ cd /DOWNLOAD_DIRECTORY
```
Extract the files:
shell
```
$ tar -xvf cqlsh-astra-DATE-bin.tar.gz
```
Replace DATE with the package release date, such as 20210304.
Run the cqlsh script:
shell
```
cd /cqlsh-astra
./bin/cqlsh -u CLIENT_ID -p CLIENT_SECRET -b PATH_TO_SCB
```
Replace the following:
- PATH_TO_SCB: The path to your database’s SCB zip file
- CLIENT_ID and CLIENT_SECRET: Either of the following sets of values from your application token:
  - The clientId value and the secret value
  - The literal string token and the token secret, prefixed by AstraCS:
(Optional) Store credentials in the cqlshrc file
If you don’t want to pass the credentials on the command line every time, you can configure the location in your cqlshrc file:
1. In the ~/.cassandra directory, open the cqlshrc file.
2. In the [authentication] section, add the CLIENT_ID and CLIENT_SECRET values from your application token.
3. In the [connection] section, add the path to the SCB.
text
[authentication] username = CLIENT_ID # or the literal string token password = CLIENT_SECRET # or the token secret prefixed by 'AstraCS:' [connection] secure_connect_bundle = PATH_TO_SCB
Make sure the command returns output indicating that you connected to your database. For example:
```
[cqlsh 6.8.0 | DSE 6.8.0.77 | CQL spec 3.4.5 | DSE protocol v2]
Use HELP for help.
username@cqlsh>
```

Use analyzers with CQL for Astra DB

Analyzers process the text in a column to enable term matching on strings. Combined with vector-based search algorithms, term matching makes it easier to find relevant information in a table. Analyzers semantically filter the results of a vector search by specific terms.

Analyzers are built on the Lucene Java Analyzer API. Storage Attached Indexes (SAI) use the Lucene Java Analyzer API to transform text columns into tokens for indexing and querying, which can use built-in or custom analyzers.

For example, if you generate an embedding from the phrase "tell me about available shoes", you can then use a vector search to get a list of rows with similar vectors. These rows will likely correlate with shoe-related strings.

Example: CQL vector searchcql

SELECT * from products
  ORDER BY vector ANN OF [6.0,2.0, ... 3.1,4.0]
  LIMIT 10;

Alternatively, you can filter these search results by a specific keyword, such as hiking:

Example: CQL vector search with filtercql

SELECT * from products
  WHERE val : 'hiking'
  ORDER BY vector ANN OF [6.0,2.0, … 3.1,4.0]
  LIMIT 10;

To enable analyzer operations with CQL on a Serverless (Vector) database, you must create a SAI with the index_analyzer option, and then use the : operator to search the indexed column that has been analyzed.

An analyzed index stores values derived from the raw column values. The stored values are dependent on the analyzer configuration options, which include the following:

The analyzer determines how the column values are analyzed before indexing occurs, and the analyzer is applied to query terms as well.

Example: STANDARD tokenizer

Create a table:

cql

CREATE TABLE default_keyspace.products
(
  id text PRIMARY KEY,
  val text
);

Create an SAI index with the index_analyzer option and stemming enabled:

cql

CREATE CUSTOM INDEX default_keyspace_products_val_idx
  ON default_keyspace.products(val)
  USING 'org.apache.cassandra.index.sai.StorageAttachedIndex' WITH OPTIONS = {
  'index_analyzer': '{
    "tokenizer" : {"name" : "standard"},
    "filters" : [{"name" : "porterstem"}]
}'};

Insert sample rows:

cql

INSERT INTO default_keyspace.products (id, val)
  VALUES ('1', 'soccer cleats');
INSERT INTO default_keyspace.products (id, val)
  VALUES ('2', 'running shoes');
INSERT INTO default_keyspace.products (id, val)
  VALUES ('3', 'hiking shoes');

Use keywords to query data from the inserted rows. The analyzer splits the text into case-independent terms.
Query on "running"cql
```
SELECT * FROM default_keyspace.products
  WHERE val : 'running';
```
Query on "hiking" and "shoes"cql
```
SELECT * FROM default_keyspace.products
  WHERE val : 'hiking' AND val : 'shoes';
```

Example: N-GRAM tokenizer

An ngram tokenizer processes text by splitting the given text into contiguous sequences of n tokens to capture the linguistic patterns and context. This is a part of natural language processing (NLP) tasks.

To configure an ngram tokenizer that also lowercases all tokens, ensure the "tokenizer" key specifies the Lucene tokenizer. The remaining key-value pairs in the JSON object configure the tokenizer:

cql

  WITH OPTIONS = {
  'index_analyzer': '{
  "tokenizer" : {"name" : "ngram", "args" : {"minGramSize":"2", "maxGramSize":"3"}},
  "filters" : [{"name" : "lowercase"}]
  }'
}

For more information, see N-GRAM with lowercase.

For more information about analyzers, the : operator, and analyzer examples, see CQL vector search with text analyzer and the Simple STANDARD analyzer example.

Non-tokenizing filters include normalize, case_sensitive, and ascii. You can’t combine non-tokenizing filters with index_analyzer, but you can chain them in a pipeline with other non-tokenizing filters:

cql

OPTIONS = {'normalize': true, 'case_sensitive': false}

Supported built-in analyzers

There are several built-in analyzers from the Lucene project (version 9.8.0). This includes the following analyzers:

Built-in analyzer types
Generic analyzers	standard, simple, whitespace, stop, lowercase, keyword
Language-specific analyzers	Arabic, Armenian, Basque, Bengali, Brazilian, Bulgarian, Catalan, CJK, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hindi, Hungarian, Indonesian, Irish, Italian, Latvian, Lithuanian, Norwegian, Persian, Portuguese, Romanian, Russian, Sorani, Spanish, Swedish, Thai, Turkish
Tokenizers	standard, classic, keyword, letter, nGram, edgeNGram, pathHierarchy, pattern, simplePattern, simplePatternSplit, thai, uax29UrlEmail, whitespace, wikipedia
CharFilters	cjk, htmlstrip, mapping, persian, patternreplace
TokenFilters	apostrophe, wordDelimiterGraph, portugueseLightStem, latvianStem, dropIfFlagged, keepWord, indicNormalization, bengaliStem, turkishLowercase, galicianStem, bengaliNormalization, portugueseMinimalStem, galicianMinimalStem, swedishMinimalStem, stop, limitTokenCount, italianLightStem, wordDelimiter, teluguStem, hungarianLightStem, protectedTerm, lowercase, capitalization, hyphenatedWords, type, keywordMarker, frenchMinimalStem, kStem, swedishLightStem, soraniNormalization, commonGramsQuery, numericPayload, persianStem, limitTokenOffset, hunspellStem, soraniStem, czechStem, norwegianMinimalStem, englishMinimalStem, norwegianLightStem, germanMinimalStem, snowballPorter, removeDuplicates, minHash, keywordRepeat, germanNormalization, dictionaryCompoundWord, synonymGraph, englishPossessive, spanishMinimalStem, fixedShingle, patternTyping, classic, frenchLightStem, trim, indonesianStem, spanishPluralStem, hindiStem, scandinavianFolding, delimitedBoost, commonGrams, reverseString, cjkWidth, fingerprint, finnishLightStem, greekStem, porterStem, limitTokenPosition, persianNormalization, typeAsSynonym, patternReplace, tokenOffsetPayload, codepointCount, bulgarianStem, synonym, germanStem, asciiFolding, decimalDigit, Word2VecSynonym, scandinavianNormalization, russianLightStem, serbianNormalization, elision, portugueseStem, arabicNormalization, length, greekLowercase, concatenateGraph, flattenGraph, fixBrokenOffsets, truncate, cjkBigram, brazilianStem, uppercase, nGram, dateRecognizer, teluguNormalization, shingle, norwegianNormalization, hindiNormalization, delimitedPayload, spanishLightStem, stemmerOverride, patternCaptureGroup, hyphenationCompoundWord, germanLightStem, edgeNGram, typeAsPayload, irishLowercase, delimitedTermFrequency, arabicStem

For more information and examples, see Built-in analyzers.

Cassandra Query Language (CQL) for Astra DB

CQL for Astra DB limitations

User-defined types aren’t supported

Unsupported values are ignored

Keyspace, table, and column names are case sensitive

Unsupported CQL commands

Use CQL for Astra DB

Data API and clients

Drivers

CQL shell

Use analyzers with CQL for Astra DB

Supported built-in analyzers

CQL documentation

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