Scylla CDC Source Connector is a source connector capturing row-level changes in the tables of a Scylla cluster. It is a Debezium connector, compatible with Kafka Connect (with Kafka 2.6.0+) and built on top of scylla-cdc-java library.
The connector reads the CDC log for specified tables and produces Kafka messages for each row-level INSERT, UPDATE or DELETE operation. The connector is able to split reading the CDC log accross multiple processes: the connector can start a separate Kafka Connect task for reading each Vnode of Scylla cluster allowing for high throughput. You can limit the number of started tasks by using tasks.max property.
Scylla CDC Source Connector seamlessly handles schema changes and topology changes (adding, removing nodes from Scylla cluster). The connector is fault-tolerant, retrying reading data from Scylla in case of failure. It periodically saves the current position in Scylla CDC log using Kafka Connect offset tracking (configurable by offset.flush.interval.ms parameter). If the connector is stopped, it is able to resume reading from previously saved offset. Scylla CDC Source Connector has at-least-once semantics.
The connector has the following capabilities:
- Kafka Connect connector using Debezium framework
- Replication of row-level changes from Scylla using Scylla CDC:
INSERTUPDATEDELETE(single row deletes)
- High scalability - able to split work accross multiple Kafka Connect workers
- Fault tolerant - connector periodically saves its progress and can resume from previously saved offset (with at-least-once semantics)
- Support for many standard Kafka Connect converters, such as JSON and Avro
- Compatible with standard Kafka Connect transformations
- Metadata about CDC events - each generated Kafka message contains information about source, such as timestamp and table name
- Seamless handling of schema changes and topology changes (adding, removing nodes from Scylla cluster)
The connector has the following limitations:
- Only Kafka 2.6.0+ is supported
- Only row-level operations are produced (
INSERT,UPDATE,DELETE):- Partition deletes - those changes are ignored
- Row range deletes - those changes are ignored
- No support for collection types (
LIST,SET,MAP) andUDT- columns with those types are omitted from generated messages - No support for preimage and postimage - changes only contain those columns that were modified, not the entire row before/after change. More information here
You can download the connector as a prebuilt package:
- JAR with dependencies (fat JAR): scylla-cdc-source-connector-1.0.1-jar-with-dependencies.jar
- Confluent Hub package (ZIP): ScyllaDB-scylla-cdc-source-connector-1.0.1.zip
The artifacts are also available in Maven Central Repository - we recommend using the "JAR with dependencies" file there.
You can also build the connector from source by using the following commands:
git clone https://github.com/scylladb/scylla-cdc-source-connector.git
cd scylla-cdc-source-connector
mvn clean packageThe connector JAR file will be available in scylla-cdc-kafka-connect/target/fat-jar directory.
Copy the JAR file with connector into your Kafka Connect deployment and append the directory containing the connector to your Kafka Connect's plugin path (plugin.path configuration property).
Scylla CDC Source Connector exposes many configuration properties. These are the most important:
| Property | Required | Description |
|---|---|---|
scylla.name |
Yes | A unique name that identifies the Scylla cluster and that is used as a prefix for all schemas, topics. The logical name allows you to easily differentiate between your different Scylla cluster deployments. Each distinct Scylla installation should have a separate namespace and be monitored by at most one Scylla CDC Source Connector. It should consist of alphanumeric or underscore (_) characters. |
scylla.cluster.ip.addresses |
Yes | List of IP addresses of nodes in the Scylla cluster that the connector will use to open initial connections to the cluster. In the form of a comma-separated list of pairs : (host1:port1,host2:port2). |
scylla.table.names |
Yes | List of CDC-enabled table names for connector to read. See Change Data Capture (CDC) for more information about configuring CDC on Scylla. Provided as a comma-separated list of pairs <keyspace name>.<table name>. |
scylla.user |
No | The username to connect to Scylla with. If not set, no authorization is done. |
scylla.password |
No | The password to connect to Scylla with. If not set, no authorization is done. |
See additional configuration properties in the "Advanced administration" section.
Example configuration (as .properties file):
name=ScyllaCDCSourceConnector
connector.class=com.scylladb.cdc.debezium.connector.ScyllaConnector
scylla.name=MyScyllaCluster
scylla.cluster.ip.addresses=127.0.0.1:9042,127.0.0.2:9042
scylla.table.names=ks.my_table
tasks.max=10
key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter
key.converter.schemas.enable=true
value.converter.schemas.enable=true
auto.create.topics.enable=true
This configuration will capture row-level changes in the ks.my_table table from Scylla cluster (127.0.0.1, 127.0.0.2). Change data capture events will appear on MyScyllaCluster.ks.my_table Kafka topic encoded as JSONs with schema information.
Scylla CDC Source Connector writes events to a separate Kafka topic for each source Scylla table. The topic name will be: logicalName.keyspaceName.tableName (logical name configured by scylla.name property). You can turn on automatic topic creation by using the auto.create.topics.enable property.
Scylla CDC Source Connector generates a data change event for each row-level INSERT, UPDATE or DELETE operation. Each event consists of key and value.
Debezium and Kafka Connect are designed around continuous streams of event messages, and the structure of these events may change over time. This could be difficult for consumers to deal with, so to make it easy Kafka Connect makes each event self-contained. Every message key and value has two parts: a schema and payload. The schema describes the structure of the payload, while the payload contains the actual data.
The data change event's key will contain a field for each column in the primary key (partition key and clustering key).
For example, given this Scylla table and INSERT operation:
CREATE TABLE ks.t(
pk int, ck int, v text, PRIMARY KEY(pk, ck)
) WITH cdc = {'enabled': true};
INSERT INTO ks.t(pk, ck, v) VALUES (1, 1, 'example row');
The data change event's key will look like this (with JSON serializer and schema enabled):
{
"schema": {
"type": "struct",
"fields": [
{
"type": "int32",
"optional": true,
"field": "ck"
},
{
"type": "int32",
"optional": true,
"field": "pk"
}
],
"optional": false,
"name": "MyScyllaCluster.ks.my_table.Key"
},
"payload": {
"ck": 1,
"pk": 1
}
}Data change event's value consists of a schema and a payload section. The payload of every data change events contains the following fields:
op: type of operation.cforINSERT,uforUPDATE,dforDELETE.before: an optional field with state of the row before the event occurred. Present inDELETEdata change events.after: an optional field with state of the row after the event occurred. Present inUPDATEandINSERTdata change events.ts_ms: time at which connector processed the event.source: metadata about the source of event:name: logical name of Scylla cluster (scylla.name).ts_ms: the time that the change was made in the database (in milliseconds). You can compute a difference betweensource.ts_msand (top-level)ts_msto determine the lag between the source Scylla change and the connector.ts_us: the time that the change was made in the database (in microseconds).keyspace_name,table_name: the name of keyspace and table this data change event originated from.
Operations in Scylla, such as INSERT or UPDATE, do not have to modify all columns of a row. To differentiate between non-modification of column and inserting/updating NULL, all non-primary-key columns are wrapped with structure containing a single value field. For example, given this Scylla table and UPDATE operation:
CREATE TABLE ks.t(
pk int, ck int, v text, PRIMARY KEY(pk, ck)
) WITH cdc = {'enabled': true};
INSERT INTO ks.t(pk, ck, v) VALUES (1, 1, 'example row');
UPDATE ks.t SET v = 'new value' WHERE pk = 1 AND ck = 1;
The v column will be represented as:
...
"v": {
"value": "new value"
}
...In case of UPDATE setting v to NULL:
UPDATE ks.t SET v = NULL WHERE pk = 1 AND ck = 1;
The v column will be represented as:
...
"v": {
"value": null
}
...If the operation did not modify the v column, the data event will contain the following representation of v:
...
"v": null
...See UPDATE example for full data change event's value.
The connector provides two single message transformations (SMTs): ScyllaExtractNewRecordState (class: com.scylladb.cdc.debezium.connector.transforms.ScyllaExtractNewRecordState) and ScyllaFlattenColumns (com.scylladb.cdc.debezium.connector.transforms.ScyllaFlattenColumns).
ScyllaExtractNewRecordState works like exactly like io.debezium.transforms.ExtractNewRecordState (in fact it is called underneath), but also flattens structure by extracting values from the aforementioned single-field structures.
Such transformation makes message structure simpler (and easier to use with e.g. Elasticsearch), but it makes it impossible to differentiate between NULL value and non-modification.
If the message is as following:
{
"schema": {
"type": "struct",
"fields": [
{
"type": "int32",
"optional": true,
"field": "ck"
},
{
"type": "int32",
"optional": true,
"field": "pk"
},
{
"type": "struct",
"fields": [
{
"type": "int32",
"optional": true,
"field": "value"
}
],
"optional": true,
"name": "NS2.ks.t.v.Cell",
"field": "v"
}
],
"optional": false,
"name": "NS2.ks.t.After"
},
"payload": {
"ck": 2,
"pk": 20,
"v": {
"value": 3
}
}
}then the same message transformed by ScyllaExtractNewRecordState would be:
{
"schema": {
"type": "struct",
"fields": [
{
"type": "int32",
"optional": true,
"field": "ck"
},
{
"type": "int32",
"optional": true,
"field": "pk"
},
{
"type": "int32",
"optional": true,
"field": "v"
}
],
"optional": false,
"name": "NS2.ks.t.After"
},
"payload": {
"ck": 2,
"pk": 20,
"v": 3
}
}Notice how v field is no longer packed in value.
ScyllaFlattenColumns flattens columns that are wrapped in value structure, such as:
"v": {
"value": 3
}transforming it into:
"v": 3Compared to ScyllaExtractNewRecordState transformation, ScyllaFlattenColumns does not remove any additional metadata or modify the message in any other way.
For example, running the transformation on this message:
{
"source": {
"version": "1.1.4",
"connector": "scylla",
"name": "SMTExample",
"ts_ms": 1706890860030,
"snapshot": {
"string": "false"
},
"db": "ks",
"keyspace_name": "ks",
"table_name": "t",
"ts_us": 1706890860030414
},
"before": null,
"after": {
"SMTExample.ks.t.Before": {
"ck": 7,
"pk": 1,
"v": {
"value": 7
}
}
},
"op": {
"string": "c"
},
"ts_ms": {
"long": 1706890892952
},
"transaction": null
}will result in the following message:
{
"source": {
"version": "1.1.4",
"connector": "scylla",
"name": "SMTExample",
"ts_ms": 1706890860030,
"snapshot": {
"string": "false"
},
"db": "ks",
"keyspace_name": "ks",
"table_name": "t",
"ts_us": 1706890860030414
},
"before": null,
"after": {
"SMTExample.ks.t.Before": {
"ck": 7,
"pk": 1,
"v": 7
}
},
"op": {
"string": "c"
},
"ts_ms": {
"long": 1706890892952
},
"transaction": null
}while ScyllaExtractNewRecordState would produce:
{
"ck": 7,
"pk": 1,
"v": 7
}Given this Scylla table and INSERT operation:
CREATE TABLE ks.t(
pk int, ck int, v text, PRIMARY KEY(pk, ck)
) WITH cdc = {'enabled': true};
INSERT INTO ks.t(pk, ck, v) VALUES (1, 1, 'example row');
The connector will generate the following data change event's value (with JSON serializer and schema enabled):
{
"schema": {
"type": "struct",
"fields": [
{
"type": "struct",
"fields": [
{
"type": "string",
"optional": false,
"field": "version"
},
{
"type": "string",
"optional": false,
"field": "connector"
},
{
"type": "string",
"optional": false,
"field": "name"
},
{
"type": "int64",
"optional": false,
"field": "ts_ms"
},
{
"type": "int64",
"optional": false,
"field": "ts_us"
},
{
"type": "string",
"optional": true,
"name": "io.debezium.data.Enum",
"version": 1,
"parameters": {
"allowed": "true,last,false"
},
"default": "false",
"field": "snapshot"
},
{
"type": "string",
"optional": false,
"field": "db"
},
{
"type": "string",
"optional": false,
"field": "keyspace_name"
},
{
"type": "string",
"optional": false,
"field": "table_name"
}
],
"optional": false,
"name": "com.scylladb.cdc.debezium.connector",
"field": "source"
},
{
"type": "struct",
"fields": [
{
"type": "int32",
"optional": true,
"field": "ck"
},
{
"type": "int32",
"optional": true,
"field": "pk"
},
{
"type": "struct",
"fields": [
{
"type": "string",
"optional": true,
"field": "value"
}
],
"optional": true,
"name": "MyScyllaCluster.ks.my_table.v.Cell",
"field": "v"
}
],
"optional": true,
"name": "MyScyllaCluster.ks.my_table.Before",
"field": "before"
},
{
"type": "struct",
"fields": [
{
"type": "int32",
"optional": true,
"field": "ck"
},
{
"type": "int32",
"optional": true,
"field": "pk"
},
{
"type": "struct",
"fields": [
{
"type": "string",
"optional": true,
"field": "value"
}
],
"optional": true,
"name": "MyScyllaCluster.ks.my_table.v.Cell",
"field": "v"
}
],
"optional": true,
"name": "MyScyllaCluster.ks.my_table.After",
"field": "after"
},
{
"type": "string",
"optional": true,
"field": "op"
},
{
"type": "int64",
"optional": true,
"field": "ts_ms"
},
{
"type": "struct",
"fields": [
{
"type": "string",
"optional": false,
"field": "id"
},
{
"type": "int64",
"optional": false,
"field": "total_order"
},
{
"type": "int64",
"optional": false,
"field": "data_collection_order"
}
],
"optional": true,
"field": "transaction"
}
],
"optional": false,
"name": "MyScyllaCluster.ks.my_table.Envelope"
},
"payload": {
"source": {
"version": "1.0.1-SNAPSHOT",
"connector": "scylla",
"name": "MyScyllaCluster",
"ts_ms": 1611578778701,
"ts_us": 1611578778701813,
"snapshot": "false",
"db": "ks",
"keyspace_name": "ks",
"table_name": "my_table"
},
"before": null,
"after": {
"ck": 1,
"pk": 1,
"v": {
"value": "example row"
}
},
"op": "c",
"ts_ms": 1611578838754,
"transaction": null
}
}Given this Scylla table and UPDATE operations:
CREATE TABLE ks.t(
pk int, ck int, v text, PRIMARY KEY(pk, ck)
) WITH cdc = {'enabled': true};
UPDATE ks.t SET v = 'new value' WHERE pk = 1 AND ck = 1;
UPDATE ks.t SET v = NULL WHERE pk = 1 AND ck = 1;
The connector will generate the following data change event's value (with JSON serializer and schema enabled) for the first UPDATE. Note that schema is ommitted as it is the same as in INSERT example:
{
"schema": {},
"payload": {
"source": {
"version": "1.0.1-SNAPSHOT",
"connector": "scylla",
"name": "MyScyllaCluster",
"ts_ms": 1611578808701,
"ts_us": 1611578808701321,
"snapshot": "false",
"db": "ks",
"keyspace_name": "ks",
"table_name": "my_table"
},
"before": null,
"after": {
"ck": 1,
"pk": 1,
"v": {
"value": "new value"
}
},
"op": "u",
"ts_ms": 1611578868758,
"transaction": null
}
}Data change event's value for the second UPDATE:
{
"schema": {},
"payload": {
"source": {
"version": "1.0.1-SNAPSHOT",
"connector": "scylla",
"name": "MyScyllaCluster",
"ts_ms": 1611578808701,
"ts_us": 1611578808701341,
"snapshot": "false",
"db": "ks",
"keyspace_name": "ks",
"table_name": "my_table"
},
"before": null,
"after": {
"ck": 1,
"pk": 1,
"v": {
"value": null
}
},
"op": "u",
"ts_ms": 1611578868758,
"transaction": null
}
}Given this Scylla table and DELETE operation:
CREATE TABLE ks.t(
pk int, ck int, v text, PRIMARY KEY(pk, ck)
) WITH cdc = {'enabled': true};
DELETE FROM ks.t WHERE pk = 1 AND ck = 1;
The connector will generate the following data change event's value (with JSON serializer and schema enabled). Note that schema is ommitted as it is the same as in INSERT example:
{
"schema": {},
"payload": {
"source": {
"version": "1.0.1-SNAPSHOT",
"connector": "scylla",
"name": "MyScyllaCluster",
"ts_ms": 1611578808701,
"ts_us": 1611578808701919,
"snapshot": "false",
"db": "ks",
"keyspace_name": "ks",
"table_name": "my_table"
},
"before": {
"ck": 1,
"pk": 1,
"v": null
},
"after": null,
"op": "d",
"ts_ms": 1611578868759,
"transaction": null
}
}In addition to the configuration parameters described in the "Configuration" section, Scylla CDC Source Connector exposes the following (non-required) configuration parameters:
| Property | Description |
|---|---|
scylla.query.time.window.size |
The size of windows queried by the connector. Changes are queried using SELECT statements with time restriction with width defined by this parameter. Value expressed in milliseconds. |
scylla.confidence.window.size |
The size of the confidence window. It is necessary for the connector to avoid reading too fresh data from the CDC log due to the eventual consistency of Scylla. The problem could appear when a newer write reaches a replica before some older write. For a short period of time, when reading, it is possible for the replica to return only the newer write. The connector mitigates this problem by not reading a window of most recent changes (controlled by this parameter). Value expressed in milliseconds. |
scylla.consistency.level |
The consistency level of CDC table read queries. This consistency level is used only for read queries to the CDC log table. By default, QUORUM level is used. |
scylla.local.dc |
The name of Scylla local datacenter. This local datacenter name will be used to setup the connection to Scylla to prioritize sending requests to the nodes in the local datacenter. If not set, no particular datacenter will be prioritized. |
Scylla CDC Source Connector reads the CDC log by quering on Vnode granularity level. It uses Kafka Connect to store current progress (offset) for each Vnode. By default, there are 256 Vnodes per each Scylla node. Kafka Connect stores those offsets in its connect-offsets internal topic, but it could grow large in case of big Scylla clusters. You can minimize this topic size, by adjusting the following configuration options on this topic:
segment.bytesorsegment.ms- lowering them will make the compaction process trigger more often.cleanup.policy=deleteand settingretention.msto at least the TTL value of your Scylla CDC table (in milliseconds; Scylla default is 24 hours). Using this configuration, older offsets will be deleted. By settingretention.msto at least the TTL value of your Scylla CDC table, we make sure to delete only those offsets that have already expired in the source Scylla CDC table.
By adjusting tasks.max property, you can configure how many Kafka Connect worker tasks will be started. By scaling up the number of nodes in your Kafka Connect cluster (and tasks.max number), you can achieve higher throughput. In general, the tasks.max property should be greater or equal the number of nodes in Kafka Connect cluster, to allow the connector to start on each node. tasks.max property should also be greater or equal the number of nodes in your Scylla cluster, especially if those nodes have high shard count (32 or greater) as they have a large number of CDC Streams.
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