Aiven Kafka GCS Connector

Build Status

This is a sink Kafka Connect connector that stores Kafka messages in a Google Cloud Storage (GCS) bucket.

How It Works

The connector subscribes to the specified Kafka topics and collects messages coming in them and periodically dumps the collected data to the specified bucket in GCS.

Sometimes—for example, on reprocessing of some data—the connector will overwrite files that are already in the bucket. You need to ensure the bucket doesn't have a retention policy that prohibits overwriting.

The following object permissions must be enabled in the bucket:

File name format

The connector uses the following format for output files (blobs): <prefix><filename>.

<prefix> is the optional prefix that can be used, for example, for subdirectories in the bucket.

<filename> is the file name. The connector has the configurable template for file names. It supports placeholders with variable names: {{ variable_name }}. Currently supported variables are:

To add zero padding to Kafka offsets, you need to add additional parameter padding in the start_offset variable, which value can be true or false (the default). For example: {{topic}}-{{partition}}-{{start_offset:padding=true}}.gz will produce file names like mytopic-1-00000000000000000001.gz.

To add formatted timestamps, use timestamp variable.
For example: {{topic}}-{{partition}}-{{start_offset}}-{{timestamp:unit=YYYY}}{{timestamp:unit=MM}}{{timestamp:unit=dd}}.gz will produce file names like mytopic-2-1-20200301.gz.

To configure the time zone for the timestamp variable, use file.name.timestamp.timezone property. Please see the description of properties in the "Configuration" section.

Only the certain combinations of variables and parameters are allowed in the file name template (however, variables in a template can be in any order). Each combination determines the mode of record grouping the connector will use. Currently supported combinations of variables and the corresponding record grouping modes are:

If the file name template is not specified, the default value is {{topic}}-{{partition}}-{{start_offset}} (+ .gz when compression is enabled).

Record grouping

Incoming records are being grouped until flushed.

Grouping by the topic and partition

In this mode, the connector groups records by the topic and partition. When a file is written, a offset of the first record in it is added to its name.

For example, let's say the template is {{topic}}-part{{partition}}-off{{start_offset}}. If the connector receives records like

topic:topicB partition:0 offset:0
topic:topicA partition:0 offset:0
topic:topicA partition:0 offset:1
topic:topicB partition:0 offset:1
flush

there will be two files topicA-part0-off0 and topicB-part0-off0 with two records in each.

Each flush produces a new set of files. For example:

topic:topicA partition:0 offset:0
topic:topicA partition:0 offset:1
flush
topic:topicA partition:0 offset:2
topic:topicA partition:0 offset:3
flush

In this case, there will be two files topicA-part0-off0 and topicA-part0-off2 with two records in each.

Grouping by the key

In this mode, the connector groups records by the Kafka key. It always puts one record in a file, the latest record that arrived before a flush for each key. Also, it overwrites files if later new records with the same keys arrive.

This mode is good for maintaining the latest values per key as files on GCS.

Let's say the template is k{{key}}. For example, when the following records arrive

key:0 value:0
key:1 value:1
key:0 value:2
key:1 value:3
flush

there will be two files k0 (containing value 2) and k1 (containing value 3).

After a flush, previously written files might be overwritten:

key:0 value:0
key:1 value:1
key:0 value:2
key:1 value:3
flush
key:0 value:4
flush

In this case, there will be two files k0 (containing value 4) and k1 (containing value 3).

The string representation of a key

The connector in this mode uses the following algorithm to create the string representation of a key:

  1. If key is null, the string value is "null" (i.e., string literal null).
  2. If key schema type is STRING, it's used directly.
  3. Otherwise, Java .toString() is applied.

If keys of you records are strings, you may want to use org.apache.kafka.connect.storage.StringConverter as key.converter.

Warning: Single key in different partitions

The group by key mode primarily targets scenarios where each key appears in one partition only. If the same key appears in multiple partitions the result may be unexpected.

For example:

topic:topicA partition:0 key:x value:aaa
topic:topicA partition:1 key:x value:bbb
flush

file kx may contain aaa or bbb, i.e. the behavior is non-deterministic.

Data format

Output files are text files that contain one record per line (i.e., they're separated by \n).

The connector can output the following fields from records into the output: the key, the value, the timestamp, and the offset. (The set and the order of these output fields is configurable.) The field values are separated by comma.

The key and the value—if they're output—are stored as binaries encoded in Base64.

For example, if we output key,value,offset,timestamp, a record line might look like:

a2V5,TG9yZW0gaXBzdW0gZG9sb3Igc2l0IGFtZXQ=,1232155,1554210895

It is possible to control the encoding of the value field by setting format.output.fields.value.encoding to base64 or none.

If the key, the value or the timestamp is null, an empty string will be output instead:

,,,1554210895

It is possible to control the number of records to be put in a particular output file by setting file.max.records. By default, it is 0, which is interpreted as "unlimited".

Configuration

Here you can read about the Connect workers configuration and here, about the connector Configuration.

Here is an example connector configuration with descriptions:

### Standard connector configuration

## Fill in your values in these:

# Unique name for the connector.
# Attempting to register again with the same name will fail.
name=my-gcs-connector

## These must have exactly these values:

# The Java class for the connector
connector.class=io.aiven.kafka.connect.gcs.GcsSinkConnector

# The key converter for this connector
# (must be set to org.apache.kafka.connect.converters.ByteArrayConverter
# or org.apache.kafka.connect.storage.StringConverter)
key.converter=org.apache.kafka.connect.converters.ByteArrayConverter

# The value converter for this connector
# (must be set to ByteArrayConverter)
value.converter=org.apache.kafka.connect.converters.ByteArrayConverter

# A comma-separated list of topics to use as input for this connector
# Also a regular expression version `topics.regex` is supported.
# See https://kafka.apache.org/documentation/#connect_configuring
topics=topic1,topic2

### Connector-specific configuration
### Fill in you values

# The name of the GCS bucket to use
# Required.
gcs.bucket.name=my-gcs-bucket

## The following two options are used to specify GCP credentials.
## See the overview of GCP authentication:
##  - https://cloud.google.com/docs/authentication/
##  - https://cloud.google.com/docs/authentication/production
## If they both are not present, the connector will try to detect
## the credentials automatically.
## If only one is present, the connector will use it to get the credentials.
## If both are present, this is an error.

# The path to a GCP credentials file.
# Optional, the default is null.
gcs.credentials.path=/some/path/google_credentials.json

# GCP credentials as a JSON object.
# Optional, the default is null.
gcs.credentials.json={"type":"...", ...}

##

# The set of the fields that are to be output, comma separated.
# Supported values are: `key`, `value`, `offset`, and `timestamp`.
# Optional, the default is `value`.
format.output.fields=key,value,offset,timestamp

# The prefix to be added to the name of each file put on GCS.
# See the GCS naming requirements https://cloud.google.com/storage/docs/naming
# Optional, the default is empty.
file.name.prefix=some-prefix/

# The compression type used for files put on GCS.
# The supported values are: `gzip`, `snappy`, `zstd`, `none`.
# Optional, the default is `none`.
file.compression.type=gzip

# The time zone in which timestamps are represented.
# Accepts short and long standard names like: `UTC`, `PST`, `ECT`,
# `Europe/Berlin`, `Europe/Helsinki`, or `America/New_York`. 
# For more information please refer to https://docs.oracle.com/javase/tutorial/datetime/iso/timezones.html.
# The default is `UTC`.
file.name.timestamp.timezone=Europe/Berlin

# The source of timestamps.
# Supports only `wallclock` which is the default value.
file.name.timestamp.source=wallclock

# The file name template.
# See "File name format" section.
# Optional, the default is `{{topic}}-{{partition}}-{{start_offset:padding=false}}` or
# `{{topic}}-{{partition}}-{{start_offset:padding=false}}.gz` if the compression is enabled.
file.name.template={{topic}}-{{partition}}-{{start_offset:padding=true}}.gz

Development

Integration testing

Integration tests are implemented using JUnit, Gradle and Docker.

To run them, you need:

In order to run the integration tests, execute from the project root directory:

./gradlew clean integrationTest -PtestGcsBucket=test-bucket-name

where PtestGcsBucket is the name of the GCS bucket to use.

The default GCP credentials will be used during the test (see the GCP documentation and the comment in GCP SDK code). This can be overridden either by seting the path to the GCP credentials file or by setting the credentials JSON string explicitly. (See Configuration section for details).

To specify the GCS credentials path, use gcsCredentialsPath property:

./gradlew clean integrationTest -PtestGcsBucket=test-bucket-name \
    -PgcsCredentialsPath=/path/to/credentials.json

To specify the GCS credentials JSON, use gcsCredentialsJson property:

./gradlew clean integrationTest -PtestGcsBucket=test-bucket-name \
    -PgcsCredentialsJson='{type":"...", ...}'

Gralde allows to set properties using environment variables, for example, ORG_GRADLE_PROJECT_testGcsBucket=test-bucket-name. See more about the ways to set properties here.

Releasing

TBD

License

This project is licensed under the GNU Affero General Public License Version 3.