GCP Storage

Connector Class

io.lenses.streamreactor.connect.gcp.storage.source.GCPStorageSourceConnector

Example

name=gcp-storageSourceConnectorParquet # this can be anything
connector.class=io.lenses.streamreactor.connect.gcp.storage.source.GCPStorageSourceConnector
tasks.max=1
connect.gcpstorage.kcql=insert into $TOPIC_NAME select * from $BUCKET_NAME:$PREFIX_NAME STOREAS `parquet`
connect.gcpstorage.gcp.auth.mode=Credentials
connect.gcpstorage.gcp.credentials=$GCP_CREDENTIALS
connect.gcpstorage.gcp.project.id=$GCP_PROJECT_ID

KCQL Support

The connector uses a SQL-like syntax to configure the connector behaviour. The full KCQL syntax is:

INSERT INTO $kafka-topic
SELECT *
FROM bucketAddress:pathPrefix
[BATCH=batch]
[STOREAS storage_format]
[LIMIT limit]
[PROPERTIES(
  'property.1'=x,
  'property.2'=x,
)]

Please note that you can employ escaping within KCQL for the INSERT INTO, SELECT * FROM, and PARTITIONBY clauses when necessary. For example, if you need to use a topic name that contains a hyphen, you can escape it as follows:

INSERT INTO `my-topic-with-hyphen`
SELECT *
FROM bucketAddress:pathPrefix

Source Bucket & Path

The GCP Storage source location is defined within the FROM clause. The connector will read all files from the given location considering the data partitioning and ordering options. Each data partition will be read by a single connector task.

The FROM clause format is:

FROM [bucketname]:pathprefix
//my-bucket-called-pears:my-folder-called-apples 

Target Bucket & Path

The target Kafka topic is specified via the INSERT INTO clause. The connector will write all the records to the given topic:

INSERT INTO my-apples-topic SELECT * FROM  my-bucket-called-pears:my-folder-called-apples 

GCP Storage File formats

The connector supports a range of storage formats, each with its own distinct functionality:

• JSON: The connector will read files containing JSON content, each line representing a distinct record.

• Avro: The connector will read Avro-stored messages from GCP Storage and translate them into Kafka’s native format.

• Parquet: The connector will read Parquet-stored messages from GCP Storage and translate them into Kafka’s native format.

• Text: The connector will read files containing lines of text, each line representing a distinct record.

• CSV: The connector will read files containing lines of text, each line representing a distinct record.

• CSV_WithHeaders: The connector will read files containing lines of text, each line representing a distinct record while skipping the header row.

• Bytes: The connector will read files containing bytes, each file is translated to a Kafka message.

Use the STOREAS clause to configure the storage format. The following options are available:

STOREAS `JSON`
STOREAS `Avro`
STOREAS `Parquet`
STOREAS `Text`
STOREAS `CSV`
STOREAS `CSV_WithHeaders`
STOREAS `Bytes`

Text Processing

When using Text storage, the connector provides additional configuration options to finely control how text content is processed.

Regex

In Regex mode, the connector applies a regular expression pattern, and only when a line matches the pattern is it considered a record. For example, to include only lines that start with a number, you can use the following configuration:

Start-End line

In Start-End Line mode, the connector reads text content between specified start and end lines, inclusive. This mode is useful when you need to extract records that fall within defined boundaries. For instance, to read records where the first line is ‘SSM’ and the last line is an empty line (’’), you can configure it as follows:

connect.gcpstorage.kcql=insert into $kafka-topic select * from lensesio:multi_line STOREAS `text` PROPERTIES('read.text.mode'='startEndLine', 'read.text.start.line'='SSM', 'read.text.end.line'='')

To trim the start and end lines, set the read.text.trim property to true:

connect.gcpstorage.kcql=insert into $kafka-topic select * from lensesio:multi_line STOREAS `text` PROPERTIES('read.text.mode'='startEndLine', 'read.text.start.line'='SSM', 'read.text.end.line'='', 'read.text.trim'='true')

Start-End tag

In Start-End Tag mode, the connector reads text content between specified start and end tags, inclusive. This mode is particularly useful when a single line of text in S3 corresponds to multiple output Kafka messages. For example, to read XML records enclosed between ‘’ and ‘’, configure it as follows:

 connect.gcpstorage.kcql=insert into $kafka-topic select * from lensesio:xml STOREAS `text` PROPERTIES('read.text.mode'='startEndTag', 'read.text.start.tag'='<SSM>', 'read.text.end.tag'='</SSM>')

Storage output matrix

Depending on the storage format of Kafka topics’ messages, the need for replication to a different cluster, and the specific data analysis requirements, there exists a guideline on how to effectively utilize converters for both sink and source operations. This guidance aims to optimize performance and minimize unnecessary CPU and memory usage.

Projections

Currently, the connector does not offer support for SQL projection; consequently, anything other than a SELECT * query is disregarded. The connector will faithfully write all the record fields to Kafka exactly as they are.

Ordering

The sink employs zero-padding in file names to ensure precise ordering, leveraging optimizations offered by the GCS API, guaranteeing the accurate sequence of files.

When using the GCS source alongside the GCS sink, the connector can adopt the same ordering method, ensuring data processing follows the correct chronological order. However, there are scenarios where GCS data is generated by applications that do not maintain lexical file name order.

In such cases, to process files in the correct sequence, the source needs to list all files in the bucket and sort them based on their last modified timestamp. To enable this behavior, set the connect.gcpstorage.source.ordering.type to LastModified. This ensures that the source correctly arranges and processes the data based on the timestamps of the files.

Throttling

To limit the number of file names the source reads from GCS in a single poll. The default value, if not specified, is 1000:

BATCH = 100

To limit the number of result rows returned from the source in a single poll operation, you can use the LIMIT clause. The default value, if not specified, is 10000.

LIMIT 10000

File Extension Filtering

The GCP Storage Source Connector allows you to filter the files to be processed based on their extensions. This is controlled by two properties: connect.gcpstorage.source.extension.excludes and connect.gcpstorage.source.extension.includes.

Excluding File Extensions

The connect.gcpstorage.source.extension.excludes property is a comma-separated list of file extensions to exclude from the source file search. If this property is not configured, all files are considered. For example, to exclude .txt and .csv files, you would set this property as follows:

connect.gcpstorage.source.extension.excludes=txt,csv

Including File Extensions

The connect.gcpstorage.source.extension.includes property is a comma-separated list of file extensions to include in the source file search. If this property is not configured, all files are considered. For example, to include only .json and .xml files, you would set this property as follows:

connect.gcpstorage.source.extension.includes=json,xml

Note: If both connect.gcpstorage.source.extension.excludes and connect.gcpstorage.source.extension.includes are set, the connector first applies the exclusion filter and then the inclusion filter.

Properties

The PROPERTIES clause is optional and adds a layer of configuration to the connector. It enhances versatility by permitting the application of multiple configurations (delimited by ‘,’). The following properties are supported:

Authentication

The connector offers two distinct authentication modes:

• Default: This mode relies on the default GCP authentication chain, simplifying the authentication process.

• File: This mode uses a local (to the connect worker) path for a file containing GCP authentication credentials.

• Credentials: In this mode, explicit configuration of a GCP Credentials string is required for authentication.

The simplest example to configure in the connector is the “Default” mode, as this requires no other configuration.

connect.gcpstorage.gcp.auth.mode=Default

When selecting the “Credentials” mode, it is essential to provide the necessary credentials. Alternatively, if you prefer not to configure these properties explicitly, the connector will follow the credentials retrieval order as described here.

Here’s an example configuration for the “Credentials” mode:

connect.gcpstorage.gcp.auth.mode=Credentials
connect.gcpstorage.gcp.credentials=$GCP_CREDENTIALS
connect.gcpstorage.gcp.project.id=$GCP_PROJECT_ID

And here is an example configuration using the “File” mode:

connect.gcpstorage.gcp.auth.mode=File
connect.gcpstorage.gcp.file=/home/secure-stuff/gcp-read-credential.txt

Remember when using file mode the file will need to exist on every worker node in your Kafka connect cluster and be readable by the Kafka Connect process.

For enhanced security and flexibility when using the “Credentials” mode, it is highly advisable to utilize Connect Secret Providers. This approach ensures robust security practices while handling access credentials.

Backup and Restore

When used in tandem with the GCP Storage Sink Connector, the GCP Storage Source Connector becomes a powerful tool for restoring Kafka topics from GCP Storage. To enable this behavior, you should set store.envelope to true. This configuration ensures that the source expects the following data structure in GCP Storage:

{
  "key": <the message Key, which can be a primitive or a complex object>,
  "value": <the message Value, which can be a primitive or a complex object>,
  "headers": {
    "header1": "value1",
    "header2": "value2"
  },
  "metadata": {
    "offset": 0,
    "partition": 0,
    "timestamp": 0,
    "topic": "topic"
  }
}

When the messages are sent to Kafka, the GCP Storage Source Connector ensures that it correctly maps the key, value, headers, and metadata fields (including timestamp and partition) to their corresponding Kafka message fields. Please note that the envelope functionality can only be used with data stored in GCP Storage as Avro, JSON, or Parquet formats.

Partition Extraction

When the envelope feature is not in use, and data restoration is required, the responsibility falls on the connector to establish the original topic partition value. To ensure that the source correctly conveys the original partitions back to Kafka Connect during reads from the source, a partition extractor can be configured to extract this information from the GCP Storage object key.

To configure the partition extractor, you can utilize the connect.gcpstorage.source.partition.extractor.type property, which supports two options:

• hierarchical: This option aligns with the default format used by the sink, topic/partition/offset.json.

• regex: When selected, you can provide a custom regular expression to extract the partition information. Additionally, when using the regex option, you must also set the connect.gcpstorage.source.partition.extractor.regex property. It’s important to note that only one lookup group is expected. For an example of a regular expression pattern, please refer to the pattern used for hierarchical, which is:

(?i)^(?:.*)\/([0-9]*)\/(?:[0-9]*)[.](?:Json|Avro|Parquet|Text|Csv|Bytes)$

Option Reference