Snowflake Sink Connector for Confluent Cloud¶
The fully-managed Snowflake Sink connector for Confluent Cloud maps and persists events from Apache Kafka® topics directly to a Snowflake database. The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) data from Apache Kafka® topics. It ingests events from Kafka topics directly into a Snowflake database, exposing the data to services for querying, enrichment, and analytics.
Note
This is the Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see the Snowflake Connector for Kafka documentation.
Features¶
The Snowflake Sink connector provides the following features:
- Database authentication: Uses private key authentication.
- Client-side field level encryption (CSFLE) support: The connector supports CSFLE for sensitive data. For more information about CSFLE setup, see the connector configuration.
- Snowflake Ingestion Methods: The connector supports the Snowpipe (default) and Snowpipe Streaming for Kafka data ingestion methods. Using Snowpipe Streaming may provide a cost-benefit for your Snowflake project.
- Confluent Cloud provides version 2.1.2 of the fully-managed Snowflake Sink connector.
This version supports Snowflake schematization
(
snowflake.enable.schematization
). When set toTRUE
the connector provides schema detection and evolution when using Snowpipe Streaming for Kafka. The default value isFALSE
. For more information, see Schema detection and schema evolution. - Input data formats: The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) input data formats. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
- Select configuration properties: The following properties determine what metadata is included in the
RECORD_METADATA
column in the Snowflake database table.snowflake.metadata.createtime
: If this value is set tofalse
, theCreateTime
property value is omitted from the metadata in theRECORD_METADATA
column. The default value istrue
.snowflake.metadata.topic
: If this value is set tofalse
, thetopic
property value is omitted from the metadata in theRECORD_METADATA
column. The default value istrue
.snowflake.metadata.offset.and.partition
: If the value is set to false, theOffset
andPartition
property values are omitted from the metadata in theRECORD_METADATA
column. The default value istrue
.snowflake.metadata.all
: If the value is set to false, the metadata in theRECORD_METADATA
column is empty. The default value istrue
.
For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.
Limitations¶
Be sure to review the following information.
- For connector limitations, see Snowflake Sink Connector limitations.
- If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
- If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.
Target table naming guidelines¶
Note the following table naming guidelines and limitations:
The fully-managed Snowflake Sink connector allows you to configure
topic:table
name mapping. This feature is also supported by the self-managed Snowflake Sink connector.Snowflake itself has limitations on object (table) naming conventions. See Identifier Requirements for details.
Kafka is much more permissive with topic naming conventions. You are allowed to use Kafka topic names that break the table name mapping in the Confluent Cloud Snowflake Sink connector.
When a Kafka topic name does not conform to Snowflake’s table naming limitations (for example,
my-topic-name
), the connector will rename the topic to a safe name with an appended hash (for example,my_topic_name_021342
). A conforming topic name (for example,my_topic_name
) will send results to the expected table namedmy_topic_name
.If the connector needs to adjust the name of the table created for a Kafka topic, there is the potential for identical table names. For example, if you are reading data from Kafka topics
numbers+x
andnumbers-x
, the tables created for these topics will both be namedNUMBERS_X
. To avoid table name duplication, the connector appends a suffix to the table name. The suffix is an underscore followed by a generated hash.
Generate a Snowflake key pair¶
Before the connector can sink data to Snowflake, you need to generate a key pair. Snowflake authentication requires 2048-bit (minimum) RSA. You add the public key to a Snowflake user account. You add the private key to the connector configuration (when completing the Quick Start instructions).
Note
- This procedure generates an unencrypted private key. You can generate and use an encrypted key. If you generate an encrypted key, add the passphrase to your connector configuration in addition to the private key. For information about generating an encrypted key, see Using Key Pair Authentication in the Snowflake documentation.
- When you use a non-encrypted private key, you might see the following configuration validation error. Check whether your private key is valid or consider using an encrypted private key.
Creating the key pair¶
Complete the following steps to generate a key pair.
Generate a private key using OpenSSL.
openssl genrsa -out snowflake_key.pem 2048
Generate the public key referencing the private key.
openssl rsa -in snowflake_key.pem -pubout -out snowflake_key.pub
List the generated Snowflake key files.
ls -l snowflake_key* -rw-r--r-- 1 1679 Jun 8 17:04 snowflake_key.pem -rw-r--r-- 1 451 Jun 8 17:05 snowflake_key.pub
Show the contents of the public key file.
cat snowflake_key.pub -----BEGIN PUBLIC KEY----- MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA2zIuUb62JmrUAMoME+SX vsz9KUCp/cC+Y+kTGfYB3jRDQ06O0UT+yUKMO/KWuc0dUxZ8s9koW5l/n+TBfxIQ ... omitted 1tD+Ktd/CTXPoVEI2tgCC9Avf/6/9HU3IpV0gL8SZ8U0N5ot4Uw+CSYB3JjMagEG bBWZ8Qc26pFk7Fd17+ykH6rEdLeQ9OElc0ZruVwSsa4AxaZOT+rqCCP7FQPzKTtA JQIDAQAB -----END PUBLIC KEY-----
Copy the key, capturing only the portion between
--BEGIN PUBLIC KEY--
and--END PUBLIC KEY--
. You can do this manually, or by using the following command:grep -v "BEGIN PUBLIC" snowflake_key.pub | grep -v "END PUBLIC"|tr -d '\r\n'
You will add this to a new user in Snowflake. In the following section, you create a user and add the public key.
Creating a user and adding the public key¶
Open your Snowflake project. Complete the following steps to create a user account and add the public key to this account.
Go to the Worksheets panel and switch to the SECURITYADMIN role.
Important
Be sure to set the SECURITYADMIN role in the Worksheets panel (shown below) and not by using the user account drop-down selection. For additional information, see User Management.
Run the following query in Worksheets to create a user, and add the public key copied earlier.
CREATE USER confluent RSA_PUBLIC_KEY='<public-key>';
Make sure to add the public key as a single line in the statement.The following shows what this looks like in Snowflake Worksheets:
Tip
If you did not set the role to SECURITYADMIN, or if you set the role using the user account drop-down menu, an SQL access control error is displayed.
SQL access control error: Insufficient privileges to operate on account '<account-name>'
Configuring user privileges¶
Complete the following steps to set the correct privileges for the user added.
For example: Suppose you want to send Apache Kafka® records to a database named
PRODUCTION
using the schema PUBLIC
. The following shows the required
queries to configure the necessary user privileges.
// Use a role that can create and manage roles and privileges:
use role securityadmin;
// Create a Snowflake role with the privileges to work with the connector
create role kafka_connector_role;
// Grant privileges on the database:
grant usage on database PRODUCTION to role kafka_connector_role;
// Grant privileges on the schema:
grant usage on schema PRODUCTION.PUBLIC to role kafka_connector_role;
grant create table on schema PRODUCTION.PUBLIC to role kafka_connector_role;
grant create stage on schema PRODUCTION.PUBLIC to role kafka_connector_role;
grant create pipe on schema PRODUCTION.PUBLIC to role kafka_connector_role;
// Grant the custom role to an existing user:
grant role kafka_connector_role to user confluent;
// Make the new role the default role:
alter user confluent set default_role=kafka_connector_role;
Note
Grant privileges directly to the role to work with the connector. Privileges do not inherit from the role hierarchy.
Extracting the private key¶
You add the private key to your Snowflake connector configuration. Extract the key and put it in a safe place until you set up your connector.
List the generated Snowflake key files.
ls -l snowflake_key* -rw-r--r-- 1 1679 Jun 8 17:04 snowflake_key.pem -rw-r--r-- 1 451 Jun 8 17:05 snowflake_key.pub
Show the contents of the private key file.
cat snowflake_key.pem -----BEGIN RSA PRIVATE KEY----- MIIEpQIBAAKCAQEA2zIuUb62JmrUAMoME+SXvsz9KUCp/cC+Y+kTGfYB3jRDQ06O 0UT+yUKMO/KWuc0dUxZ8s9koW5l/n+TBfxIQx+24C2+l9t3TxxaLdf/YCgQwKNR9 dO9/c+SkX8NfcwUynGEo3wpmdb4hp0X9TfWKX9vG//zK2tndmMUrFY5OcGSSVJYJ Wv3gk04sVxhINo5knpgZoUVztxcRLm/vNvIX1tD+Ktd/CTXPoVEI2tgCC9Avf/6/ 9HU3IpV0gL8SZ8U0N5ot4Uw+CSYB3JjMagEGbBWZ8Qc26pFk7Fd17+ykH6rEdLeQ ... omitted UfrYj7+p03yVflrsB+nyuPETnRJx41b01GrwJk+75v5EIg8U71PQDWfy1qOrUk/d 9u25iaVRzi6DFM0ppE76Lh72SKy+m0iEZIXWbV9q6vf46Oz1PrtffAzyi4pyJbe/ ypQ53f0CgYEA7rE6Dh0tG7EnYfFYrnHLXFC2aVtnkfCMIZX/VIZPX82VGB1mV43G qTDQ/ax1tit6RHDBk7VU4Xn545Tgj1z6agYPvHtkhxYTq50xVBXr/xwlMnzUZ9s3 VjGpMYQANm2seleV6/si54mT4TkUyB7jMgWdFsewtwF60quvxmiA9RU= -----END RSA PRIVATE KEY-----
Copy the key. You will add it to the connector configuration. Copy only the part of the key between
--BEGIN RSA PRIVATE KEY--
and--END RSA PRIVATE KEY--
). You can do this manually or you can use the following command:grep -v "BEGIN RSA PRIVATE KEY" snowflake_key.pem | grep -v "END RSA PRIVATE KEY"|tr -d '\r\n'
Save the key to use later when you are completing the Quick Start steps. Or, you can complete the previous step when you actually need to get the key for the connector config.
Quick Start¶
Use this quick start to get up and running with the Confluent Cloud Snowflake Sink connector. The quick start provides the basics of selecting the connector and configuring it to consume data from Kafka and persist the data to a Snowflake database.
- Prerequisites
- Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud.
- The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
- Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
- A Snowflake account and key pair to use for connector authentication with the Snowflake database.
- The user created must be granted privileges in Snowflake to modify the database and schema. For more information, see Access Control Privileges.
- The data system the sink connector is connecting to should be in the same region as your Confluent Cloud cluster. If you use a different region or cloud platform, be aware that you may incur additional data transfer charges. Contact your Confluent account team or Confluent Support if you need to use Confluent Cloud and connect to a data system that is in a different region or on a different cloud platform.
- For networking considerations, see Networking and DNS. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.
- Kafka cluster credentials. The following lists the different ways you can provide credentials.
- Enter an existing service account resource ID.
- Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.
- Create a Confluent Cloud API key and secret. To create a key and secret, you can use confluent api-key create or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.
Using the Confluent Cloud Console¶
Step 1: Launch your Confluent Cloud cluster¶
See the Quick Start for Confluent Cloud for installation instructions.
Step 2: Add a connector¶
In the left navigation menu, click Connectors. If you already have connectors in your cluster, click + Add connector.
Step 4: Enter the connector details¶
Note
- Ensure you have all your prerequisites completed.
- The example commands use Confluent CLI version 2. For more information see, Confluent CLI v2.
At the Add Snowflake Sink Connector screen, complete the following:
If you’ve already populated your Kafka topics, select the topics you want to connect from the Topics list.
To create a new topic, click +Add new topic.
- Select the way you want to provide Kafka Cluster credentials. You can
choose one of the following options:
- My account: This setting allows your connector to globally access everything that you have access to. With a user account, the connector uses an API key and secret to access the Kafka cluster. This option is not recommended for production.
- Service account: This setting limits the access for your connector by using a service account. This option is recommended for production.
- Use an existing API key: This setting allows you to specify an API key and a secret pair. You can use an existing pair or create a new one. This method is not recommended for production environments.
- Click Continue.
Under Snowflake Credentials, enter the following:
Connection URL: Enter the URL for accessing your Snowflake account. Use the format
https://<account-locator>.<region-ID>.snowflakecomputing.com
. For example:https://abcd12345.us-east-1.snowflakecomputing.com
The
https://
and443
port number are optional. For more information, see Account Locator in a Region. Do not use the region ID if your account is in the AWS US West region and you are using AWS PrivateLink.Connection user name: Enter the user name created earlier. Note that if using the SNOWPIPE_STEAMING ingestion method, you must add the Snowflake role name to use.
Private key: Enter the private key created earlier as a single line. Enter only the part of the key between
--BEGIN RSA PRIVATE KEY--
and--END RSA PRIVATE KEY--
.Snowflake role: Enter the role the connector uses when inserting rows into the table. This property is required if the active ingestion method is SNOWPIPE_STREAMING. If the ingestion method is SNOWPIPE this property is not required and uses the default role.
Database name: Enter the database name containing the table to insert rows into.
Schema name: Schema name that contains the table to insert rows into.
Topics to tables mapping:
Click Continue.
Note
Configuration properties that are not shown in the Cloud Console use the default values. See Configuration Properties for configuration property values and descriptions.
Select the Ingestion method: This property defaults to
SNOWPIPE
. SelectSNOWPIPE_STREAMING
. Snowpipe Streaming for Kafka may provide a cost-benefit for your Snowflake project.Select the Input Kafka record value format (data coming from the Kafka topic): JSON (schemaless), AVRO, JSON_SR (JSON Schema), or PROTOBUF. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
(Optional) Enable Client-Side Field Level Encryption for data decryption. Specify a Service Account to access the Schema Registry and associated encryption rules or keys with that schema. Select the connector behavior (
ERROR
orNONE
) on data decryption failure. If set toERROR
, the connector fails and writes the encrypted data in the DLQ. If set toNONE
, the connector writes the encrypted data in the target system without decryption. For more information on CSFLE setup, see Manage CSFLE for connectors.Show advanced configurations
Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.
Connection details
Select the Input Kafka record key format. Options are JSON (schemaless), AVRO, JSON_SR (JSON Schema), PROTOBUF, or STRING. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
Whether or not to include “createtime” in metadata: If the value is set to
FALSE
, theCreateTime
property value is omitted from the metadata in theRECORD_METADATA
column. The default value isTRUE
.Whether or not to include “topic” in metadata: If the value is set to
FALSE
, the topic property value is omitted from the metadata in theRECORD_METADATA
column. The default value isTRUE
.Whether or not to include “offset” and “partition” in metadata: If the value is set to
FALSE
, the Offset and Partition property values are omitted from the metadata in theRECORD_METADATA
column. The default value isTRUE
.Whether or not to include metadata column: If the value is set to
FALSE
, the metadata in theRECORD_METADATA
column is completely empty. The default value isTRUE
.Enable schematization: Set to
TRUE
to enable schema detection and evolution when using Snowpipe Streaming for Kafka. The default value isFALSE
. For more information, see Schema detection and schema evolution.The time in seconds to flush cached data: Number of seconds between buffer flushes, where the flush is from the Kafka’s memory cache to the internal stage. The default value is 120 seconds and minimum value is 10 seconds. The connector also uses the following number of records property and size of the buffer property to determine when to flush cached data. When one of these property values is reached, the connector flushes Kafka records to Snowflake.
The number of records to flush cached data: The number of records between buffer flushes, where the flush is from the Kafka’s memory cache to the internal stage. The default and minimum value is 10,000 records. The connector also uses the previous time in seconds property and the following size of the buffer property to determine when to flush cached data. When one of these property values is reached, the connector flushes Kafka records to Snowflake.
The size of the connector record buffer: The buffer size defaults to 5000000 bytes (5 MB). The records are compressed when written to Snowflake.
When a flush is triggered when the cache reaches 5 MB, you might expect to see a 5 MB data file in Snowflake. You will see a much smaller file (for example, ~150 KB). This is because the 5 MB of flushed data is converted from Java to UTF. This conversion reduces the file size by 50 percent. The file is then compressed with gzip, which further reduces the file size by 95 percent.
Error Tolerance: This property defaults to
all
which allows the connector to ignore all record processing errors. Set this tonone
to have the connector stop on record errors, which is the default Connect framework configuration setting.Key Subject Name Strategy: Determines how to construct the subject name under which the key schema is registered with Schema Registry. This property defaults to
TopicNameStrategy
. Set this property toRecordNameStrategy
to derive the subject name from the record name. For more information, see Subject name strategy.Value Subject Name Strategy: Determines how to construct the subject name under which the value schema is registered with Schema Registry. This property defaults to
TopicNameStrategy
. Set this property toRecordNameStrategy
to derive the subject name from the record name. For more information, see Subject name strategy.
Note
See the Single Message Transforms (SMT) documentation for details about transforms and predicates. See Unsupported transformations for a list of SMTs that are not supported with this connector.
Click Continue.
Based on the number of topic partitions you select, you will be provided with a recommended number of tasks. One task can handle up to 100 partitions.
- To change the number of recommended tasks, enter the number of
tasks for the connector to use
in the Tasks field. Each task is limited to a number of topic
partitions based on the
buffer.size.bytes
property value. For example, a10
MB buffer size is limited to 50 topic partitions, a20
MB buffer is limited to 25 topic partitions,50
MB buffer is limited to 10 topic partitions, and a100
MB buffer to 5 topic partitions. - Click Continue.
Step 5: Check Snowflake¶
After the connector is running, verify that messages are populating your Snowflake database table.
For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.
Tip
When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.
For Snowflake troubleshooting, see Troubleshooting Issues in the Snowflake documentation.
Note
- The Snowflake Sink connector does not remove Snowflake pipes when a connector is deleted. For instructions to manually clean up Snowflake pipes, see Dropping Pipes.
- Snowflake Snowpipe failure can prevent messages from showing up in the target table despite being successfully written by the Snowflake Sink connector. If this happens, check the Snowflake COPY_HISTORY view, internal stage, or table stage to find the message and associated error. For more on the workflow of Snowflake Sink connector, see Workflow for the Kafka Connector.
Using the Confluent CLI¶
Complete the following steps to set up and run the connector using the Confluent CLI.
Note
Make sure you have all your prerequisites completed.
Step 1: List the available connectors¶
Enter the following command to list available connectors:
confluent connect plugin list
Step 2: List the connector configuration properties¶
Enter the following command to show the connector configuration properties:
confluent connect plugin describe <connector-plugin-name>
The command output shows the required and optional configuration properties.
Step 3: Create the connector configuration file¶
Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties.
{
"connector.class": "SnowflakeSink",
"name": "<connector-name>",
"kafka.auth.mode": "KAFKA_API_KEY",
"kafka.api.key": "<my-kafka-api-key>",
"kafka.api.secret": "<my-kafka-api-secret>",
"topics": "<topic1>, <topic2>",
"input.data.format": "JSON",
"snowflake.url.name": "https://wm83168.us-central1.gcp.snowflakecomputing.com:443",
"snowflake.user.name": "<login-username>",
"snowflake.private.key": "<private-key>",
"snowflake.database.name": "<database-name>",
"snowflake.schema.name": "<schema-name>",
"tasks.max": "1"
}
Note the following required property definitions:
"connector.class"
: Identifies the connector plugin name."name"
: Enter a name for your connector.
"kafka.auth.mode"
: Identifies the connector authentication mode you want to use. There are two options:SERVICE_ACCOUNT
orKAFKA_API_KEY
(the default). To use an API key and secret, specify the configuration propertieskafka.api.key
andkafka.api.secret
, as shown in the example configuration (above). To use a service account, specify the Resource ID in the propertykafka.service.account.id=<service-account-resource-ID>
. To list the available service account resource IDs, use the following command:confluent iam service-account list
For example:
confluent iam service-account list Id | Resource ID | Name | Description +---------+-------------+-------------------+------------------- 123456 | sa-l1r23m | sa-1 | Service account 1 789101 | sa-l4d56p | sa-2 | Service account 2
"topics"
: Enter one topic or multiple comma-separated topics."input.data.format"
: Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf)."snowflake.url.name"
: Enter the URL for accessing your Snowflake account. Use the formathttps://<account_locator>.<region_id>.<cloud_provider>.snowflakecomputing.com:443
. Thehttps://
and443
port number are optional. For more information, see Account Locator in a Region. Do not use the region ID if your account is in the AWS US West region and you are not using AWS PrivateLink."snowflake.user.name"
: Enter the user name created earlier. Note that if using the SNOWPIPE_STEAMING ingestion method, you must add the"snowflake.role.name"
property. See Configuration Properties for all property values and descriptions."snowflake.private.key"
:- Enter the private key created earlier as a single line.
- Enter only the part of the key between
--BEGIN RSA PRIVATE KEY--
and--END RSA PRIVATE KEY--
.
"snowflake.database.name"
: Enter the database name containing the table to insert rows into."snowflake.schema.name"
: Enter the Snowflake Schema name that contains the table to insert rows into."tasks.max"
: Enter the number of tasks for the connector. Refer to Confluent Cloud connector limitations for additional information.
The following are optional properties to include in the configuration. These
properties affect what metadata is included in the RECORD_METADATA
column in
the Snowflake database table.
"snowflake.metadata.createtime"
: If this value is set to"false"
, theCreateTime
property value is omitted from the metadata in theRECORD_METADATA
column. The default value is"true"
."snowflake.metadata.topic"
: If this value is set to"false"
, thetopic
property value is omitted from the metadata in theRECORD_METADATA
column. The default value is"true"
."snowflake.metadata.offset.and.partition"
: If the value is set to"false"
, theOffset
andPartition
property values are omitted from the metadata in theRECORD_METADATA
column. The default value is"true"
."snowflake.metadata.all"
: If the value is set to"false"
, the metadata in theRECORD_METADATA
column is empty. The default value is"true"
.
Note
(Optional) To enable CSFLE for data encryption, specify the following properties:
csfle.enabled
: Flag to indicate whether the connector honors CSFLE rules.sr.service.account.id
: A Service Account to access the Schema Registry and associated encryption rules or keys with that schema.csfle.onFailure
: Configures the connector behavior (ERROR
orNONE
) on data decryption failure. If set toERROR
, the connector fails and writes the encrypted data in the DLQ. If set toNONE
, the connector writes the encrypted data in the target system without decryption.
For more information on CSFLE setup, see Manage CSFLE for connectors.
Set the following properties that determine when records are flushed to Snowflake. Records are flushed when the first one of these values is met. For example: The interval to flush records is set to 120
seconds. This time interval has elapsed from the last flush, but the number of records value has not been met. Records are flushed because the time interval tripped before the records property.
"buffer.flush.time"
: The time (in seconds) the connector waits before flushing cached records to Snowflake. The default value is120
seconds, and the minimum value is 10 seconds. You can configure a longer time interval."buffer.count.records"
: Records are cached in a buffer (per partition) before they are flushed to Snowflake. The default value is10000
. This is the minimum number of records. You can configure this to a larger number of records. Records are flushed to Snowflake when the number of records reaches the property value.Caution
Increasing
buffer.flush.time
andbuffer.count.records
above default values may cause the connect worker to run out of memory if record volumes are very high."buffer.size.bytes"
: Records are cached in a buffer (per partition) before being written to Snowflake as data files. The buffer size defaults to5000000
bytes (5 MB). This is the minimum cache size value. Records are flushed to Snowflake when this buffer reaches the property size.Note
When a flush is triggered when the cache reaches 5 MB, you might expect to see a 5 MB data file in Snowflake. You will see a much smaller file (for example, ~150 KB). This is because the 5 MB of flushed data is converted from Java to UTF. This conversion reduces the file size by 50 percent. The file is then compressed with gzip, which further reduces the file size by 95 percent.
"tasks.max"
: Enter the maximum number of tasks that the connector will use. Each task is limited to a number of topic partitions based on thebuffer.size.bytes
property value. For example, a10
MB buffer size is limited to 50 topic partitions, a20
MB buffer is limited to 25 topic partitions,50
MB buffer is limited to 10 topic partitions, and a100
MB buffer to 5 topic partitions.
Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI. See Unsupported transformations for a list of SMTs that are not supported with this connector.
See Configuration Properties for all property values and descriptions.
Step 4: Load the properties file and create the connector¶
Enter the following command to load the configuration and start the connector:
confluent connect cluster create --config-file <file-name>.json
For example:
confluent connect cluster create --config-file snowflake-sink.json
Example output:
Created connector confluent-snowflake lcc-ix4dl
Step 5: Check the connector status¶
Enter the following command to check the connector status:
confluent connect cluster list
Example output:
ID | Name | Status | Type
+-----------+-------------------------+---------+------+
lcc-ix4dl | confluent-snowflake | RUNNING | sink
Step 6: Check Snowflake¶
After the connector is running, verify that records are populating your Snowflake database.
For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.
Tip
When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.
For Snowflake troubleshooting, see Troubleshooting Issues in the Snowflake documentation.
Note
- The Snowflake Sink connector does not remove Snowflake pipes when a connector is deleted. For instructions to manually clean up Snowflake pipes, see Dropping Pipes.
- Snowflake Snowpipe failure can prevent messages from showing up in the target table despite being successfully written by the Snowflake Sink connector. If this happens, check the Snowflake COPY_HISTORY view, internal stage, or table stage to find the message and associated error. For more on the workflow of Snowflake Sink connector, see Workflow for the Kafka Connector.
Configuration Properties¶
Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.
Which topics do you want to get data from?¶
topics
Identifies the topic name or a comma-separated list of topic names.
- Type: list
- Importance: high
Schema Config¶
schema.context.name
Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.
- Type: string
- Default: default
- Importance: medium
Input messages¶
input.data.format
Sets the input Kafka record value format. Valid entries are JSON, AVRO, JSON_SR, or PROTOBUF. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF
- Type: string
- Default: JSON
- Importance: high
input.key.format
Sets the input Kafka record key format. Valid entries are AVRO, JSON_SR, PROTOBUF, STRING or JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF
- Type: string
- Default: STRING
- Valid Values: AVRO, JSON, JSON_SR, PROTOBUF, STRING
- Importance: high
key.converter.reference.subject.name.strategy
Set the subject reference name strategy for key. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.
- Type: string
- Default: DefaultReferenceSubjectNameStrategy
- Importance: high
value.converter.reference.subject.name.strategy
Set the subject reference name strategy for value. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.
- Type: string
- Default: DefaultReferenceSubjectNameStrategy
- Importance: high
How should we connect to your data?¶
name
Sets a name for your connector.
- Type: string
- Valid Values: A string at most 64 characters long
- Importance: high
Kafka Cluster credentials¶
kafka.auth.mode
Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode.
- Type: string
- Default: KAFKA_API_KEY
- Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
- Importance: high
kafka.api.key
Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.
- Type: password
- Importance: high
kafka.service.account.id
The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.
- Type: string
- Importance: high
kafka.api.secret
Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.
- Type: password
- Importance: high
How should we connect to your Snowflake database?¶
snowflake.url.name
The URL for accessing your Snowflake account, in the form of https://<account_name>.<region_id>.snowflakecomputing.com:443. Note that the https:// and port number are optional. The region ID is not used if your account is in the AWS US West region and you are not using AWS PrivateLink.
- Type: string
- Importance: high
snowflake.user.name
User login name for the Snowflake account
- Type: string
- Importance: high
snowflake.private.key
The private key to authenticate the user. Include only the key, not the header or footer. If the key is split across multiple lines, remove the line breaks. You can provide either an unencrypted key or an encrypted key. If you use an encrypted key, provide the snowflake.private.key.passphrase parameter so Snowflake can decrypt the key. Use this parameter only if the snowflake.private.key parameter value is encrypted.
- Type: password
- Importance: high
snowflake.database.name
The name of the database that contains the table to insert rows into.
- Type: string
- Importance: high
snowflake.role.name
Access control role to use when inserting the rows into the table. If Ingestion method is Snowpipe_Streaming Ingestion then it’s required. If Ingestion method is Snowpipe then its not required and use default role
- Type: string
- Importance: low
Database details¶
snowflake.schema.name
The name of the schema that contains the table to insert rows into.
- Type: string
- Importance: high
snowflake.topic2table.map
Map of topics to tables (optional). Format : comma-separated tuples, e.g. <topic-1>:<table-1>,<topic-2>:<table-2>,…
- Type: string
- Importance: high
Snowflake connection¶
snowflake.ingestion.method
Choose the preferred ingestion method. The connector supports the SNOWPIPE (default) and SNOWPIPE_STREAMIMG for Kafka data ingestion. Using SNOWPIPE_STREAMING may provide a cost-benefit for your Snowflake project.
- Type: string
- Default: SNOWPIPE
- Importance: high
Connection details¶
snowflake.private.key.passphrase
If snowflake.private.key is encrypted, this passphrase is used to decrypt the key. If the value of this parameter is not empty, Kafka uses this phrase to try to decrypt the private key.
- Type: password
- Default: [hidden]
- Importance: medium
snowflake.metadata.createtime
If the value is set to FALSE, the CreateTime property value is omitted from the metadata in the RECORD_METADATA column. The default value is TRUE.
- Type: boolean
- Default: true
- Importance: medium
snowflake.metadata.topic
If the value is set to FALSE, the topic property value is omitted from the metadata in the RECORD_METADATA column. The default value is TRUE.
- Type: boolean
- Default: true
- Importance: medium
snowflake.metadata.offset.and.partition
If the value is set to FALSE, the Offset and Partition property values are omitted from the metadata in the RECORD_METADATA column. The default value is TRUE.
- Type: boolean
- Default: true
- Importance: medium
snowflake.metadata.all
If the value is set to FALSE, the metadata in the RECORD_METADATA column is completely empty. The default value is TRUE.
- Type: boolean
- Default: true
- Importance: medium
snowflake.enable.schematization
Specify to TRUE to enable schema detection and evolution for Kafka Connector with Snowpipe Streaming. The default value is FALSE
- Type: boolean
- Default: false
- Importance: medium
buffer.flush.time
Number of seconds between buffer flushes, where the flush is from the Kafka’s memory cache to the internal stage. The default value is 120 seconds. Minimum value allowed is 10 for snowflake.ingestion.method=SNOWPIPE, and 1 for snowflake.ingestion.method=SNOWPIPE_STREAMING. The connector uses buffer.count.records and buffer.size.bytes=10,000,000 (10MB) as well. Whichever comes first, the connector will flush Kafka records to Snowflake.
- Type: long
- Default: 120
- Valid Values: Value must be greater than 10 in SNOWPIPE method OR greater than 1 in SNOWPIPE_STREAMING method
- Importance: low
buffer.count.records
Number of records between buffer flushes, where the flush is from the Kafka’s memory cache to the internal stage. The default and minimum value is 10,000 records. The connector uses buffer.flush.time and buffer.size.bytes=10,000,000 (10MB) as well. Whichever comes first, the connector will flush Kafka records to Snowflake.
- Type: long
- Default: 10000
- Valid Values: [10000,…]
- Importance: low
buffer.size.bytes
Kafka records are cached in a buffer (per partition) before being written to Snowflake as data files. The buffer size defaults to 10000000 bytes (10 MB). The records are compressed when written to Snowflake. Because of the compression, the size of the cached records buffer may be larger that the size of the resulting data files created in Snowflake.
- Type: long
- Default: 10000000
- Valid Values: [10000000,…,100000000]
- Importance: low
Error handling¶
errors.tolerance
Use this property if you would like to configure the connector’s error handling behavior differently from the Connect framework’s.
- Type: string
- Default: all
- Importance: low
Organize my data by…¶
key.subject.name.strategy
Determines how to construct the subject name under which the key schema is registered with Schema Registry.
- Type: string
- Default: TopicNameStrategy
- Valid Values: RecordNameStrategy, TopicNameStrategy
- Importance: medium
value.subject.name.strategy
Determines how to construct the subject name under which the value schema is registered with Schema Registry.
- Type: string
- Default: TopicNameStrategy
- Valid Values: RecordNameStrategy, TopicNameStrategy
- Importance: medium
Consumer configuration¶
max.poll.interval.ms
The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).
- Type: long
- Default: 300000 (5 minutes)
- Valid Values: [60000,…,1800000] for non-dedicated clusters and [60000,…] for dedicated clusters
- Importance: low
max.poll.records
The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.
- Type: long
- Default: 500
- Valid Values: [1,…,500] for non-dedicated clusters and [1,…] for dedicated clusters
- Importance: low
Number of tasks for this connector¶
tasks.max
The number of tasks for the connector. Each task is limited to a number of topic partitions based on the buffer.size.bytes configuration, e.g., 10 MB -> 50 Topic Partitions, 20 MB-> 25 Topic Partitions, 50 MB -> 10 Topic Partitions, and 100 MB -> 5 Topic Partitions.
- Type: int
- Valid Values: [1,…]
- Importance: high
Troubleshooting¶
For Snowflake troubleshooting, see Troubleshooting Issues in the Snowflake documentation.
Tip
When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.
Suggested Reading¶
The following blog post provides an introduction to the Snowflake Sink connector and a scenario walkthrough.
Blog post: Announcing the Snowflake Sink connector for Apache Kafka in Confluent Cloud
Next Steps¶
For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.