Set up the Openflow Connector for SQL Server (CDC)¶
Note
This connector is subject to the Snowflake Connector Terms.
This topic describes how to set up the Openflow Connector for SQL Server (CDC).
For information on the incremental load process, see Incremental replication.
Prerequisites¶
Before setting up the connector, ensure that you have completed the following prerequisites:
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Ensure that you have reviewed About Openflow Connector for SQL Server (CDC).
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Ensure that you have reviewed Supported SQL Server versions.
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Ensure that you have set up your runtime deployment. For more information, see the following topics:
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If you use Openflow - Snowflake Deployments, ensure that you have reviewed configuring required domains and have granted access to the required domains for the SQL Server connector.
Set up your SQL Server instance¶
Before setting up the connector, perform the following tasks in your SQL Server environment:
Note
You must perform these tasks as a database administrator.
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Enable Change Data Capture on the databases and tables that you plan to replicate:
Note
Run the
sp_cdc_enable_tableprocedure for every table that you plan to replicate. Runsp_cdc_enable_dbonce per database.The connector requires that CDC is enabled on the databases and tables before replication starts. You can also enable CDC on additional tables while the connector is running.
Note
Platform-specific variants for enabling CDC at the database level. The
sp_cdc_enable_tablecall shown above is the same on every platform; only the database-level enable procedure differs.-
AWS RDS for SQL Server. You can’t call
sys.sp_cdc_enable_dbdirectly on RDS because RDS doesn’t expose thesysadminserver role. Use the RDS-provided wrapper instead:See Using change data capture for Amazon RDS for SQL Server. CDC isn’t supported on the Web edition of RDS for SQL Server.
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Google Cloud SQL for SQL Server. You can’t call
sys.sp_cdc_enable_dbdirectly. Use the Cloud SQL-provided wrapper instead:See Enable change data capture (CDC) on Cloud SQL for SQL Server. Cloud SQL for SQL Server currently offers SQL Server 2017, 2019, and 2022 only.
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Azure SQL Database (single database). Use the standard
sys.sp_cdc_enable_dbprocedure. On the DTU-based purchasing model, CDC requires the S3 service tier or higher (CDC isn’t supported on Basic, S0, S1, or S2). On the vCore-based purchasing model, CDC is supported on any tier. See Change data capture with Azure SQL Database. -
Azure SQL Managed Instance. Use the standard
sys.sp_cdc_enable_dbprocedure. Enabling CDC requires membership in thesysadminserver role.
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Create a login for the SQL Server instance:
This login is used to create users for the databases you plan to replicate.
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Create a user for each database you are replicating by running the following SQL Server command in each database:
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Grant the required permissions to the user for each database that you are replicating.
Add the user to the
db_datareaderrole and grant SELECT on thecdcschema so the connector can read both the source tables and the CDC change tables:Run these commands in each database that you plan to replicate.
Note
These permissions give the connector read access to every user table in the database. To scope access more tightly, grant
SELECTonly on the specific tables being replicated and onSCHEMA::cdcinstead of adding the user to thedb_datareaderrole. -
Deploy the Openflow CDC wrapper procedures so the connector can manage capture instances and apply source schema changes autonomously. For more information, see Deploy the Openflow CDC wrapper procedures.
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(Optional) Grant the VIEW DEFINITION privilege on the User Defined Data Types (UDDT).
If your tables contain columns that use User Defined Data Types (UDDT), and the UDDT is owned by a different user than the connector user, you must grant the VIEW DEFINITION permission to the connector user as shown in the following SQL Server example:
Without this permission, columns using UDDT are silently excluded from replication.
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(Optional) Configure SSL connection.
If you use an SSL connection to connect SQL Server, create the root certificate for your database server. This is required when configuring the connector.
Deploy the Openflow CDC wrapper procedures¶
The connector applies supported source table schema changes (DDL) without stopping replication or requiring a manual re-snapshot. To do this, the connector manages SQL Server capture instances autonomously: when a tracked table’s schema changes, the connector creates a new capture instance for the updated schema and drops the old one after it finishes the transition. For an overview of the process, see Schema changes.
Creating and dropping capture instances normally requires db_owner. Rather than granting the
connector that level of access, deploy a small set of wrapper procedures that perform these
operations on the connector’s behalf and grant the connector permission to run only those two
procedures.
This design has the following properties:
- The connector can run only the two wrapper procedures. For capture-instance management, the
connector is granted
EXECUTEon onlydbo.sf_openflow_cdc_enable_tableanddbo.sf_openflow_cdc_disable_table. It doesn’t holddb_ownerand can’t call the underlyingsys.sp_cdc_enable_tableorsys.sp_cdc_disable_tableprocedures directly. The wrapper procedures run withEXECUTE AS OWNER, so they supply the elevated privileges only for the specific, audited operation. - Every operation is recorded in an audit table. Each invocation of a wrapper procedure writes an
attemptrow to thedbo.openflow_cdc_audittable before it calls the engine, then asuccessorfailurerow (including the SQL Server error number and message on failure) after the call. The rows are append-only: the wrappers never update or delete audit rows.
Deploy the procedures as a database administrator. Run the following scripts, in order, in each
CDC-enabled database being replicated. Run them as a principal that already holds
db_owner.
Note
Perform these tasks as a database administrator, after creating the connector’s database user as described in Set up your SQL Server instance.
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openflow_cdc_audit_setup.sql: Creates the append-onlydbo.openflow_cdc_audittable that the wrapper procedures write to. -
sf_openflow_cdc_enable_table.sql: Creates the wrapper procedure that the connector calls to add a new capture instance during a schema transition. -
sf_openflow_cdc_disable_table.sql: Creates the wrapper procedure that the connector calls to drop the old capture instance after a schema transition completes. -
openflow_cdc_grants.sql: Grants the connector’s database user permission to run the two wrapper procedures and to read the CDC metadata, change tables, and audit trail. Replace<user_name>with the connector’s database user created in Set up your SQL Server instance, then run the script.
Note
The connector also needs SELECT on each replicated source table. SQL Server applies row-level
filtering to cdc.change_tables for callers that don’t hold db_owner, returning only rows for
source tables that the caller can read. The db_datareader role granted in
Set up your SQL Server instance satisfies this requirement. If access was scoped
more tightly instead of using db_datareader, make sure the connector has SELECT on every source
table that it replicates.
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Verify the deployment. Confirm that the two wrapper procedures exist and that the audit table is queryable:
The first query returns both
sf_openflow_cdc_enable_tableandsf_openflow_cdc_disable_table. The second query confirms that the audit table exists and is readable.
Set up your Snowflake environment¶
As a Snowflake administrator, perform the following tasks:
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Create a destination database in Snowflake to store the replicated data:
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Create a Snowflake service user:
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Create a Snowflake role for the connector and grant the required privileges:
Use this role to manage the connector’s access to the Snowflake database.
To create objects in the destination database, you must grant the USAGE and CREATE SCHEMA privileges on the database to the role used to manage access.
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Create a Snowflake warehouse for the connector and grant the required privileges:
Snowflake recommends starting with a XSMALL warehouse size, then experimenting with size depending on the number of tables being replicated and the amount of data transferred. Large numbers of tables typically scale better with multi-cluster warehouses, rather than a larger warehouse size. For more information, see multi-cluster warehouses.
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Set up the public and private keys for key pair authentication:
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Create a pair of secure keys (public and private).
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Store the private key for the user in a file to supply to the connector’s configuration.
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Assign the public key to the Snowflake service user:
For more information, see Key-pair authentication and key-pair rotation.
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Install the connector¶
To install the connector, do the following as a data engineer:
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Navigate to the Openflow overview page. In the Featured connectors section, select View more connectors.
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On the Openflow connectors page, find the connector and select Add to runtime.
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In the Select runtime dialog, select your runtime from the Available runtimes drop-down list and click Add.
Note
Before you install the connector, ensure that you have created a database and schema in Snowflake for the connector to store ingested data.
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Authenticate to the deployment with your Snowflake account credentials and select Allow when prompted to allow the runtime application to access your Snowflake account. The connector installation process takes a few minutes to complete.
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Authenticate to the runtime with your Snowflake account credentials.
The Openflow canvas appears with the connector process group added to it.
Runtime sizing¶
The runtime size determines the CPU and memory available to the connector. The available sizes are Small, Medium, and Large. The connector requires Medium or Large. Choose the size when you create the runtime: you can’t change the size of an existing runtime in place.
Choose Large if you expect high replication throughput or if source tables contain wide rows.
Resize a runtime¶
Runtime size is fixed at creation, so to change size you run the connector on a different runtime. You have two options depending on whether you want to preserve the current replication progress.
If you don’t need to keep the progress of the current connector, the simplest path is to create a new runtime at the size you need and install a new connector instance on it. The new connector starts from scratch: it snapshots all configured tables and then captures ongoing changes from that point. The replication progress of the existing connector is discarded.
To keep the progress of the current connector, for example to avoid re-snapshotting tables that took a long time to snapshot initially, migrate the connector to the new runtime. This reuses the existing destination tables and resumes incremental replication from where it left off.
For migration instructions, see Reinstall the connector.
Configure the connector¶
To configure the connector, do the following as a data engineer:
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Right-click on the imported process group and select Parameters.
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Populate the required parameter values.
For more information on the required parameter values, see the following sections:
- SQLServer Source Parameters: Used to establish a connection with SQL Server.
- SQLServer Destination Parameters: Used to establish a connection with Snowflake.
- SQLServer Ingestion Parameters: Used to specify the tables to replicate.
Start by setting the parameters of the SQLServer Source Parameters context, then the SQLServer Destination Parameters context. After you complete this, enable the connector. The connector connects to both SQLServer and Snowflake and starts running. However, the connector doesn’t replicate any data until any tables to be replicated are explicitly added to its configuration.
To configure specific tables for replication, edit the SQLServer Ingestion Parameters context. After you apply the changes to the SQLServer Ingestion Parameters context, the configuration is picked up by the connector, and the replication lifecycle starts for every table.
SQLServer Source Parameters¶
| Parameter | Description |
|---|---|
| SQLServer Connection URL | The full JDBC URL used to connect to the source. For a standalone SQL Server instance or Azure SQL Managed Instance, point the URL at the instance. The connector discovers the databases to replicate from that instance.
For Azure SQL Database, point the URL at a specific database using the
|
| SQLServer JDBC Driver | Select the Reference asset checkbox to upload the SQL Server JDBC driver. |
| SQLServer Username | The user name for the connector. |
| SQLServer Password | The password for the connector. |
Note
Azure SQL Database refers to the single-database PaaS offering, not Azure SQL Managed Instance.
SQLServer Destination Parameters¶
| Parameter | Description | Required |
|---|---|---|
| Destination Database | The database where data is persisted. It must already exist in Snowflake. The name is case-sensitive. For unquoted identifiers, provide the name in uppercase. | Yes |
| Destination Schema Pattern | A pattern for the names of destination schemas where data is persisted. The connector creates the schemas if they don’t exist. You can customize the pattern per ingested table using these optional variables:
For example, for a table with the qualified name To ingest all tables into a single schema, provide a schema name without any variables,
like Important Don’t change this setting after the connector has begun ingesting data. Changing this setting after ingestion has begun breaks the existing ingestion. If you must change this setting, create a new connector instance. | Yes |
| Snowflake Authentication Strategy | When using:
| Yes |
| Snowflake Account Identifier | When using:
| Yes |
| Snowflake Connection Strategy | When using KEY_PAIR, specify the strategy for connecting to Snowflake:
| Required for BYOC with KEY_PAIR only, otherwise ignored. |
| Snowflake Object Identifier Resolution | Specifies how source object identifiers such as schemas, tables, and columns names are stored and queried in Snowflake. This setting dictates whether you must use double quotes in SQL queries. Option 1: Default, case-insensitive (recommended).
For example Note Snowflake recommends using this option if database objects are not expected to have mixed case names. Important Do not change this setting after connector ingestion has begun. Changing this setting after ingestion has begun breaks the existing ingestion. If you must change this setting, create a new connector instance. Option 2: case-sensitive.
Note Snowflake recommends using this option if you must preserve source casing for legacy or compatibility reasons.
For example, if the source database includes table names that differ in case only, such as | Yes |
| Snowflake Private Key | When using:
| No |
| Snowflake Private Key File | When using:
| No |
| Snowflake Private Key Password | When using:
| No |
| Snowflake Role | When using:
| Yes |
| Snowflake Username | When using:
| Yes |
| Oversized Value Strategy | Determines how the connector handles values that exceed its internal size limits (16 MB) during replication. Possible values are:
| No |
| Snowflake Warehouse | Snowflake warehouse used to run queries. | Yes |
SQLServer Ingestion Parameters¶
| Parameter | Description |
|---|---|
| Included Table Names | A comma-separated list of source table paths, including their databases and schemas, for example:
|
| Included Table Regex | A regular expression to match against table paths, including database and schema names. Every path matching the expression is replicated, and new tables matching the pattern that are created later are also included automatically, for example:
|
| Column Filter JSON | Optional. A JSON array of filter objects specifying which columns to include or exclude per table. For syntax details and examples, see Replicate a subset of columns in a table. |
| Merge Task Schedule CRON | CRON expression defining periods when merge operations from Journal to Destination Table will be
triggered. Set it to For example:
For additional information and examples, see the cron triggers tutorial in the Quartz Documentation |
Restart table replication¶
A table in FAILED state — for example, due to a missing primary key or unsupported schema change — does not restart automatically. If a table enters a FAILED state or you need to restart replication from scratch, use the following procedure to remove and re-add the table to replication.
Note
If the failure was caused by an issue in the source table such as a missing primary key, resolve that issue in the source database before continuing.
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Remove the table from replication, using one of the following methods:
- Add the table to the Re-snapshot Table Exclusions parameter to temporarily exclude it from replication. This is convenient when the table is matched by an Included Table Regex that you don’t want to change.
- In the Ingestion Parameters context, either remove the table from Included Table Names or modify the Included Table Regex so the table is no longer matched.
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Verify the table has been removed:
- In the Openflow runtime canvas, right-click a processor group and choose Controller Services.
- In the table listing controller services, locate the Table State Store row, click the three vertical dots on the right side of the row, then choose View State.
Important
You must wait until the table’s state is fully removed from this list before proceeding. Do not continue until this configuration change has completed.
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Clean up the destination: Once the table’s state shows as fully removed, manually DROP the destination table in Snowflake. Note that the connector will not overwrite an existing destination table during the snapshot phase; if the table still exists, replication will fail again. Optionally, the journal table and stream can also be removed if they are no longer needed.
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Re-add the table by reversing the change you made in the first step: either remove the table from Re-snapshot Table Exclusions, or add it back to Included Table Names or Included Table Regex. The connector then re-snapshots the table.
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Verify the restart: Check the Table State Store using the instructions given previously. The state of the table should appear with the status NEW, then transition to SNAPSHOT_REPLICATION, and finally INCREMENTAL_REPLICATION.
Replicate a subset of columns in a table¶
The connector can filter the data replicated per table to a subset of configured columns. Primary key columns are always included regardless of exclusions.
To apply column filters, set the Column Filter JSON parameter in the Ingestion Parameters context to a JSON array of filter objects, one per table you want to filter.
Columns can be included or excluded by name or by regular expression pattern. You can apply a single condition per table, or combine multiple conditions, with exclusions always taking precedence over inclusions.
Syntax¶
Each object in the array identifies a table and specifies which columns to include or exclude.
Because this connector uses three-part fully qualified names (database, schema, and table), each object
can include a database or databasePattern field in addition to the schema and table fields.
The following rules apply:
- Use
database,schema, andtablefor exact name matching, ordatabasePattern,schemaPattern, andtablePatternfor regex matching. You can’t use both a field and its pattern variant in the same object (for example,schemaandschemaPatterncan’t both appear). - At least one of
included,excluded,includedPattern, orexcludedPatternmust be provided. - When both included and excluded filters are specified, exclusions take precedence.
- When multiple filters match the same table, the last matching filter is used, with exact matches taking precedence over pattern-based filters.
- The value can be an array of objects to apply different filters to different tables.
Examples¶
Include specific columns by name:
Exclude specific columns by name:
Combine an include pattern with a specific exclusion (for example, include all email columns except admin_email):
Mix a database pattern with an exact schema and table name to apply a filter across databases:
Pass multiple filter objects to apply different rules to different tables:
Including and excluding the same column¶
Removing a column from a table’s replicated set (by excluding it or by removing
it from the included list) has the same effect on the destination as dropping
the column at the source: the connector soft-deletes the column on the
destination by renaming it with a suffix (by default, __SNOWFLAKE_DELETED).
If you then add the column back to the replicated set and later remove it a
second time, replication for the affected table fails because the soft-deleted
column name is already taken. To recover, restart replication for the affected
table.
Replicate a partitioned table¶
The connector supports replication of partitioned tables. A SQL Server partitioned table is replicated into Snowflake as a single destination table, containing data from all partitions.
To replicate a partitioned table, ensure that CDC is enabled on the partitioned table, as described in Set up your SQL Server instance.
Track data changes in tables¶
The connector replicates the current state of data from the source tables, as well as detected changes from each polling interval. This data is stored in journal tables created in the same schema as the destination table.
The journal table names are formatted as: <source_table_name>_JOURNAL_<timestamp>_<schema_generation>
where <timestamp> is the value of epoch seconds when the source table was added to replication, and <schema_generation> is an integer increasing with every schema change on the source table.
As a result, source tables that undergo schema changes will have multiple journal tables.
When you remove a table from replication, then add it back, the <timestamp> value changes, and <schema_generation> starts again from 1.
Important
Snowflake recommends not altering the structure of journal tables in any way. The connector uses them to update the destination table as part of the replication process.
The connector never drops journal tables, but uses the latest journal for every replicated source table, only reading append-only streams on top of journals. To reclaim the storage, you can:
- Truncate all journal tables at any time.
- Drop the journal tables related to source tables that were removed from replication.
- Drop all but the latest generation journal tables for actively replicated tables.
For example, if your connector is set to actively replicate source table orders,
and you have earlier removed table customers from replication, you may have
the following journal tables. In this case you can drop all of them except orders_5678_2.
Configure scheduling of merge tasks¶
The connector uses a warehouse to merge CDC data into destination tables. This operation is triggered by the MergeSnowflakeJournalTable processor. If there are no new changes or if no new flow files are waiting in the MergeSnowflakeJournalTable queue, no merge is triggered and the warehouse auto-suspends.
Use the CRON expression in the Merge task Schedule CRON parameter to limit the warehouse cost and limit merges to only scheduled time. It throttles the flow files coming to the MergeSnowflakeJournalTable processor and merges are triggered only in a dedicated period of time. For more information about scheduling, see Scheduling strategy.
Run the flow¶
- Right-click on the plane and select Enable all Controller Services.
- Right-click on the imported process group and select Start. The connector starts the data ingestion.