If you have comments, questions etc., please comment on this post or ping me. This means that the logical server name must start with Latin letters or an underscore (e.g., [a-z,A-Z,_]), Here is an example of the configuration for a connector instance that monitors a SQL Server server at port 3306 on 192.168.99.100, which we logically name fullfillment: See the complete list of connector properties that can be specified in these configurations. When the connector starts, it will grab a consistent snapshot of the schemas in your SQL Server database and start streaming changes, producing events for every inserted, updated, and deleted row. Right, so we want to install the Debezium SQL Server connector. For example, consider a SQL Server installation with an inventory database that contains four tables: products, products_on_hand, customers, and orders in schema dbo. Controls which rows from tables will be included in snapshot. This connector was added in Debezium 0.9.0. Other than SQL Servers DATETIMEOFFSET data type (which contain time zone information), the other temporal types depend on the value of the time.precision.mode configuration property. I have been doing a couple of conference talks lately (virtual, of course) about streaming data from SQL Server to Kafka. For example, the following connector configuration properties can be used to secure connections to the Kafka broker: In addition to the pass-through to the Kafka producer and consumer, the properties starting with database., e.g. Debezium is able to capture schema changes over time. Positive integer value that specifies the maximum size of the blocking queue into which change events read from the database log are placed before they are written to Kafka. The total number of tables that are being included in the snapshot. The payload section of every change event value produced by the SQL Server connector has an envelope structure with the following fields: op is a mandatory field that contains a string value describing the type of operation. This means that SQL Server creates a capture table that contains all changes executed on the source table. This can be used to monitor whether the connector is still receiving change events from the database. Click on that button and then right-click on the connector for the product you want (SQL Server Connector Plug-in), and copy the link. There are several things we can learn by just looking at this payload section. The connector is able to detect whether the CDC is enabled or disabled for whitelisted source table during the runtime and modify its behaviour. Ok, so we have seen how we can install a connector using confluent-hub install in the command option in the docker-compose.yml. It may appear that the JSON representations of the events are much larger than the rows they describe. That image is an image containing some base connectors and also tools for generating data. The schema describes the structure of the payload, while the payload contains the actual data. Represents the number of nanoseconds past epoch, and does not include timezone information. Controls whether a tombstone event should be generated after a delete event. The broker(s) contain topics that are the sinks for the source connectors. Defaults to 2048. Fully-qualified tables could be defined as DB_NAME.TABLE_NAME or SCHEMA_NAME.TABLE_NAME, depending on the specific connector. Specifies the maximum number of rows that should be read in one go from each table while taking a snapshot. Some Rights Reserved. Therefore I decided to write this post to have something to go back to for next time. In any case, let us quickly go over how to test it works. But for now, let us use this image. If your application does not tolerate such a transition period we recommend to follow the cold schema update. When the columns for a rows primary/unique key are updated, the value of the rows key has changed so Debezium will output three events: a DELETE event and a tombstone event with the old key for the row, followed by an INSERT event with the new key for the row. In Figure 3, we see that cp-all-in-one (outlined in red) has some sub-directories. Fully-qualified names for columns are of the form schemaName.tableName.columnName. Source connectors which understand how to interact with the source system send records into Kafka. Use that image in the docker compose file in place of the original connect image. If we look at the value of the keys payload field, well see that it is indeed a structure (which in JSON is just an object) with a single id field, whose value is 1004. The name of the Java class for the connector. If the Kafka cluster is still up bring it down. So far, we have seen that the SQL Server connector is installed, but we have not made sure it does what it is supposed to do, i.e. This event gives a consumer all kinds of information that it can use to process the removal of this row. I also mentioned the CONNECT_PLUGIN_PATH, which indicates where the service loads connectors from. We have now confirmed that everything works! This queue can provide backpressure to the CDC table reader when, for example, writes to Kafka are slower or if Kafka is not available. Wait a little while for the cluster to start up, and then use Postman to retrieve the installed connectors as in Code Snippet 3. This time though, you will not see the datagen connector as it is not part of the cp-server-connect-base image. The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop. Having edited the docker-compose.yml file, we now start the cluster: docker-compose up -d. NOTE: Above I say * get the latest release *. This is true, because the JSON representation must include the schema and the payload portions of the message. We see in Figure 2 how the source system is SQL Server and how the source connector is the Debezium SQL Server connector. When you look at the topics in Kafka at this stage, you do not see any topic related to the table we want to stream data from. We see various Connect connectors, but nothing for SQL Server. Some topics: .NET, SQL Server, Data Science, R, Windows Azure and a lot more. A quick side note here about cp-all-in-one. This configuration can be sent via POST to a running Kafka Connect service, which will then record the configuration and start up the one connector task that will connect to the SQL Server database, read the transaction log, and record events to Kafka topics. We said that: We then looked at how to deploy a Debezium connector, more specifically the SQL Server connector, to a Kafka Connect Docker container. I mentioned above that we need a file telling Docker what to do. Heres an example: When we compare this to the value in the insert event, we see a couple of differences in the payload section: The op field value is now u, signifying that this row changed because of an update, The before field now has the state of the row with the values before the database commit. There is a period of time between the database schema update and creating the new capture instance. For instance this means that in case of a newly added column any change event produced during this time will not yet contain a field for that new column. It works by the use of connectors which are. Optionally release the locks obtained in step 2, i.e. Also a field event_serial_no is present during streaming. Updates every 10,000 rows scanned and upon completing a table. The scale schema parameter contains an integer representing how many digits the decimal point was shifted. However, we need something to test that what we are doing works, so heres some code to set up a database on SQL Server: Code Snippet 1: DB Objects Creation Script. The name of this SQL Server connector class. This means that when using the Avro Converter, the resulting Avro schems for each table in each logical source have their own evolution and history. On the other hand, since SQL Server supports tenth of microsecond precision, the events generated by a connector with the connect time precision mode will result in a loss of precision when the database column has a fractional second precision value greater than 3: Represents the number of milliseconds since midnight, and does not include timezone information. There are quite a few Docker images, and Docker composes files around for setting up Kafka and Kafka Connect. Therefore, when the SQL Server connector first connects to a particular SQL Server database, it starts by performing a consistent snapshot of each of the database schemas. Due to the way CDC is implemented in SQL Server, it is necessary to work in co-operation with a database operator in order to ensure the Debezium connector continues to produce data change events when the schema is updated. In the diagram we publish to one topic as we only retrieve data from one table. When true the delete operations are represented by a delete event and a subsequent tombstone event. If the connector stops for any reason (including communication failures, network problems, or crashes), upon restart it simply continues reading the CDC tables where it last left off. We see in Figure 10 how we indeed have installed a pre-release version of the connector - yay! In this post, I describe what to do if you want to run the Debezium SQL Server connector in a Docker environment. Events captured during log reading are not affected by it. The number of milliseconds that elapsed since the last change was recovered from the history store. Whether field names will be sanitized to adhere to Avro naming requirements. making sure we start up the worker process, Kafka Connect is a JVM process allowing us to stream data between Apache Kafka and other systems. These systems can be databases, Hadoop, files, etc. The link for the latest development release for the SQL Server Connector Plug-in at the time of me writing this post (August 2021) is: When you look at Code Snippet 8 beware of the line continuations \. As I mentioned initially, this post is not about SQL Server CDC or configuring the Debezium connector. As the connector reads changes and produces events, it records the position in the database log (LSN / Log Sequence Number), that is associated with CDC record, with each event. An interval in milli-seconds that the connector should wait before taking a snapshot after starting up; Unique name for the connector. The benefit of using Confluent Platform is that you get ALL the goodies, including Control Center, which is the WEB UI for Confluent Platform. There are generally two procedures how to execute the schema change: cold - this is executed when Debezium is stopped. The name of the database history topic where the connector will write and recover DDL statements. The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop. To make this possible, Debeziums SQL Server connector always follows the delete event with a special tombstone event that has the same key but null value. Mode to control which transaction isolation level is used and how long the connector locks the monitored tables. Each snapshot consists of the following steps: Obtain a lock on each of the monitored tables to ensure that no structural changes can occur to any of the tables. The code in Code Snippet 1 creates a database, DebeziumTest and a table, dbo.tb_CDCTab1 in the database. Fully-qualified names for columns are of the form schemaName.tableName.columnName. The port number of the SQL Server instance. It is thus recommended to execute all DDLs in a single batch so the procedure is done only once. Note that primary key columns are always included in the events key, also if blacklisted from the value. Represents the number of milliseconds since epoch, and does not include timezone information. We started by looking at what Kafka Connect and Debezium is. Debezium has Kafka Connect connectors for a multitude of source systems. Kafka Connect is a tool for streaming data between Apache Kafka and other systems in a scalable and reliable way. This way, we start with a consistent view of all of the data, yet continue reading without having lost any of the changes made while the snapshot was taking place. The first one contains the old values and the second one contains new values. As with most enterprise editions this requires a licennse. When that snapshot is complete, the connector continuously streams the changes that were committed to SQL Server and generates corresponding insert, update and delete events. Ok, cool - so we see the syntax to install a connector in Code Snippet 4, but this looks suspiciously like how we do it from a bash shell. That is due to the update event composed of two events behind the scenes and we are exposing only the second one. The operator should follow this sequence of steps, Suspend the application that generates the database records, Wait for Debezium to stream all unstreamed changes, Apply all changes to the source table schema, Create a new capture table for the update source table using sys.sp_cdc_enable_table procedure with a unique value for parameter @capture_instance, When Debezium starts streaming from the new capture table it is possible to drop the old one using sys.sp_cdc_disable_table stored procedure with parameter @capture_instance set to the old capture instance name, The hot schema update does not require any downtime in application and data processing. systems we want to get data from. The following configuration properties are required unless a default value is available. The number of milliseconds since the connector has read and processed the most recent event. Using Confluent Hub, you can install Kafka Connect connectors, whether you do it locally or in Docker. read_uncommitted In this mode neither table nor row-level locks are acquired, but connector does not guarantee snapshot consistency. The connector then produces a change event for every row-level insert, update, and delete operation that was published via the CDC API, recording all the change events for each table in a separate Kafka topic. The total number of schema changes applie during recovery and runtime. The event contains a field for each column value, and how that value is represented in the event depends on the SQL data type of the column. Ah, thats where the magic of Docker compose files comes in. A semi-colon list of regular expressions that match fully-qualified tables and columns to map a primary key. This is the safest procedure but might not be feasible for applications with high-availability requirements. That changes as soon as you insert some data into the table: When you refresh the Topics page in Control Center after executing the code in Code Snippet 12, you see this: Cool, in Figure 13, outlined in red, we see a new topic. The cp-all-in-one directory looks like so: Figure 3: Kafka Connect SQL Server & Debezium. Transaction identifier of the last processed transaction. These topics are Debezium specific topics, and you as a user would not do much with them. Outlined in blue, we see that we have one Connect cluster. The semantic type describes how the Kafka Connect schema captures the meaning of the field using the name of the Kafka Connect schema for the field. A possible use case for large append-only tables is setting a specific point where to start (resume) snapshotting, in case a previous snapshotting was interrupted. The number of changes that were read during recovery phase. So far weve seen samples of create and update events. Learn how. all the connectors. Scan all of the relevant database tables and schemas as valid at the LSN position read in step 3, and generate a READ event for each row and write that event to the appropriate table-specific Kafka topic. We can either install it via the docker-compose.yml file similar to what we did in Code Snippet 5 or what we did in Code Snippet 6. Sink systems. The source field structure has the same fields as before, but the values are different since this event is from a different position in the transaction log. This is the topic for the table we want to stream data from into Kafka. Flag that denotes whether the connector is currently connected to the database server. This may be useful when consumers only know about the built-in Kafka Connect logical types and are unable to handle variable-precision time values. Integer port number of the SQL Server database server. Debeziums SQL Server Connector can monitor and record the row-level changes in the schemas of a SQL Server database. Kafka, Zookeeper, and Kafka Connect all have built-in support for JMX metrics.
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