Debezium connector for MongoDB

Overview

MongoDB’s replication mechanism provides redundancy and high availability, and is the preferred way to run MongoDB in production. MongoDB connector captures the changes in a replica set or sharded cluster.

A MongoDB replica set consists of a set of servers that all have copies of the same data, and replication ensures that all changes made by clients to documents on the replica set’s primary are correctly applied to the other replica set’s servers, called secondaries. MongoDB replication works by having the primary record the changes in its oplog (or operation log), and then each of the secondaries reads the primary’s oplog and applies in order all of the operations to their own documents. When a new server is added to a replica set, that server first performs an snapshot of all of the databases and collections on the primary, and then reads the primary’s oplog to apply all changes that might have been made since it began the snapshot. This new server becomes a secondary (and able to handle queries) when it catches up to the tail of the primary’s oplog.

Change Stream

The Debezium MongoDB connector uses a similar replication mechanism to the one described above, though it does not actually become a member of the replica set. The main difference is that the connector does not read the oplog directly, but delegates capturing and decoding the oplog to MongoDB’s Change Streams feature. With change streams, the MongoDB server exposes changes to collections as an event stream. The Debezium connector watches the stream and delivers the changes downstream. And, when the connector sees a replica set for the first time, it looks at the oplog to get the last recorded transaction and then performs a snapshot of the primary’s databases and collections. When all the data is copied, the connector then creates a change stream from the position it read earlier from the oplog.

As the MongoDB connector processes changes, it periodically records the position in the oplog/stream where the event originated. When the connector stops, it records the last oplog/stream position that it processed, so that upon restart it simply begins streaming from that position. In other words, the connector can be stopped, upgraded or maintained, and restarted some time later, and it will pick up exactly where it left off without losing a single event. Of course, MongoDB’s oplogs are usually capped at a maximum size, which means that the connector should not be stopped for too long, or else some of the operations in the oplog might be purged before the connector has a chance to read them. In this case, upon restart the connector will detect the missing oplog operations, perform a snapshot, and then proceed with streaming the changes.

The MongoDB connector is also quite tolerant of changes in membership and leadership of the replica sets, of additions or removals of shards within a sharded cluster, and network problems that might cause communication failures. The connector always uses the replica set’s primary node to stream changes, so when the replica set undergoes an election and a different node becomes primary, the connector will immediately stop streaming changes, connect to the new primary, and start streaming changes using the new primary node. Likewise, if connector experiences any problems communicating with the replica set primary, it will try to reconnect (using exponential backoff so as to not overwhelm the network or replica set) and continue streaming changes from where it last left off. In this way the connector is able to dynamically adjust to changes in replica set membership and to automatically handle communication failures.

Additional resources

• Replication mechanism

• Replica set

• Replica set elections

• Sharded cluster

• Shard addition

• Shard removal

• Change Streams

How the MongoDB connector works

An overview of the MongoDB topologies that the connector supports is useful for planning your application.

When a MongoDB connector is configured and deployed, it starts by connecting to the MongoDB servers at the seed addresses, and determines the details about each of the available replica sets. Since each replica set has its own independent oplog, the connector will try to use a separate task for each replica set. The connector can limit the maximum number of tasks it will use, and if not enough tasks are available the connector will assign multiple replica sets to each task, although the task will still use a separate thread for each replica set.

Supported MongoDB topologies

The MongoDB connector supports the following MongoDB topologies:

MongoDB replica set

The Debezium MongoDB connector can capture changes from a single MongoDB replica set. Production replica sets require a minimum of at least three members.

To use the MongoDB connector with a replica set, provide the addresses of one or more replica set servers as seed addresses through the connector’s mongodb.hosts property. The connector will use these seeds to connect to the replica set, and then once connected will get from the replica set the complete set of members and which member is primary. The connector will start a task to connect to the primary and capture the changes from the primary’s oplog. When the replica set elects a new primary, the task will automatically switch over to the new primary.

MongoDB sharded cluster

A MongoDB sharded cluster consists of:

• One or more shards, each deployed as a replica set;

• A separate replica set that acts as the cluster’s configuration server

• One or more routers (also called mongos) to which clients connect and that routes requests to the appropriate shards

  To use the MongoDB connector with a sharded cluster, configure the connector with the host addresses of the configuration server replica set. When the connector connects to this replica set, it discovers that it is acting as the configuration server for a sharded cluster, discovers the information about each replica set used as a shard in the cluster, and will then start up a separate task to capture the changes from each replica set. If new shards are added to the cluster or existing shards removed, the connector will automatically adjust its tasks accordingly.

MongoDB standalone server

The MongoDB connector is not capable of monitoring the changes of a standalone MongoDB server, since standalone servers do not have an oplog. The connector will work if the standalone server is converted to a replica set with one member.

Logical connector name

The connector configuration property topic.prefix serves as a logical name for the MongoDB replica set or sharded cluster. The connector uses the logical name in a number of ways: as the prefix for all topic names, and as a unique identifier when recording the change stream position of each replica set.

You should give each MongoDB connector a unique logical name that meaningfully describes the source MongoDB system. We recommend logical names begin with an alphabetic or underscore character, and remaining characters that are alphanumeric or underscore.

Performing a snapshot

When a task starts up using a replica set, it uses the connector’s logical name and the replica set name to find an offset that describes the position where the connector previously stopped reading changes. If an offset can be found and it still exists in the oplog, then the task immediately proceeds with streaming changes, starting at the recorded offset position.

However, if no offset is found or if the oplog no longer contains that position, the task must first obtain the current state of the replica set contents by performing a snapshot. This process starts by recording the current position of the oplog and recording that as the offset (along with a flag that denotes a snapshot has been started). The task will then proceed to copy each collection, spawning as many threads as possible (up to the value of the snapshot.max.threads configuration property) to perform this work in parallel. The connector will record a separate read event for each document it sees, and that read event will contain the object’s identifier, the complete state of the object, and source information about the MongoDB replica set where the object was found. The source information will also include a flag that denotes the event was produced during a snapshot.

This snapshot will continue until it has copied all collections that match the connector’s filters. If the connector is stopped before the tasks’ snapshots are completed, upon restart the connector begins the snapshot again.

Ad hoc snapshots

By default, a connector runs an initial snapshot operation only after it starts for the first time. Following this initial snapshot, under normal circumstances, the connector does not repeat the snapshot process. Any future change event data that the connector captures comes in through the streaming process only.

However, in some situations the data that the connector obtained during the initial snapshot might become stale, lost, or incomplete. To provide a mechanism for recapturing collection data, Debezium includes an option to perform ad hoc snapshots. The following changes in a database might be cause for performing an ad hoc snapshot:

• The connector configuration is modified to capture a different set of collections.

• Kafka topics are deleted and must be rebuilt.

• Data corruption occurs due to a configuration error or some other problem.

You can re-run a snapshot for a collection for which you previously captured a snapshot by initiating a so-called ad-hoc snapshot. Ad hoc snapshots require the use of signaling collections. You initiate an ad hoc snapshot by sending a signal request to the Debezium signaling collection.

When you initiate an ad hoc snapshot of an existing collection, the connector appends content to the topic that already exists for the collection. If a previously existing topic was removed, Debezium can create a topic automatically if automatic topic creation is enabled.

Ad hoc snapshot signals specify the collections to include in the snapshot. The snapshot can capture the entire contents of the database, or capture only a subset of the collections in the database.

You specify the collections to capture by sending an execute-snapshot message to the signaling collection. Set the type of the execute-snapshot signal to incremental, and provide the names of the collections to include in the snapshot, as described in the following table:

Triggering an ad hoc snapshot

You initiate an ad hoc snapshot by adding an entry with the execute-snapshot signal type to the signaling collection. After the connector processes the message, it begins the snapshot operation. The snapshot process reads the first and last primary key values and uses those values as the start and end point for each collection. Based on the number of entries in the collection, and the configured chunk size, Debezium divides the collection into chunks, and proceeds to snapshot each chunk, in succession, one at a time.

Currently, the execute-snapshot action type triggers incremental snapshots only. For more information, see Incremental snapshots.

Incremental snapshots

To provide flexibility in managing snapshots, Debezium includes a supplementary snapshot mechanism, known as incremental snapshotting. Incremental snapshots rely on the Debezium mechanism for sending signals to a Debezium connector. Incremental snapshots are based on the DDD-3 design document.

In an incremental snapshot, instead of capturing the full state of a database all at once, as in an initial snapshot, Debezium captures each collection in phases, in a series of configurable chunks. You can specify the collections that you want the snapshot to capture and the size of each chunk. The chunk size determines the number of rows that the snapshot collects during each fetch operation on the database. The default chunk size for incremental snapshots is 1 KB.

As an incremental snapshot proceeds, Debezium uses watermarks to track its progress, maintaining a record of each collection row that it captures. This phased approach to capturing data provides the following advantages over the standard initial snapshot process:

• You can run incremental snapshots in parallel with streamed data capture, instead of postponing streaming until the snapshot completes. The connector continues to capture near real-time events from the change log throughout the snapshot process, and neither operation blocks the other.

• If the progress of an incremental snapshot is interrupted, you can resume it without losing any data. After the process resumes, the snapshot begins at the point where it stopped, rather than recapturing the collection from the beginning.

• You can run an incremental snapshot on demand at any time, and repeat the process as needed to adapt to database updates. For example, you might re-run a snapshot after you modify the connector configuration to add a collection to its collection.include.list property.

Incremental snapshot process

When you run an incremental snapshot, Debezium sorts each collection by primary key and then splits the collection into chunks based on the configured chunk size. Working chunk by chunk, it then captures each collection row in a chunk. For each row that it captures, the snapshot emits a READ event. That event represents the value of the row when the snapshot for the chunk began.

As a snapshot proceeds, it’s likely that other processes continue to access the database, potentially modifying collection records. To reflect such changes, INSERT, UPDATE, or DELETE operations are committed to the transaction log as per usual. Similarly, the ongoing Debezium streaming process continues to detect these change events and emits corresponding change event records to Kafka.

How Debezium resolves collisions among records with the same primary key

In some cases, the UPDATE or DELETE events that the streaming process emits are received out of sequence. That is, the streaming process might emit an event that modifies a collection row before the snapshot captures the chunk that contains the READ event for that row. When the snapshot eventually emits the corresponding READ event for the row, its value is already superseded. To ensure that incremental snapshot events that arrive out of sequence are processed in the correct logical order, Debezium employs a buffering scheme for resolving collisions. Only after collisions between the snapshot events and the streamed events are resolved does Debezium emit an event record to Kafka.

Snapshot window

To assist in resolving collisions between late-arriving READ events and streamed events that modify the same collection row, Debezium employs a so-called snapshot window. The snapshot windows demarcates the interval during which an incremental snapshot captures data for a specified collection chunk. Before the snapshot window for a chunk opens, Debezium follows its usual behavior and emits events from the transaction log directly downstream to the target Kafka topic. But from the moment that the snapshot for a particular chunk opens, until it closes, Debezium performs a de-duplication step to resolve collisions between events that have the same primary key..

For each data collection, the Debezium emits two types of events, and stores the records for them both in a single destination Kafka topic. The snapshot records that it captures directly from a table are emitted as READ operations. Meanwhile, as users continue to update records in the data collection, and the transaction log is updated to reflect each commit, Debezium emits UPDATE or DELETE operations for each change.

As the snapshot window opens, and Debezium begins processing a snapshot chunk, it delivers snapshot records to a memory buffer. During the snapshot windows, the primary keys of the READ events in the buffer are compared to the primary keys of the incoming streamed events. If no match is found, the streamed event record is sent directly to Kafka. If Debezium detects a match, it discards the buffered READ event, and writes the streamed record to the destination topic, because the streamed event logically supersede the static snapshot event. After the snapshot window for the chunk closes, the buffer contains only READ events for which no related transaction log events exist. Debezium emits these remaining READ events to the collection’s Kafka topic.

The connector repeats the process for each snapshot chunk.

Triggering an incremental snapshot

Currently, the only way to initiate an incremental snapshot is to send an ad hoc snapshot signal to the signaling collection on the source database. You submit signals to the collection as SQL INSERT queries. After Debezium detects the change in the signaling collection, it reads the signal, and runs the requested snapshot operation.

The query that you submit specifies the collections to include in the snapshot, and, optionally, specifies the kind of snapshot operation. Currently, the only valid option for snapshots operations is the default value, incremental.

To specify the collections to include in the snapshot, provide a data-collections array that lists the collections or an array of regular expressions used to match collections, for example,
{"data-collections": ["public.MyFirstTable", "public.MySecondTable"]}

The data-collections array for an incremental snapshot signal has no default value. If the data-collections array is empty, Debezium detects that no action is required and does not perform a snapshot.

Prerequisites

• Signaling is enabled.

  • A signaling data collection exists on the source database.

  • The signaling data collection is specified in the signal.data.collection property.

Procedure

1. Send a SQL query to add the ad hoc incremental snapshot request to the signaling collection:

   INSERT INTO _<signalTable>_ (id, type, data) VALUES (_'<id>'_, _'<snapshotType>'_, '{"data-collections": ["_<tableName>_","_<tableName>_"],"type":"_<snapshotType>_"}');

   For example,

   INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.table1", "schema2.table2"],"type":"incremental"}');

   The values of the id,type, and data parameters in the command correspond to the fields of the signaling collection.

   The following collection describes the these parameters:

   Table 2. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling collection

   Value	Description
   myschema.debezium_signal	Specifies the fully-qualified name of the signaling collection on the source database
   ad-hoc-1	The id parameter specifies an arbitrary string that is assigned as the id identifier for the signal request. Use this string to identify logging messages to entries in the signaling collection. Debezium does not use this string. Rather, during the snapshot, Debezium generates its own id string as a watermarking signal.
   execute-snapshot	Specifies type parameter specifies the operation that the signal is intended to trigger.
   data-collections	A required component of the data field of a signal that specifies an array of collection names or regular expressions to match collection names to include in the snapshot. The array lists regular expressions which match collections by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling collection in the signal.data.collection configuration property.
   incremental	An optional type component of the data field of a signal that specifies the kind of snapshot operation to run. Currently, the only valid option is the default value, incremental. Specifying a type value in the SQL query that you submit to the signaling collection is optional. If you do not specify a value, the connector runs an incremental snapshot.

The following example, shows the JSON for an incremental snapshot event that is captured by a connector.

Example: Incremental snapshot event message

{
    "before":null,
    "after": {
        "pk":"1",
        "value":"New data"
    },
    "source": {
        ...
        "snapshot":"incremental" (1)
    },
    "op":"r", (2)
    "ts_ms":"1620393591654",
    "transaction":null
}

Stopping an incremental snapshot

Incremental snapshots can also be stopped by sending a signal to the collection on the source database. You submit signals to the collection as SQL INSERT queries. After Debezium detects the change in the signaling collection, it reads the signal, and stops the incremental snapshot operation if it’s in progress.

The query that you submit specifies the snapshot operation of incremental, and, optionally, the collections of the current running snapshot to be removed.

Prerequisites

• Signaling is enabled.

  • A signaling data collection exists on the source database.

  • The signaling data collection is specified in the signal.data.collection property.

Procedure

1. Send a SQL query to stop the ad hoc incremental snapshot to the signaling collection:

   INSERT INTO _<signalTable>_ (id, type, data) values (_'<id>'_, 'stop-snapshot', '{"data-collections": ["_<tableName>_","_<tableName>_"],"type":"incremental"}');

   For example,

   INSERT INTO myschema.debezium_signal (id, type, dat) values ('ad-hoc-1', 'stop-snapshot', '{"data-collections": ["schema1.table1", "schema2.table2"],"type":"incremental"}');

   The values of the id, type, and data parameters in the command correspond to the fields of the signaling collection.

   The following collection describes these parameters:

   Table 3. Descriptions of fields in a SQL command for sending a stop incremental snapshot signal to the signaling collection

   Value	Description
   myschema.debezium_signal	Specifies the fully-qualified name of the signaling collection on the source database
   ad-hoc-1	The id parameter specifies an arbitrary string that is assigned as the id identifier for the signal request. Use this string to identify logging messages to entries in the signaling collection. Debezium does not use this string.
   stop-snapshot	Specifies type parameter specifies the operation that the signal is intended to trigger.
   data-collections	An optional component of the data field of a signal that specifies an array of collection names or regular expressions to match collection names to remove from the snapshot. The array lists regular expressions which match collections by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling collection in the signal.data.collection configuration property. If this component of the data field is omitted, the signal stops the entire incremental snapshot that is in progress.
   incremental	A required component of the data field of a signal that specifies the kind of snapshot operation that is to be stopped. Currently, the only valid option is incremental. Specifying a type value in the SQL query that you submit to the signaling collection is required. If you do not specify a value, the signal will not stop the incremental snapshot.

Streaming changes

After the connector task for a replica set records an offset, it uses the offset to determine the position in the oplog where it should start streaming changes. The task then (depending on the configuration) either connects to the replica set’s primary node or connects to a replica-set-wide change stream and starts streaming changes from that position. It processes all of create, insert, and delete operations, and converts them into Debezium change events. Each change event includes the position in the oplog where the operation was found, and the connector periodically records this as its most recent offset. The interval at which the offset is recorded is governed by offset.flush.interval.ms, which is a Kafka Connect worker configuration property.

When the connector is stopped gracefully, the last offset processed is recorded so that, upon restart, the connector will continue exactly where it left off. If the connector’s tasks terminate unexpectedly, however, then the tasks may have processed and generated events after it last records the offset but before the last offset is recorded; upon restart, the connector begins at the last recorded offset, possibly generating some the same events that were previously generated just prior to the crash.

As mentioned above, the connector tasks always use the replica set’s primary node to stream changes from the oplog, ensuring that the connector sees the most up-to-date operations as possible and can capture the changes with lower latency than if secondaries were to be used instead. When the replica set elects a new primary, the connector immediately stops streaming changes, connects to the new primary, and starts streaming changes from the new primary node at the same position. Likewise, if the connector experiences any problems communicating with the replica set members, it tries to reconnect, by using exponential backoff so as to not overwhelm the replica set, and once connected it continues streaming changes from where it last left off. In this way, the connector is able to dynamically adjust to changes in replica set membership and automatically handle communication failures.

To summarize, the MongoDB connector continues running in most situations. Communication problems might cause the connector to wait until the problems are resolved.

Pre-image support

Starting in MongoDB 6.0, you can configure change stream to emit pre-image to populate the before field for Mongo change events. To utilize pre-image, you need to first enable changeStreamPreAndPostImages for a collection using db.createCollection(), create, or collMod. And configure capture.mode as one of the *_with_pre_image options.

Topic names

The MongoDB connector writes events for all insert, update, and delete operations to documents in each collection to a single Kafka topic. The name of the Kafka topics always takes the form logicalName.databaseName.collectionName, where logicalName is the logical name of the connector as specified with the topic.prefix configuration property, databaseName is the name of the database where the operation occurred, and collectionName is the name of the MongoDB collection in which the affected document existed.

For example, consider a MongoDB replica set with an inventory database that contains four collections: products, products_on_hand, customers, and orders. If the connector monitoring this database were given a logical name of fulfillment, then the connector would produce events on these four Kafka topics:

• fulfillment.inventory.products

• fulfillment.inventory.products_on_hand

• fulfillment.inventory.customers

• fulfillment.inventory.orders

Notice that the topic names do not incorporate the replica set name or shard name. As a result, all changes to a sharded collection (where each shard contains a subset of the collection’s documents) all go to the same Kafka topic.

You can set up Kafka to auto-create the topics as they are needed. If not, then you must use Kafka administration tools to create the topics before starting the connector.

Partitions

The MongoDB connector does not make any explicit determination about how to partition topics for events. Instead, it allows Kafka to determine how to partition topics based on event keys. You can change Kafka’s partitioning logic by defining the name of the Partitioner implementation in the Kafka Connect worker configuration.

Kafka maintains total order only for events written to a single topic partition. Partitioning the events by key does mean that all events with the same key always go to the same partition. This ensures that all events for a specific document are always totally ordered.

Transaction Metadata

Debezium can generate events that represents transaction metadata boundaries and enrich change data event messages.

For every transaction BEGIN and END, Debezium generates an event that contains the following fields:

status

BEGIN or END

id

String representation of unique transaction identifier.

event_count (for END events)

Total number of events emitted by the transaction.

data_collections (for END events)

An array of pairs of data_collection and event_count that provides number of events emitted by changes originating from given data collection.

The following example shows a typical message:

{
  "status": "BEGIN",
  "id": "1462833718356672513",
  "event_count": null,
  "data_collections": null
}
{
  "status": "END",
  "id": "1462833718356672513",
  "event_count": 2,
  "data_collections": [
    {
      "data_collection": "rs0.testDB.collectiona",
      "event_count": 1
    },
    {
      "data_collection": "rs0.testDB.collectionb",
      "event_count": 1
    }
  ]
}

Unless overridden via the topic.transaction option, transaction events are written to the topic named .transaction.

Change data event enrichment

When transaction metadata is enabled, the data message Envelope is enriched with a new transaction field. This field provides information about every event in the form of a composite of fields:

id

String representation of unique transaction identifier.

total_order

The absolute position of the event among all events generated by the transaction.

data_collection_order

The per-data collection position of the event among all events that were emitted by the transaction.

Following is an example of what a message looks like:

{
  "after": "{\"_id\" : {\"$numberLong\" : \"1004\"},\"first_name\" : \"Anne\",\"last_name\" : \"Kretchmar\",\"email\" : \"annek@noanswer.org\"}",
  "source": {
...
  },
  "op": "c",
  "ts_ms": "1580390884335",
  "transaction": {
    "id": "1462833718356672513",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

Data change events

The Debezium MongoDB connector generates a data change event for each document-level operation that inserts, updates, or deletes data. Each event contains a key and a value. The structure of the key and the value depends on the collection that was changed.

Debezium and Kafka Connect are designed around continuous streams of event messages. However, the structure of these events may change over time, which can be difficult for consumers to handle. To address this, each event contains the schema for its content or, if you are using a schema registry, a schema ID that a consumer can use to obtain the schema from the registry. This makes each event self-contained.

The following skeleton JSON shows the basic four parts of a change event. However, how you configure the Kafka Connect converter that you choose to use in your application determines the representation of these four parts in change events. A schema field is in a change event only when you configure the converter to produce it. Likewise, the event key and event payload are in a change event only if you configure a converter to produce it. If you use the JSON converter and you configure it to produce all four basic change event parts, change events have this structure:

{
 "schema": { (1)
   ...
  },
 "payload": { (2)
   ...
 },
 "schema": { (3)
   ...
 },
 "payload": { (4)
   ...
 },
}

By default, the connector streams change event records to topics with names that are the same as the event’s originating collection. See topic names.

Change event keys

A change event’s key contains the schema for the changed document’s key and the changed document’s actual key. For a given collection, both the schema and its corresponding payload contain a single id field. The value of this field is the document’s identifier represented as a string that is derived from MongoDB extended JSON serialization strict mode.

Consider a connector with a logical name of fulfillment, a replica set containing an inventory database, and a customers collection that contains documents such as the following.

Example document

{
  "_id": 1004,
  "first_name": "Anne",
  "last_name": "Kretchmar",
  "email": "annek@noanswer.org"
}

Example change event key

Every change event that captures a change to the customers collection has the same event key schema. For as long as the customers collection has the previous definition, every change event that captures a change to the customers collection has the following key structure. In JSON, it looks like this:

{
  "schema": { (1)
    "type": "struct",
    "name": "fulfillment.inventory.customers.Key", (2)
    "optional": false, (3)
    "fields": [ (4)
      {
        "field": "id",
        "type": "string",
        "optional": false
      }
    ]
  },
  "payload": { (5)
    "id": "1004"
  }
}

This example uses a document with an integer identifier, but any valid MongoDB document identifier works the same way, including a document identifier. For a document identifier, an event key’s payload.id value is a string that represents the updated document’s original _id field as a MongoDB extended JSON serialization that uses strict mode. The following table provides examples of how different types of _id fields are represented.

Change event values

The value in a change event is a bit more complicated than the key. Like the key, the value has a schema section and a payload section. The schema section contains the schema that describes the Envelope structure of the payload section, including its nested fields. Change events for operations that create, update or delete data all have a value payload with an envelope structure.

Consider the same sample document that was used to show an example of a change event key:

Example document

{
  "_id": 1004,
  "first_name": "Anne",
  "last_name": "Kretchmar",
  "email": "annek@noanswer.org"
}

The value portion of a change event for a change to this document is described for each event type:

• create events

• update events

• delete events

• Tombstone events

create events

The following example shows the value portion of a change event that the connector generates for an operation that creates data in the customers collection:

{
    "schema": { (1)
      "type": "struct",
      "fields": [
        {
          "type": "string",
          "optional": true,
          "name": "io.debezium.data.Json", (2)
          "version": 1,
          "field": "after"
        },
        {
          "type": "string",
          "optional": true,
          "name": "io.debezium.data.Json",
          "version": 1,
          "field": "patch"
        },
        {
          "type": "struct",
          "fields": [
            {
              "type": "string",
              "optional": false,
              "field": "version"
            },
            {
              "type": "string",
              "optional": false,
              "field": "connector"
            },
            {
              "type": "string",
              "optional": false,
              "field": "name"
            },
            {
              "type": "int64",
              "optional": false,
              "field": "ts_ms"
            },
            {
              "type": "boolean",
              "optional": true,
              "default": false,
              "field": "snapshot"
            },
            {
              "type": "string",
              "optional": false,
              "field": "db"
            },
            {
              "type": "string",
              "optional": false,
              "field": "rs"
            },
            {
              "type": "string",
              "optional": false,
              "field": "collection"
            },
            {
              "type": "int32",
              "optional": false,
              "field": "ord"
            },
            {
              "type": "int64",
              "optional": true,
              "field": "h"
            }
          ],
          "optional": false,
          "name": "io.debezium.connector.mongo.Source", (3)
          "field": "source"
        },
        {
          "type": "string",
          "optional": true,
          "field": "op"
        },
        {
          "type": "int64",
          "optional": true,
          "field": "ts_ms"
        }
      ],
      "optional": false,
      "name": "dbserver1.inventory.customers.Envelope" (4)
      },
    "payload": { (5)
      "after": "{\"_id\" : {\"$numberLong\" : \"1004\"},\"first_name\" : \"Anne\",\"last_name\" : \"Kretchmar\",\"email\" : \"annek@noanswer.org\"}", (6)
      "source": { (7)
        "version": "2.0.0.Final",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": false,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 31,
        "h": 1546547425148721999
      },
      "op": "c", (8)
      "ts_ms": 1558965515240 (9)
    }
  }

update events

Change streams capture mode

The value of a change event for an update in the sample customers collection has the same schema as a create event for that collection. Likewise, the event value’s payload has the same structure. However, the event value payload contains different values in an update event. An update event does have an after value only if the capture.mode option is set to change_streams_update_full. A before value would be provided if the capture.mode option is set to one of the *_with_pre_image option. There is a new structured field updateDescription with a few additional fields in this case:

• updatedFields is a string field that contains the JSON representation of the updated document fields with their values

• removedFields is a list of field names that were removed from the document

• truncatedArrays is a list of arrays in the document that were truncated

Here is an example of a change event value in an event that the connector generates for an update in the customers collection:

{
    "schema": { ... },
    "payload": {
      "op": "u", (1)
      "ts_ms": 1465491461815, (2)
      "before":"{\"_id\": {\"$numberLong\": \"1004\"},\"first_name\": \"unknown\",\"last_name\": \"Kretchmar\",\"email\": \"annek@noanswer.org\"}", (3)
      "after":"{\"_id\": {\"$numberLong\": \"1004\"},\"first_name\": \"Anne Marie\",\"last_name\": \"Kretchmar\",\"email\": \"annek@noanswer.org\"}", (4)
      "updateDescription": {
        "removedFields": null,
        "updatedFields": "{\"first_name\": \"Anne Marie\"}", (5)
        "truncatedArrays": null
      },
      "source": { (6)
        "version": "2.0.0.Final",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": false,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 1,
        "h": null,
        "tord": null,
        "stxnid": null,
        "lsid":"{\"id\": {\"$binary\": \"FA7YEzXgQXSX9OxmzllH2w==\",\"$type\": \"04\"},\"uid\": {\"$binary\": \"47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=\",\"$type\": \"00\"}}",
        "txnNumber":1
      }
    }
  }

delete events

The value in a delete change event has the same schema portion as create and update events for the same collection. The payload portion in a delete event contains values that are different from create and update events for the same collection. In particular, a delete event contains neither an after value nor a updateDescription value. Here is an example of a delete event for a document in the customers collection:

{
    "schema": { ... },
    "payload": {
      "op": "d", (1)
      "ts_ms": 1465495462115, (2)
      "before":"{\"_id\": {\"$numberLong\": \"1004\"},\"first_name\": \"Anne Marie\",\"last_name\": \"Kretchmar\",\"email\": \"annek@noanswer.org\"}",(3)
      "source": { (4)
        "version": "2.0.0.Final",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": true,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 6,
        "h": 1546547425148721999
      }
    }
  }

MongoDB connector events are designed to work with Kafka log compaction. Log compaction enables removal of some older messages as long as at least the most recent message for every key is kept. This lets Kafka reclaim storage space while ensuring that the topic contains a complete data set and can be used for reloading key-based state.

Tombstone events

All MongoDB connector events for a uniquely identified document have exactly the same key. When a document is deleted, the delete event value still works with log compaction because Kafka can remove all earlier messages that have that same key. However, for Kafka to remove all messages that have that key, the message value must be null. To make this possible, after Debezium’s MongoDB connector emits a delete event, the connector emits a special tombstone event that has the same key but a null value. A tombstone event informs Kafka that all messages with that same key can be removed.

Setting up MongoDB

The MongoDB connector uses MongoDB’s change streams to capture the changes, so the connector works only with MongoDB replica sets or with sharded clusters where each shard is a separate replica set. See the MongoDB documentation for setting up a replica set or sharded cluster. Also, be sure to understand how to enable access control and authentication with replica sets.

You must also have a MongoDB user that has the appropriate roles to read the admin database where the oplog can be read. Additionally, the user must also be able to read the config database in the configuration server of a sharded cluster and must have listDatabases privilege action. When change streams are used (the default) the user also must have cluster-wide privilege actions find and changeStream.

When you intend to utilize pre-image and populate the before field, you need to first enable changeStreamPreAndPostImages for a collection using db.createCollection(), create, or collMod.

MongoDB in the Cloud

You can use the Debezium connector for MongoDB with MongoDB Atlas. Note that MongoDB Atlas only supports secure connections via SSL, i.e. the +mongodb.ssl.enabled connector option must be set to true.

Deployment

To deploy a Debezium MongoDB connector, you install the Debezium MongoDB connector archive, configure the connector, and start the connector by adding its configuration to Kafka Connect.

Prerequisites

• Apache Zookeeper, Apache Kafka, and Kafka Connect are installed.

• MongoDB is installed and is set up to work with the Debezium connector.

Procedure

1. Download the connector’s plug-in archive,

2. Extract the JAR files into your Kafka Connect environment.

3. Add the directory with the JAR files to Kafka Connect’s plugin.path.

4. Restart your Kafka Connect process to pick up the new JAR files.

If you are working with immutable containers, see Debezium’s Container images for Apache Zookeeper, Apache Kafka, and Kafka Connect with the MongoDB connector already installed and ready to run.

You can also run Debezium on Kubernetes and OpenShift.

The Debezium tutorial walks you through using these images, and this is a great way to learn about Debezium.

MongoDB connector configuration example

Following is an example of the configuration for a connector instance that captures data from a MongoDB replica set rs0 at port 27017 on 192.168.99.100, which we logically name fullfillment. Typically, you configure the Debezium MongoDB connector in a JSON file by setting the configuration properties that are available for the connector.

You can choose to produce events for a particular MongoDB replica set or sharded cluster. Optionally, you can filter out collections that are not needed.

{
  "name": "inventory-connector", (1)
  "config": {
    "connector.class": "io.debezium.connector.mongodb.MongoDbConnector", (2)
    "mongodb.hosts": "rs0/192.168.99.100:27017", (3)
    "topic.prefix": "fullfillment", (4)
    "collection.include.list": "inventory[.]*" (5)
  }
}

For the complete list of the configuration properties that you can set for the Debezium MongoDB connector, see MongoDB connector configuration properties.

You can send this configuration with a POST command to a running Kafka Connect service. The service records the configuration and starts one connector task that performs the following actions:

• Connects to the MongoDB replica set or sharded cluster.

• Assigns tasks for each replica set.

• Performs a snapshot, if necessary.

• Reads the change stream.

• Streams change event records to Kafka topics.

Adding connector configuration

To start running a Debezium MongoDB connector, create a connector configuration, and add the configuration to your Kafka Connect cluster.

Prerequisites

• MongoDB is set up to work with a Debezium connector.

• The Debezium MongoDB connector is installed.

Procedure

1. Create a configuration for the MongoDB connector.

2. Use the Kafka Connect REST API to add that connector configuration to your Kafka Connect cluster.

Results

After the connector starts, it completes the following actions:

• Performs a consistent snapshot of the collections in your MongoDB replica sets.

• Reads the change streams for the replica sets.

• Produces change events for every inserted, updated, and deleted document.

• Streams change event records to Kafka topics.

Connector properties

The Debezium MongoDB connector has numerous configuration properties that you can use to achieve the right connector behavior for your application. Many properties have default values. Information about the properties is organized as follows:

• Required Debezium MongoDB connector configuration properties

• Advanced Debezium MongoDB connector configuration properties

The following configuration properties are required unless a default value is available.

The following advanced configuration properties have good defaults that will work in most situations and therefore rarely need to be specified in the connector’s configuration.

Monitoring

The Debezium MongoDB connector has two metric types in addition to the built-in support for JMX metrics that Zookeeper, Kafka, and Kafka Connect have.

• Snapshot metrics provide information about connector operation while performing a snapshot.

• Streaming metrics provide information about connector operation when the connector is capturing changes and streaming change event records.

The Debezium monitoring documentation provides details about how to expose these metrics by using JMX.

Snapshot Metrics

The MBean is debezium.mongodb:type=connector-metrics,context=snapshot,server=*<mongodb.server.name>*,task=*<task.id>*.

Snapshot metrics are not exposed unless a snapshot operation is active, or if a snapshot has occurred since the last connector start.

The following table lists the shapshot metrics that are available.

The Debezium MongoDB connector also provides the following custom snapshot metrics:

Streaming Metrics

The MBean is debezium.mongodb:type=connector-metrics,context=streaming,server=*<mongodb.server.name>*,task=*<task.id>*.

The following table lists the streaming metrics that are available.

The Debezium MongoDB connector also provides the following custom streaming metrics:

MongoDB connector common issues

Debezium is a distributed system that captures all changes in multiple upstream databases, and will never miss or lose an event. When the system is operating normally and is managed carefully, then Debezium provides exactly once delivery of every change event.

If a fault occurs, the system does not lose any events. However, while it is recovering from the fault, it might repeat some change events. In such situations, Debezium, like Kafka, provides at least once delivery of change events.

The rest of this section describes how Debezium handles various kinds of faults and problems.

Configuration and startup errors

In the following situations, the connector fails when trying to start, reports an error or exception in the log, and stops running:

• The connector’s configuration is invalid.

• The connector cannot successfully connect to MongoDB by using the specified connection parameters.

After a failure, the connector attempts to reconnect by using exponential backoff. You can configure the maximum number of reconnection attempts.

In these cases, the error will have more details about the problem and possibly a suggested work around. The connector can be restarted when the configuration has been corrected or the MongoDB problem has been addressed.

MongoDB becomes unavailable

Once the connector is running, if the primary node of any of the MongoDB replica sets become unavailable or unreachable, the connector will repeatedly attempt to reconnect to the primary node, using exponential backoff to prevent saturating the network or servers. If the primary remains unavailable after the configurable number of connection attempts, the connector will fail.

The attempts to reconnect are controlled by three properties:

• connect.backoff.initial.delay.ms - The delay before attempting to reconnect for the first time, with a default of 1 second (1000 milliseconds).

• connect.backoff.max.delay.ms - The maximum delay before attempting to reconnect, with a default of 120 seconds (120,000 milliseconds).

• connect.max.attempts - The maximum number of attempts before an error is produced, with a default of 16.

Each delay is double that of the prior delay, up to the maximum delay. Given the default values, the following table shows the delay for each failed connection attempt and the total accumulated time before failure.

Kafka Connect process stops gracefully

If Kafka Connect is being run in distributed mode, and a Kafka Connect process is stopped gracefully, then prior to shutdown of that processes Kafka Connect will migrate all of the process’ connector tasks to another Kafka Connect process in that group, and the new connector tasks will pick up exactly where the prior tasks left off. There is a short delay in processing while the connector tasks are stopped gracefully and restarted on the new processes.

If the group contains only one process and that process is stopped gracefully, then Kafka Connect will stop the connector and record the last offset for each replica set. Upon restart, the replica set tasks will continue exactly where they left off.

Kafka Connect process crashes

If the Kafka Connector process stops unexpectedly, then any connector tasks it was running will terminate without recording their most recently-processed offsets. When Kafka Connect is being run in distributed mode, it will restart those connector tasks on other processes. However, the MongoDB connectors will resume from the last offset recorded by the earlier processes, which means that the new replacement tasks may generate some of the same change events that were processed just prior to the crash. The number of duplicate events depends on the offset flush period and the volume of data changes just before the crash.

Kafka becomes unavailable

As the connector generates change events, the Kafka Connect framework records those events in Kafka using the Kafka producer API. Kafka Connect will also periodically record the latest offset that appears in those change events, at a frequency that you have specified in the Kafka Connect worker configuration. If the Kafka brokers become unavailable, the Kafka Connect worker process running the connectors will simply repeatedly attempt to reconnect to the Kafka brokers. In other words, the connector tasks will simply pause until a connection can be reestablished, at which point the connectors will resume exactly where they left off.

Connector is stopped for a long interval

If the connector is gracefully stopped, the replica sets can continue to be used and any new changes are recorded in MongoDB’s oplog. When the connector is restarted, it will resume streaming changes for each replica set where it last left off, recording change events for all of the changes that were made while the connector was stopped. If the connector is stopped long enough such that MongoDB purges from its oplog some operations that the connector has not read, then upon startup the connector will perform a snapshot.

A properly configured Kafka cluster is capable of massive throughput. Kafka Connect is written with Kafka best practices, and given enough resources will also be able to handle very large numbers of database change events. Because of this, when a connector has been restarted after a while, it is very likely to catch up with the database, though how quickly will depend upon the capabilities and performance of Kafka and the volume of changes being made to the data in MongoDB.

MongoDB loses writes

In certain failure situations, MongoDB can lose commits, which results in the MongoDB connector being unable to capture the lost changes. For example, if the primary crashes suddenly after it applies a change and records the change to its oplog, the oplog might become unavailable before secondary nodes can read its contents. As a result, the secondary node that is elected as the new primary node might be missing the most recent changes from its oplog.

At this time, there is no way to prevent this side effect in MongoDB.