Debezium connector for MongoDB

Debezium’s MongoDB connector tracks a MongoDB replica set or a MongoDB sharded cluster for document changes in databases and collections, recording those changes as events in Kafka topics. The connector automatically handles the addition or removal of shards in a sharded cluster, changes in membership of each replica set, elections within each replica set, and awaiting the resolution of communications problems.

For information about the MongoDB versions that are compatible with this connector, see the Debezium release overview.

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.

MongoDB connector supports two distinct modes of capturing the changes controlled by the capture.mode option:

oplog based
change streams based

Oplog capture mode (legacy)

The Debezium MongoDB connector uses the same replication mechanism as described above, though it does not actually become a member of the replica set. Just like MongoDB secondaries, however, the connector always reads the oplog of the replica set’s primary. 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 starts streaming changes from the position it read earlier from the oplog. Operations in the MongoDB oplog are idempotent, so no matter how many times the operations are applied, they result in the same end state.

The disadvantage of this mode is that only insert change events will contain the full document, whereas update events only contain a representation of changed fields (i.e. unmodified fields cannot be obtained from an update event), and delete events contain no representation of the deleted document apart from its key.

This mode should be considered as the legacy one. It is not supported on MongoDB 5 and the user is strongly advised to not use it for MongoDB 4.x server.

Change Stream mode

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.

This is the recommended mode starting with MongoDB 4.x.

Both capture modes use different values stored in offsets that allow them to resume streaming from the last position seen after a connector restart. Thus it is not possible to switch from the change streams mode to the oplog mode. To prevent any inadvertent capture mode changes, the connector has a built-in safety check.

When the connector is started it checks the stored offsets. If the original capture mode was oplog-based and the new mode is change streams based, then it will try to migrate to change streams. If the original capture mode was change streams based, it will keep using change streams, also if the new mode is oplog-based, and a warning about this will be emitted to the logs.

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

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.

When running the connector against a sharded cluster, use a value of tasks.max that is greater than the number of replica sets. This will allow the connector to create one task for each replica set, and will let Kafka Connect coordinate, distribute, and manage the tasks across all of the available worker processes.

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.

When MongoDB is fronted by a proxy (such as with Docker on OS X or Windows), then when a client connects to the replica set and discovers the members, the MongoDB client will exclude the proxy as a valid member and will attempt and fail to connect directly to the members rather than go through the proxy.

In such a case, set the connector’s optional mongodb.members.auto.discover configuration property to false to instruct the connector to forgo membership discovery and instead simply use the first seed address (specified via the mongodb.hosts property) as the primary node. This may work, but still make cause issues when election occurs.

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.

MongoDB does not recommend running a standalone server in production. For more information, see the MongoDB documentation.

Logical connector name

The connector configuration property mongodb.name 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 oplog/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.

Try to avoid task reassignment and reconfiguration while the connector performs snapshots of any replica sets. The connector generates log messages to report on the progress of the snapshot. To provide for the greatest control, run a separate Kafka Connect cluster for each connector.

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:

Table 1. Example of an ad hoc `execute-snapshot` signal record
Field	Default	Value
`type`	`incremental`	Specifies the type of snapshot that you want to run. Setting the type is optional. Currently, you can request only `incremental` snapshots.
`data-collections`	N/A	An array that contains the fully-qualified names of the collection to be snapshotted. The format of the names is the same as for the `signal.data.collection` configuration option.

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, 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 and the connector is configured to capture it.
- The signaling data collection is specified in the signal.data.collection property.

Procedure

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 to include in the snapshot. The array lists 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
}

Item Field name Description

Item	Field name	Description
1	`snapshot`	Specifies the type 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.
2	`op`	Specifies the event type. The value for snapshot events is `r`, signifying a `READ` operation.

snapshot

Specifies the type 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.

op

Specifies the event type.
The value for snapshot events is r, signifying a READ operation.

Incremental snapshots are currently supported for single replica set deployments only. This limitation will be removed in the next version.

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.

When everything is operating nominally, Kafka consumers will actually see every message exactly once. However, when things go wrong Kafka can only guarantee consumers will see every message at least once. Therefore, your consumers need to anticipate seeing messages more than once.

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.

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 mongodb.name 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.

Limits on when Debezium receives transaction metadata

Debezium registers and receives metadata only for transactions that occur after you deploy the connector. Metadata for transactions that occur before you deploy the connector is not available.

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 transaction.topic option, transaction events are written to the topic named database.server.name.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:

{
  "patch": null,
  "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)
   ...
 },
}

Table 3. Overview of change event basic content
Item	Field name	Description
1	`schema`	The first `schema` field is part of the event key. It specifies a Kafka Connect schema that describes what is in the event key’s `payload` portion. In other words, the first `schema` field describes the structure of the key for the document that was changed.
2	`payload`	The first `payload` field is part of the event key. It has the structure described by the previous `schema` field and it contains the key for the document that was changed.
3	`schema`	The second `schema` field is part of the event value. It specifies the Kafka Connect schema that describes what is in the event value’s `payload` portion. In other words, the second `schema` describes the structure of the document that was changed. Typically, this schema contains nested schemas.
4	`payload`	The second `payload` field is part of the event value. It has the structure described by the previous `schema` field and it contains the actual data for the document that was changed.

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.

The MongoDB connector ensures that all Kafka Connect schema names adhere to the Avro schema name format. This means that the logical server name must start with a Latin letter or an underscore, that is, a-z, A-Z, or _. Each remaining character in the logical server name and each character in the database and collection names must be a Latin letter, a digit, or an underscore, that is, a-z, A-Z, 0-9, or \_. If there is an invalid character it is replaced with an underscore character.

This can lead to unexpected conflicts if the logical server name, a database name, or a collection name contains invalid characters, and the only characters that distinguish names from one another are invalid and thus replaced with underscores.

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"
  }
}

Table 4. Description of change event key
Item	Field name	Description
1	`schema`	The schema portion of the key specifies a Kafka Connect schema that describes what is in the key’s `payload` portion.
2	`fulfillment.inventory.customers.Key`	Name of the schema that defines the structure of the key’s payload. This schema describes the structure of the key for the document that was changed. Key schema names have the format connector-name.database-name.collection-name.`Key`. In this example: `fulfillment` is the name of the connector that generated this event. `inventory` is the database that contains the collection that was changed. `customers` is the collection that contains the document that was updated.
3	`optional`	Indicates whether the event key must contain a value in its `payload` field. In this example, a value in the key’s payload is required. A value in the key’s payload field is optional when a document does not have a key.
4	`fields`	Specifies each field that is expected in the `payload`, including each field’s name, type, and whether it is required.
5	`payload`	Contains the key for the document for which this change event was generated. In this example, the key contains a single `id` field of type `string` whose value is `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.

Table 5. Examples of representing document `_id` fields in event key payloads
Type	MongoDB `_id` Value	Key’s payload
Integer	1234	`{ "id" : "1234" }`
Float	12.34	`{ "id" : "12.34" }`
String	"1234"	`{ "id" : "\"1234\"" }`
Document	`{ "hi" : "kafka", "nums" : [10.0, 100.0, 1000.0] }`	`{ "id" : "{\"hi\" : \"kafka\", \"nums\" : [10.0, 100.0, 1000.0]}" }`
ObjectId	`ObjectId("596e275826f08b2730779e1f")`	`{ "id" : "{\"$oid\" : \"596e275826f08b2730779e1f\"}" }`
Binary	`BinData("a2Fma2E=",0)`	`{ "id" : "{\"$binary\" : \"a2Fma2E=\", \"$type\" : \"00\"}" }`

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)
      "patch": null,
      "source": { (7)
        "version": "2.0.0.Alpha1",
        "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)
    }
  }

Table 6. Descriptions of *create* event value fields
Item	Field name	Description
1	`schema`	The value’s schema, which describes the structure of the value’s payload. A change event’s value schema is the same in every change event that the connector generates for a particular collection.
2	`name`	In the `schema` section, each `name` field specifies the schema for a field in the value’s payload. `io.debezium.data.Json` is the schema for the payload’s `after`, `patch`, and `filter` fields. This schema is specific to the `customers` collection. A create event is the only kind of event that contains an `after` field. An update event contains a `filter` field and a `patch` field. A delete event contains a `filter` field, but not an `after` field nor a `patch` field.
3	`name`	`io.debezium.connector.mongo.Source` is the schema for the payload’s `source` field. This schema is specific to the MongoDB connector. The connector uses it for all events that it generates.
4	`name`	`dbserver1.inventory.customers.Envelope` is the schema for the overall structure of the payload, where `dbserver1` is the connector name, `inventory` is the database, and `customers` is the collection. This schema is specific to the collection.
5	`payload`	The value’s actual data. This is the information that the change event is providing. It may appear that the JSON representations of the events are much larger than the documents they describe. This is because the JSON representation must include the schema and the payload portions of the message. However, by using the Avro converter, you can significantly decrease the size of the messages that the connector streams to Kafka topics.
6	`after`	An optional field that specifies the state of the document after the event occurred. In this example, the `after` field contains the values of the new document’s `_id`, `first_name`, `last_name`, and `email` fields. The `after` value is always a string. By convention, it contains a JSON representation of the document. MongoDB’s oplog entries contain the full state of a document only for _create_ events and also for `update` events, when the `capture.mode` option is set to `change_streams_update_full`; in other words, a create event is the only kind of event that contains an after field, when the `capture.mode` option is set either to `oplog` or `change_streams`.
7	`source`	Mandatory field that describes the source metadata for the event. This field contains information that you can use to compare this event with other events, with regard to the origin of the events, the order in which the events occurred, and whether events were part of the same transaction. The source metadata includes: Debezium version. Name of the connector that generated the event. Logical name of the MongoDB replica set, which forms a namespace for generated events and is used in Kafka topic names to which the connector writes. Names of the collection and database that contain the new document. If the event was part of a snapshot. Timestamp for when the change was made in the database and ordinal of the event within the timestamp. Unique identifier of the MongoDB operation, which depends on the version of MongoDB. It is either the `h` field in the oplog event, or a field named `stxnid`, which represents the `lsid` and `txnNumber` fields from the oplog event (oplog capture mode only). Unique identifiers of the MongoDB session `lsid` and transaction number `txnNumber` in case the change was executed inside a transaction (change streams capture mode only).
8	`op`	Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, `c` indicates that the operation created a document. Valid values are: `c` = create `u` = update `d` = delete `r` = read (applies to only snapshots)
9	`ts_ms`	Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task. In the `source` object, `ts_ms` indicates the time that the change was made in the database. By comparing the value for `payload.source.ts_ms` with the value for `payload.ts_ms`, you can determine the lag between the source database update and Debezium.

update events

Oplog capture mode (legacy)

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 not have an after value. Instead, it has these two fields:

patch is a string field that contains the JSON representation of the idempotent update operation
filter is a string field that contains the JSON representation of the selection criteria for the update. The filter string can include multiple shard key fields for sharded collections.

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)
      "patch": "{\"$set\":{\"first_name\":\"Anne Marie\"}}", (3)
      "filter": "{\"_id\" : {\"$numberLong\" : \"1004\"}}", (4)
      "source": { (5)
        "version": "2.0.0.Alpha1",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": false,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 6,
        "h": 1546547425148721999
      }
    }
  }

Table 7. Descriptions of *update* event value fields
Item	Field name	Description
1	`op`	Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, `u` indicates that the operation updated a document.
2	`ts_ms`	Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task. In the `source` object, `ts_ms` indicates the time that the change was made in the database. By comparing the value for `payload.source.ts_ms` with the value for `payload.ts_ms`, you can determine the lag between the source database update and Debezium.
3	`patch`	Contains the JSON string representation of the actual MongoDB idempotent change to the document. In this example, the update changed the `first_name` field to a new value. An update event value does not contain an `after` field.
4	`filter`	Contains the JSON string representation of the MongoDB selection criteria that was used to identify the document to be updated.
5	`source`	Mandatory field that describes the source metadata for the event. This field contains the same information as a create event for the same collection, but the values are different since this event is from a different position in the oplog. The source metadata includes: Debezium version. Name of the connector that generated the event. Logical name of the MongoDB replica set, which forms a namespace for generated events and is used in Kafka topic names to which the connector writes. Names of the collection and database that contain the updated document. If the event was part of a snapshot. Timestamp for when the change was made in the database and ordinal of the event within the timestamp. Unique identifier of the MongoDB operation, which depends on the version of MongoDB. It is either the `h` field in the oplog event, or a field named `stxnid`, which represents the `lsid` and `txnNumber` fields from the oplog event.

In a Debezium change event, MongoDB provides the content of the patch field. The format of this field depends on the version of the MongoDB database. Consequently, be prepared for potential changes to the format when you upgrade to a newer MongoDB database version. Examples in this document were obtained from MongoDB 3.4, In your application, event formats might be different.

In MongoDB’s oplog, update events do not contain the before or after states of the changed document. Consequently, it is not possible for a Debezium connector to provide this information. However, a Debezium connector provides a document’s starting state in create and read events. Downstream consumers of the stream can reconstruct document state by keeping the latest state for each document and comparing the state in a new event with the saved state. Debezium connector’s are not able to keep this state.

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. 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)
      "after":"{\"_id\": {\"$numberLong\": \"1004\"},\"first_name\": \"Anne Marie\",\"last_name\": \"Kretchmar\",\"email\": \"annek@noanswer.org\"}", (3)
      "updateDescription": {
        "removedFields": null,
        "updatedFields": "{\"first_name\": \"Anne Marie\"}", (4)
        "truncatedArrays": null
      },
      "source": { (5)
        "version": "2.0.0.Alpha1",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": false,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 1,
        "h": null,
        "tord": null,
        "stxnid": null,
        "lsid":"{\"id\": {\"$binary\": \"FA7YEzXgQXSX9OxmzllH2w==\",\"$type\": \"04\"},\"uid\": {\"$binary\": \"47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=\",\"$type\": \"00\"}}",
        "txnNumber":1
      }
    }
  }

Table 8. Descriptions of *update* event value fields
Item	Field name	Description
1	`op`	Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, `u` indicates that the operation updated a document.
2	`ts_ms`	Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task. In the `source` object, `ts_ms` indicates the time that the change was made in the database. By comparing the value for `payload.source.ts_ms` with the value for `payload.ts_ms`, you can determine the lag between the source database update and Debezium.
3	`after`	Contains the JSON string representation of the actual MongoDB document. An update event value does not contain an `after` field if the capture mode is not set to `change_streams_update_full`
4	`updatedFields`	Contains the JSON string representation of the updated field values of the document. In this example, the update changed the `first_name` field to a new value.
5	`source`	Mandatory field that describes the source metadata for the event. This field contains the same information as a create event for the same collection, but the values are different since this event is from a different position in the oplog. The source metadata includes: Debezium version. Name of the connector that generated the event. Logical name of the MongoDB replica set, which forms a namespace for generated events and is used in Kafka topic names to which the connector writes. Names of the collection and database that contain the updated document. If the event was part of a snapshot. Timestamp for when the change was made in the database and ordinal of the event within the timestamp. Unique identifiers of the MongoDB session `lsid` and transaction number `txnNumber` in case the change was executed inside a transaction.

The after value in the event should be handled as the at-point-of-time value of the document. The value is not calculated dynamically but is obtained from the collection. It is thus possible if multiple updates are closely following one after the other, that all update updates events will contain the same after value which will be representing the last value stored in the document.

If your application depends on gradual change evolution then you should rely on updateDescription only.

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 patch or updateDescription values. Here is an example of a delete event for a document in the customers collection:

{
    "schema": { ... },
    "payload": {
      "op": "d", (1)
      "ts_ms": 1465495462115, (2)
      "filter": "{\"_id\" : {\"$numberLong\" : \"1004\"}}", (3)
      "source": { (4)
        "version": "2.0.0.Alpha1",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": true,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 6,
        "h": 1546547425148721999
      }
    }
  }

Table 9. Descriptions of *delete* event value fields
Item	Field name	Description
1	`op`	Mandatory string that describes the type of operation. The `op` field value is `d`, signifying that this document was deleted.
2	`ts_ms`	Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task. In the `source` object, `ts_ms` indicates the time that the change was made in the database. By comparing the value for `payload.source.ts_ms` with the value for `payload.ts_ms`, you can determine the lag between the source database update and Debezium.
3	`filter`	Contains the JSON string representation of the MongoDB selection criteria that was used to identify the document to be deleted (oplog capture mode only).
4	`source`	Mandatory field that describes the source metadata for the event. This field contains the same information as a create or update event for the same collection, but the values are different since this event is from a different position in the oplog. The source metadata includes: Debezium version. Name of the connector that generated the event. Logical name of the MongoDB replica set, which forms a namespace for generated events and is used in Kafka topic names to which the connector writes. Names of the collection and database that contained the deleted document. If the event was part of a snapshot. Timestamp for when the change was made in the database and ordinal of the event within the timestamp. Unique identifier of the MongoDB operation, which depends on the version of MongoDB. It is either the `h` field in the oplog event, or a field named `stxnid`, which represents the `lsid` and `txnNumber` fields from the oplog event (oplog capture mode only). Unique identifiers of the MongoDB session `lsid` and transaction number `txnNumber` in case the change was executed inside a transaction (change streams capture mode only).

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 oplog/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.

MongoDB in the Cloud

You can use the Debezium connector for MongoDB with MongoDB Atlas. When connecting Debezium to MongoDB Atlas, enable one of the capture modes to be based on change streams, rather than oplog. 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

Download the connector’s plug-in archive,
Extract the JAR files into your Kafka Connect environment.
Add the directory with the JAR files to Kafka Connect’s plugin.path.
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)
    "mongodb.name": "fullfillment", (4)
    "collection.include.list": "inventory[.]*" (5)
  }
}

1	The name of our connector when we register it with a Kafka Connect service.
2	The name of the MongoDB connector class.
3	The host addresses to use to connect to the MongoDB replica set.
4	The logical name of the MongoDB replica set, which forms a namespace for generated events and is used in all the names of the Kafka topics to which the connector writes, the Kafka Connect schema names, and the namespaces of the corresponding Avro schema when the Avro converter is used.
5	A list of regular expressions that match the collection namespaces (for example, <dbName>.<collectionName>) of all collections to be monitored. This is optional.

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 oplog/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

Create a configuration for the MongoDB connector.
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 oplogs/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.

Property Default Description

name

Unique name for the connector. Attempting to register again with the same name will fail. (This property is required by all Kafka Connect connectors.)

connector.class

The name of the Java class for the connector. Always use a value of io.debezium.connector.mongodb.MongoDbConnector for the MongoDB connector.

mongodb.hosts

The comma-separated list of hostname and port pairs (in the form 'host' or 'host:port') of the MongoDB servers in the replica set. The list can contain a single hostname and port pair. If mongodb.members.auto.discover is set to false, then the host and port pair should be prefixed with the replica set name (e.g., rs0/localhost:27017).

It is mandatory to provide the current primary address. This limitation will be removed in the next Debezium release.

mongodb.name

A unique name that identifies the connector and/or MongoDB replica set or sharded cluster that this connector monitors. Each server should be monitored by at most one Debezium connector, since this server name prefixes all persisted Kafka topics emanating from the MongoDB replica set or cluster. Use only alphanumeric characters, hyphens, dots and underscores to form the name. The logical name should be unique across all other connectors, because the name is used as the prefix in naming the Kafka topics that receive records from this connector.

Do not change the value of this property. If you change the name value, after a restart, instead of continuing to emit events to the original topics, the connector emits subsequent events to topics whose names are based on the new value.

mongodb.user

Name of the database user to be used when connecting to MongoDB. This is required only when MongoDB is configured to use authentication.

mongodb.password

Password to be used when connecting to MongoDB. This is required only when MongoDB is configured to use authentication.

mongodb.authsource

admin

Database (authentication source) containing MongoDB credentials. This is required only when MongoDB is configured to use authentication with another authentication database than admin.

mongodb.ssl.enabled

false

Connector will use SSL to connect to MongoDB instances.

mongodb.ssl.invalid.hostname.allowed

false

When SSL is enabled this setting controls whether strict hostname checking is disabled during connection phase. If true the connection will not prevent man-in-the-middle attacks.

database.include.list

empty string

An optional comma-separated list of regular expressions that match database names to be monitored; any database name not included in database.include.list is excluded from monitoring. By default all databases are monitored. Must not be used with database.exclude.list.

database.exclude.list

empty string

An optional comma-separated list of regular expressions that match database names to be excluded from monitoring; any database name not included in database.exclude.list is monitored. Must not be used with database.include.list.

collection.include.list

empty string

An optional comma-separated list of regular expressions that match fully-qualified namespaces for MongoDB collections to be monitored; any collection not included in collection.include.list is excluded from monitoring. Each identifier is of the form databaseName.collectionName. By default the connector will monitor all collections except those in the local and admin databases. Must not be used with collection.exclude.list.

collection.exclude.list

empty string

An optional comma-separated list of regular expressions that match fully-qualified namespaces for MongoDB collections to be excluded from monitoring; any collection not included in collection.exclude.list is monitored. Each identifier is of the form databaseName.collectionName. Must not be used with collection.include.list.

snapshot.mode

initial

Specifies the criteria for running a snapshot upon startup of the connector. The default is initial, and specifies that the connector reads a snapshot when either no offset is found or if the oplog/change stream no longer contains the previous offset. The never option specifies that the connector should never use snapshots, instead the connector should proceed to tail the log.

capture.mode

change_streams_update_full

Specifies the method used to capture changes from the MongoDB server. The default is change_streams_update_full, and specifies that the connector captures changes via MongoDB Change Streams mechanism, and that update events should contain the full document. The change_streams mode will use the same capturing method, but update events won’t contain the full document.
The oplog mode specifies that the MongoDB oplog will be accessed directly; this is the legacy method and should not be used for new connector instances.

snapshot.include.collection.list

All collections specified in collection.include.list

An optional, comma-separated list of regular expressions that match names of schemas specified in collection.include.list for which you want to take the snapshot.

field.exclude.list

empty string

An optional comma-separated list of the fully-qualified names of fields that should be excluded from change event message values. Fully-qualified names for fields are of the form databaseName.collectionName.fieldName.nestedFieldName, where databaseName and collectionName may contain the wildcard (*) which matches any characters.

field.renames

empty string

An optional comma-separated list of the fully-qualified replacements of fields that should be used to rename fields in change event message values. Fully-qualified replacements for fields are of the form databaseName.collectionName.fieldName.nestedFieldName:newNestedFieldName, where databaseName and collectionName may contain the wildcard (*) which matches any characters, the colon character (:) is used to determine rename mapping of field. The next field replacement is applied to the result of the previous field replacement in the list, so keep this in mind when renaming multiple fields that are in the same path.

tasks.max

1

The maximum number of tasks that should be created for this connector. The MongoDB connector will attempt to use a separate task for each replica set, so the default is acceptable when using the connector with a single MongoDB replica set. When using the connector with a MongoDB sharded cluster, we recommend specifying a value that is equal to or more than the number of shards in the cluster, so that the work for each replica set can be distributed by Kafka Connect.

snapshot.max.threads

1

Positive integer value that specifies the maximum number of threads used to perform an intial sync of the collections in a replica set. Defaults to 1.

tombstones.on.delete

true

Controls whether a delete event is followed by a tombstone event.

true - a delete operation is represented by a delete event and a subsequent tombstone event.

false - only a delete event is emitted.

After a source record is deleted, emitting a tombstone event (the default behavior) allows Kafka to completely delete all events that pertain to the key of the deleted row in case log compaction is enabled for the topic.

snapshot.delay.ms

An interval in milliseconds that the connector should wait before taking a snapshot after starting up;
Can be used to avoid snapshot interruptions when starting multiple connectors in a cluster, which may cause re-balancing of connectors.

snapshot.fetch.size

0

Specifies the maximum number of documents that should be read in one go from each collection while taking a snapshot. The connector will read the collection contents in multiple batches of this size.
Defaults to 0, which indicates that the server chooses an appropriate fetch size.

schema.name.adjustment.mode

avro

Specifies how schema names should be adjusted for compatibility with the message converter used by the connector. Possible settings:

avro replaces the characters that cannot be used in the Avro type name with underscore.
none does not apply any adjustment.

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.

Table 11. Debezium MongoDB connector advanced configuration properties
Property	Default	Description
`max.batch.size`	`2048`	Positive integer value that specifies the maximum size of each batch of events that should be processed during each iteration of this connector. Defaults to 2048.
`max.queue.size`	`8192`	Positive integer value that specifies the maximum number of records that the blocking queue can hold. When Debezium reads events streamed from the database, it places the events in the blocking queue before it writes them to Kafka. The blocking queue can provide backpressure for reading change events from the database in cases where the connector ingests messages faster than it can write them to Kafka, or when Kafka becomes unavailable. Events that are held in the queue are disregarded when the connector periodically records offsets. Always set the value of `max.queue.size` to be larger than the value of `max.batch.size`.
`max.queue.size.in.bytes`	`0`	A long integer value that specifies the maximum volume of the blocking queue in bytes. By default, volume limits are not specified for the blocking queue. To specify the number of bytes that the queue can consume, set this property to a positive long value. If `max.queue.size` is also set, writing to the queue is blocked when the size of the queue reaches the limit specified by either property. For example, if you set `max.queue.size=1000`, and `max.queue.size.in.bytes=5000`, writing to the queue is blocked after the queue contains 1000 records, or after the volume of the records in the queue reaches 5000 bytes.
`poll.interval.ms`	`1000`	Positive integer value that specifies the number of milliseconds the connector should wait during each iteration for new change events to appear. Defaults to 1000 milliseconds, or 1 second.
`connect.backoff.initial.delay.ms`	`1000`	Positive integer value that specifies the initial delay when trying to reconnect to a primary after the first failed connection attempt or when no primary is available. Defaults to 1 second (1000 ms).
`connect.backoff.max.delay.ms`	`1000`	Positive integer value that specifies the maximum delay when trying to reconnect to a primary after repeated failed connection attempts or when no primary is available. Defaults to 120 seconds (120,000 ms).
`connect.max.attempts`	`16`	Positive integer value that specifies the maximum number of failed connection attempts to a replica set primary before an exception occurs and task is aborted. Defaults to 16, which with the defaults for `connect.backoff.initial.delay.ms` and `connect.backoff.max.delay.ms` results in just over 20 minutes of attempts before failing.
`mongodb.members.auto.discover`	`true`	Boolean value that specifies whether the addresses in 'mongodb.hosts' are seeds that should be used to discover all members of the cluster or replica set (`true`), or whether the address(es) in `mongodb.hosts` should be used as is (`false`). The default is `true` and should be used in all cases except where MongoDB is fronted by a proxy.
`source.struct.version`	v2	Schema version for the `source` block in CDC events. Debezium 0.10 introduced a few breaking changes to the structure of the `source` block in order to unify the exposed structure across all the connectors. By setting this option to `v1` the structure used in earlier versions can be produced. Note that this setting is not recommended and is planned for removal in a future Debezium version.
`heartbeat.interval.ms`	`0`	Controls how frequently heartbeat messages are sent. This property contains an interval in milliseconds that defines how frequently the connector sends messages into a heartbeat topic. This can be used to monitor whether the connector is still receiving change events from the database. You also should leverage heartbeat messages in cases where only records in non-captured collections are changed for a longer period of time. In such situation the connector would proceed to read the oplog/change stream from the database but never emit any change messages into Kafka, which in turn means that no offset updates are committed to Kafka. This will cause the oplog files to be rotated out but connector will not notice it so on restart some events are no longer available which leads to the need of re-execution of the initial snapshot. Set this parameter to `0` to not send heartbeat messages at all. Disabled by default.
`heartbeat.topics.prefix`	`__debezium-heartbeat`	Controls the naming of the topic to which heartbeat messages are sent. The topic is named according to the pattern `<heartbeat.topics.prefix>.<server.name>`.
`sanitize.field.names`	`true` when connector configuration explicitly specifies the `key.converter` or `value.converter` parameters to use Avro, otherwise defaults to `false`.	Whether field names are sanitized to adhere to Avro naming requirements. See Avro naming for more details.
`skipped.operations`		comma-separated list of operation types that will be skipped during streaming. The operations include: `c` for inserts/create, `u` for updates, and `d` for deletes. By default, no operations are skipped.
`snapshot.collection.filter.overrides`		Controls which collection items are included in snapshot. This property affects snapshots only. Specify a comma-separated list of collection names in the form databaseName.collectionName. For each collection that you specify, also specify another configuration property: `snapshot.collection.filter.overrides.databaseName.collectionName`. For example, the name of the other configuration property might be: `snapshot.collection.filter.overrides.customers.orders`. Set this property to a valid filter expression that retrieves only the items that you want in the snapshot. When the connector performs a snapshot, it retrieves only the items that matches the filter expression.
`provide.transaction.metadata`	`false`	When set to `true` Debezium generates events with transaction boundaries and enriches data events envelope with transaction metadata. See Transaction Metadata for additional details.
`transaction.topic`	`${database.server.name}.transaction`	Controls the name of the topic to which the connector sends transaction metadata messages. The placeholder `${database.server.name}` can be used for referring to the connector’s logical name (see Logical connector name); defaults to `${database.server.name}.transaction`, for example `dbserver1.transaction`.
`retriable.restart.connector.wait.ms`	10000 (10 seconds)	The number of milliseconds to wait before restarting a connector after a retriable error occurs.
`mongodb.poll.interval.ms`	`30000`	The interval in which the connector polls for new, removed, or changed replica sets.
`mongodb.connect.timeout.ms`	10000 (10 seconds)	The number of milliseconds the driver will wait before a new connection attempt is aborted.
`mongodb.socket.timeout.ms`	0	The number of milliseconds before a send/receive on the socket can take before a timeout occurs. A value of `0` disables this behavior.
`mongodb.server.selection.timeout.ms`	30000 (30 seconds)	The number of milliseconds the driver will wait to select a server before it times out and throws an error.
`cursor.max.await.time.ms`	`0`	Specifies the maximum number of milliseconds the oplog/change stream cursor will wait for the server to produce a result before causing an execution timeout exception. A value of `0` indicates using the server/driver default wait timeout.
`signal.data.collection`	No default	Fully-qualified name of the data collection that is used to send signals to the connector. Use the following format to specify the collection name: `<databaseName>.<collectionName>`
`incremental.snapshot.chunk.size`	`1024`	The maximum number of documents that the connector fetches and reads into memory during an incremental snapshot chunk. Increasing the chunk size provides greater efficiency, because the snapshot runs fewer snapshot queries of a greater size. However, larger chunk sizes also require more memory to buffer the snapshot data. Adjust the chunk size to a value that provides the best performance in your environment.

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>.

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.

Attributes Type Description

Attributes	Type	Description
`LastEvent`	`string`	The last snapshot event that the connector has read.
`MilliSecondsSinceLastEvent`	`long`	The number of milliseconds since the connector has read and processed the most recent event.
`TotalNumberOfEventsSeen`	`long`	The total number of events that this connector has seen since last started or reset.
`NumberOfEventsFiltered`	`long`	The number of events that have been filtered by include/exclude list filtering rules configured on the connector.
`CapturedTables`	`string[]`	The list of tables that are captured by the connector.
`QueueTotalCapacity`	`int`	The length the queue used to pass events between the snapshotter and the main Kafka Connect loop.
`QueueRemainingCapacity`	`int`	The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop.
`TotalTableCount`	`int`	The total number of tables that are being included in the snapshot.
`RemainingTableCount`	`int`	The number of tables that the snapshot has yet to copy.
`SnapshotRunning`	`boolean`	Whether the snapshot was started.
`SnapshotAborted`	`boolean`	Whether the snapshot was aborted.
`SnapshotCompleted`	`boolean`	Whether the snapshot completed.
`SnapshotDurationInSeconds`	`long`	The total number of seconds that the snapshot has taken so far, even if not complete.
`RowsScanned`	`Map<String, Long>`	Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table.
`MaxQueueSizeInBytes`	`long`	The maximum buffer of the queue in bytes. This metric is available if `max.queue.size.in.bytes` is set to a positive long value.
`CurrentQueueSizeInBytes`	`long`	The current volume, in bytes, of records in the queue.

LastEvent

string

The last snapshot event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since last started or reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

CapturedTables

string[]

The list of tables that are captured by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the snapshotter and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop.

TotalTableCount

int

The total number of tables that are being included in the snapshot.

RemainingTableCount

int

The number of tables that the snapshot has yet to copy.

SnapshotRunning

boolean

Whether the snapshot was started.

SnapshotAborted

boolean

Whether the snapshot was aborted.

SnapshotCompleted

boolean

Whether the snapshot completed.

SnapshotDurationInSeconds

long

The total number of seconds that the snapshot has taken so far, even if not complete.

RowsScanned

Map<String, Long>

Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table.

MaxQueueSizeInBytes

long

The maximum buffer of the queue in bytes. This metric is available if max.queue.size.in.bytes is set to a positive long value.

CurrentQueueSizeInBytes

long

The current volume, in bytes, of records in the queue.

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

Attribute Type Description

Attribute	Type	Description
`NumberOfDisconnects`	`long`	Number of database disconnects.

NumberOfDisconnects

long

Number of database disconnects.

Streaming Metrics

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

The following table lists the streaming metrics that are available.

Attributes Type Description

Attributes	Type	Description
`LastEvent`	`string`	The last streaming event that the connector has read.
`MilliSecondsSinceLastEvent`	`long`	The number of milliseconds since the connector has read and processed the most recent event.
`TotalNumberOfEventsSeen`	`long`	The total number of events that this connector has seen since the last start or metrics reset.
`TotalNumberOfCreateEventsSeen`	`long`	The total number of create events that this connector has seen since the last start or metrics reset.
`TotalNumberOfUpdateEventsSeen`	`long`	The total number of update events that this connector has seen since the last start or metrics reset.
`TotalNumberOfDeleteEventsSeen`	`long`	The total number of delete events that this connector has seen since the last start or metrics reset.
`NumberOfEventsFiltered`	`long`	The number of events that have been filtered by include/exclude list filtering rules configured on the connector.
`CapturedTables`	`string[]`	The list of tables that are captured by the connector.
`QueueTotalCapacity`	`int`	The length the queue used to pass events between the streamer and the main Kafka Connect loop.
`QueueRemainingCapacity`	`int`	The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop.
`Connected`	`boolean`	Flag that denotes whether the connector is currently connected to the database server.
`MilliSecondsBehindSource`	`long`	The number of milliseconds between the last change event’s timestamp and the connector processing it. The values will incoporate any differences between the clocks on the machines where the database server and the connector are running.
`NumberOfCommittedTransactions`	`long`	The number of processed transactions that were committed.
`SourceEventPosition`	`Map<String, String>`	The coordinates of the last received event.
`LastTransactionId`	`string`	Transaction identifier of the last processed transaction.
`MaxQueueSizeInBytes`	`long`	The maximum buffer of the queue in bytes. This metric is available if `max.queue.size.in.bytes` is set to a positive long value.
`CurrentQueueSizeInBytes`	`long`	The current volume, in bytes, of records in the queue.

LastEvent

string

The last streaming event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since the last start or metrics reset.

TotalNumberOfCreateEventsSeen

long

The total number of create events that this connector has seen since the last start or metrics reset.

TotalNumberOfUpdateEventsSeen

long

The total number of update events that this connector has seen since the last start or metrics reset.

TotalNumberOfDeleteEventsSeen

long

The total number of delete events that this connector has seen since the last start or metrics reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

CapturedTables

string[]

The list of tables that are captured by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the streamer and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop.

Connected

boolean

Flag that denotes whether the connector is currently connected to the database server.

MilliSecondsBehindSource

long

The number of milliseconds between the last change event’s timestamp and the connector processing it. The values will incoporate any differences between the clocks on the machines where the database server and the connector are running.

NumberOfCommittedTransactions

long

The number of processed transactions that were committed.

SourceEventPosition

Map<String, String>

The coordinates of the last received event.

LastTransactionId

string

Transaction identifier of the last processed transaction.

MaxQueueSizeInBytes

long

The maximum buffer of the queue in bytes. This metric is available if max.queue.size.in.bytes is set to a positive long value.

CurrentQueueSizeInBytes

long

The current volume, in bytes, of records in the queue.

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

Attribute Type Description

Attribute	Type	Description
`NumberOfDisconnects`	`long`	Number of database disconnects.
`NumberOfPrimaryElections`	`long`	Number of primary node elections.

NumberOfDisconnects

long

Number of database disconnects.

NumberOfPrimaryElections

long

Number of primary node elections.

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.

Reconnection attempt number	Delay before attempt, in seconds	Total delay before attempt, in minutes and seconds
1	1	00:01
2	2	00:03
3	4	00:07
4	8	00:15
5	16	00:31
6	32	01:03
7	64	02:07
8	120	04:07
9	120	06:07
10	120	08:07
11	120	10:07
12	120	12:07
13	120	14:07
14	120	16:07
15	120	18:07
16	120	20:07

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.

Because there is a chance that some events may be duplicated during a recovery from failure, consumers should always anticipate some events may be duplicated. Debezium changes are idempotent, so a sequence of events always results in the same state.

Debezium also includes with each change event message the source-specific information about the origin of the event, including the MongoDB event’s unique transaction identifier (h) and timestamp (sec and ord). Consumers can keep track of other of these values to know whether it has already seen a particular event.

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.

If the connector remains stopped for long enough, MongoDB might purge older oplog files and the connector’s last position may be lost. In this case, when the connector configured with initial snapshot mode (the default) is finally restarted, the MongoDB server will no longer have the starting point and the connector will fail with an error.

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.