{prodname}'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 link:https://debezium.io/releases/[{prodname} release overview].
For information about the MongoDB versions that are compatible with this connector, see the link:{LinkDebeziumSupportedConfigurations}[{NameDebeziumSupportedConfigurations}].
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 https://docs.mongodb.com/manual/core/replica-set-sync/[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.
Although the {prodname} MongoDB connector does not become part of a replica set, it uses a similar replication mechanism to obtain oplog data.
The main difference is that the connector does not read the oplog directly.
Instead, it delegates the capture and decoding of oplog data to the MongoDB https://docs.mongodb.com/manual/changeStreams/[change streams] feature.
With change streams, the MongoDB server exposes the changes that occur in a collection as an event stream.
The {prodname} connector monitors the stream and then delivers the changes downstream.
The first time that the connector detects a replica set, it examines the oplog to obtain the last recorded transaction, and then performs a snapshot of the primary's databases and collections.
After the connector finishes copying the data, it creates a change stream beginning from the oplog position that it read earlier.
As the MongoDB connector processes changes, it periodically records the position at which the event originated in the oplog stream.
When the connector stops, it records the last oplog stream position that it processed, so that after a restart it can resume streaming from that position.
In other words, the connector can be stopped, upgraded or maintained, and restarted some time later, and always pick up exactly where it left off without losing a single event.
Of course, MongoDB oplogs are usually capped at a maximum size, so if the connector is stopped for long periods, operations in the oplog might be purged before the connector has a chance to read them.
In this case, after a restart the connector detects the missing oplog operations, performs a snapshot, and then proceeds to stream 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.
Similarly, if connector is unable to communicate with the replica set primary, it attempts to reconnect (using exponential backoff so as to not overwhelm the network or replica set).
After connection is reestablished, the connector continues to stream changes from the last event that it captured.
In this way the connector dynamically adjusts to changes in replica set membership, and automatically handles communication disruptions.
You can specify MongoDB read preferences for a connection in the connector properties.
The method that you use to set read preferences depends on the MongoDB topology, and the xref:mongodb-property-mongodb-connection-mode[`mongodb.connection.mode`].
Replica set topology::
Set the read preference in the xref:mongodb-property-mongodb-connection-string[`mongodb.connection.string`].
Sharded cluster topology::
Set the read preference based on the connection mode, as shown in the following table:
+
.Setting the read preference for a sharded cluster based on the `mongodb.connection.mode`
[cols="3,6",options="header"]
|===
|Connection mode |Property for specifying read preference
|`sharded`
|`mongodb.connection.string`
|`replica_set`
|`mongodb.connection.string.shard.params`
|===
+
In a sharded cluster, the connector first initiates a connection to the mongos router specified in the `mongodb.connection.string`.
For that initial connection, regardless of the connection mode, the connector honors the read preferences that are specified in the `mongodb.connection.string`.
When the connection mode is set to `replica_set`, after the connector establishes the initial router connection, it retrieves topology information from the router's `config.shards`.
It then uses the retrieved shard addresses to connect to individual shards in the cluster, constructing connection strings that use the connection parameters in xref:mongodb-property-mongodb-connection-string-shard-params[`mongodb.connection.string.shard.params`].
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.
====
ifdef::product[]
The following topics provide details about how the {prodname} MongoDB connector works:
To use the MongoDB connector with a replica set, you must set the value of the `mongodb.connection.string` property in the connector configuration to the https://www.mongodb.com/docs/manual/reference/connection-string/[replica set connection string].
Due to changes in the way that the connector manages database connections, this release of {prodname} no longer supports use of the `mongodb.members.auto.discover` property to prevent the connector from performing membership discovery.
To use the MongoDB connector with a sharded cluster, in the connector configuration, set the value of the `mongodb.connection.string` property to the https://www.mongodb.com/docs/manual/reference/connection-string/[sharded cluster connection string].
The `mongodb.connection.string` property replaces the deprecated `mongodb.hosts` property that was used to provide earlier versions of the connector with the host address of the _configuration server_ replica.
The connector user might also require the following permission:
* Read from the `config.shards` system collection.
.Database read permissions
The connector user must be able to read from all databases, or to read from a specific database, depending on the value of the connector's xref:mongodb-property-capture-scope[`capture.scope`] property.
Assign one of the following permissions to the user, depending on the `capture.scope` setting:
`capture.scope` is set to `deployment`::
Grant the user permission to read any database.
`capture.scope` is set to `database`::
Grant the user permission to read the database specified by the connector's xref:mongodb-property-capture-target[`capture.target`] property.
.Permission to use the MongoDB `ping` command
Regardless of the `capture.scope` setting, the user requires permission to run the MongoDB https://www.mongodb.com/docs/manual/reference/command/ping/[ping] command.
.Permission to read the `config.shards` collection
For connectors that capture changes from a sharded MongoDB cluster, and for which the xref:mongodb-property-mongodb-connection-mode[`mongodb.connection.mode`] property is set to `replica_set`, you must grant the user permission to read the `config.shards` system collection.
The connector uses the logical name in a number of ways: as the prefix for all topic names, and as a unique identifier when recording the change stream position of each replica set.
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 xref:mongodb-streaming-changes[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.
Ad hoc snapshots are a Technology Preview feature for the {prodname} MongoDB connector.
Technology Preview features are not supported with Red Hat production service-level agreements (SLAs) and might not be functionally complete;
therefore, Red Hat does not recommend implementing any Technology Preview features in production environments.
This Technology Preview feature provides early access to upcoming product innovations, enabling you to test functionality and provide feedback during the development process.
For more information about support scope, see link:https://access.redhat.com/support/offerings/techpreview/[Technology Preview Features Support Scope].
Incremental snapshots are a Technology Preview feature for the {prodname} MongoDB connector.
Technology Preview features are not supported with Red Hat production service-level agreements (SLAs) and might not be functionally complete;
therefore, Red Hat does not recommend implementing any Technology Preview features in production environments.
This Technology Preview feature provides early access to upcoming product innovations, enabling you to test functionality and provide feedback during the development process.
For more information about support scope, see link:https://access.redhat.com/support/offerings/techpreview/[Technology Preview Features Support Scope].
Incremental snapshots requires the primary key to be stably ordered. However, `String` may not guarantees stable ordering as encodings and special characters
can lead to unexpected behaviour (https://www.mongodb.com/docs/manual/reference/bson-types/#ref-sort-string-internationalization-id3/[Mongo sort `String`]).
Please consider using other types for the primary key when performing incremental snapshots.
To use incremental snapshots with sharded MongoDB clusters, you must set specific values for the following properties:
* Set xref:mongodb-property-mongodb-connection-mode[`mongodb.connection.mode`] to `sharded`.
* Set xref:mongodb-property-incremental-snapshot-chunk-size[`incremental.snapshot.chunk.size`] to a value that is high enough to compensate for the link:https://www.mongodb.com/docs/manual/administration/change-streams-production-recommendations/#sharded-clusters[increased complexity] of change stream pipelines.
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.
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 link:https://kafka.apache.org/documentation/#offset.flush.interval.ms[`offset.flush.interval.ms`], which is a Kafka Connect worker configuration property.
When the connector is stopped gracefully, the last offset processed is recorded so that, upon restart, the connector will continue exactly where it left off.
If the connector's tasks terminate unexpectedly, however, then the tasks may have processed and generated events after it last records the offset but before the last offset is recorded; upon restart, the connector begins at the last _recorded_ offset, possibly generating some the same events that were previously generated just prior to the crash.
As mentioned earlier, 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.
In MongoDB 6.0 and later, you can configure change streams to emit the pre-image state of a document to populate the `before` field for MongoDB change events.
To enable the use of pre-images in MongoDB, you must set the `changeStreamPreAndPostImages` for a collection by using `db.createCollection()`, `create`, or `collMod`.
To enable the {prodname} MongoDB to include pre-images in change events, set the `capture.mode` for the connector to one of the `*_with_pre_image` options.
The size of a MongoDB change stream event is limited to 16 megabytes.
The use of pre-images thus increases the likelihood of exceeding this threshold, which can lead to failures.
For information about how to avoid exceeding the change stream limit, see the https://www.mongodb.com/docs/manual/changeStreams/#change-streams-with-document-pre--and-post-images/[MongoDB documentation].
The name of the Kafka topics always takes the form _logicalName_._databaseName_._collectionName_, where _logicalName_ is the xref:mongodb-logical-connector-name[logical name] of the connector as specified with the `topic.prefix` configuration property, _databaseName_ is the name of the database where the operation occurred, and _collectionName_ is the name of the MongoDB collection in which the affected document existed.
For example, consider a MongoDB replica set with an `inventory` database that contains four collections: `products`, `products_on_hand`, `customers`, and `orders`.
If the connector monitoring this database were given a logical name of `fulfillment`, then the connector would produce events on these four Kafka topics:
For every transaction `BEGIN` and `END`, {prodname} 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 {prodname} 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.
{prodname} 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:
|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.
|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.
|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.
|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 xref:mongodb-topic-names[topic names].
The MongoDB connector ensures that all Kafka Connect schema names adhere to the link:http://avro.apache.org/docs/current/spec.html#names[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.
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 link:https://docs.mongodb.com/manual/reference/mongodb-extended-json/[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.
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:
a|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: +
* `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.
|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.
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.
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:
|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.
`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.
a|`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.
a|`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.
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 {link-prefix}:{link-avro-serialization}#avro-serialization[Avro converter], you can significantly decrease the size of the messages that the connector streams to Kafka topics.
|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 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 regardless of `capture.mode` option.
a|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:
* 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.
* 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).
a|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:
a|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 {prodname}.
a|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`
a|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 {prodname}.
|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.
a|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:
* {prodname} 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.
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.
The value in a _delete_ change event has the same `schema` portion as _create_ and _update_ events for the same collection. The `payload` portion in a _delete_ event contains values that are different from _create_ and _update_ events for the same collection. In particular, a _delete_ event contains neither an `after` value nor a `updateDescription` value. Here is an example of a _delete_ event for a document in the `customers` collection:
a|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 {prodname}.
a|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:
* 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.
* 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 link:{link-kafka-docs}/#compaction[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.
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 {prodname}’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.
The MongoDB connector uses MongoDB's change streams to capture the changes, so the connector works only with MongoDB replica sets or with sharded clusters where each shard is a separate replica set.
See the MongoDB documentation for setting up a https://docs.mongodb.com/manual/replication/[replica set] or https://docs.mongodb.com/manual/sharding/[sharded cluster].
Also, be sure to understand how to enable https://docs.mongodb.com/manual/tutorial/deploy-replica-set-with-keyfile-access-control/#deploy-repl-set-with-auth[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 you intend to utilize pre-image and populate the `before` field, you need to first enable `changeStreamPreAndPostImages` for a collection using `db.createCollection()`, `create`, or `collMod`.
Note that MongoDB Atlas only supports secure connections via SSL, i.e. the xref:mongodb-property-mongodb-ssl-enabled[`+mongodb.ssl.enabled`] connector option _must_ be set to `true`.
The {prodname} MongoDB connector works by obtaining changes from Oplog using change streams. As oplog is a capped collection, older entries are overwritten once maximum configured size is reached. For MongoDB connector to be able to deal with failures it is important that the oplog retention config is such that the resume point is available upon restart or retries. If the last oplog position is lost, the connector can not start again and a new connector has to be created afresh (See ).
To avoid this, you can provide a higher https://www.mongodb.com/docs/manual/core/replica-set-oplog/[Oplog size] or preferably a Minimum Oplog Window config available since MongoDB 4.4 (i.e. time-based, entries of the last X-hours are guaranteed to be available even if oplog size cap is reached.) The later is preferable but may not always be the option as it can have impact if the cluster is reaching its capacity and there is sudden load of data. Thus you can instead choose to provide a higher Oplog size based on you workloads, ideally should be more than peak Oplog/Hour.
E.g. If we have 3 GB oplog per hour and only 1 GB oplog size limit. Oplog entries will get cleared thrice an hour, which would be bad if Debezium fails during this time. Thus an oplog size of ? gb should be optimum for such workload.
To deploy a {prodname} MongoDB connector, you install the {prodname} MongoDB connector archive, configure the connector, and start the connector by adding its configuration to Kafka Connect.
.Prerequisites
* link:https://zookeeper.apache.org/[Apache Zookeeper], link:http://kafka.apache.org/[Apache Kafka], and link:{link-kafka-docs}.html#connect[Kafka Connect] are installed.
If you are working with immutable containers, see link:https://quay.io/organization/debezium[{prodname}'s Container images] for Apache Zookeeper, Apache Kafka, and Kafka Connect with the MongoDB connector already installed and ready to run.
To deploy a {prodname} MongoDB connector, you must build a custom Kafka Connect container image that contains the {prodname} connector archive and then push this container image to a container registry.
* You have an account and permissions to create and manage containers in the container registry (such as `quay.io` or `docker.io`) to which you plan to add the container that will run your Debezium connector.
|`metadata.annotations` indicates to the Cluster Operator that `KafkaConnector` resources are used to configure connectors in this Kafka Connect cluster.
|2
|`spec.image` specifies the name of the image that you created to run your Debezium connector.
<1> The name that is used to register the connector with Kafka Connect.
<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> An optional list of regular expressions that match the collection namespaces (for example, <dbName>.<collectionName>) of all collections to be monitored.
. Create your connector instance with Kafka Connect.
For example, if you saved your `KafkaConnector` resource in the `inventory-connector.yaml` file, you would run the following command:
+
[source,shell,options="nowrap"]
----
oc apply -f inventory-connector.yaml
----
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The preceding command registers `inventory-connector` and the connector starts to run against the `inventory` collection as defined in the `KafkaConnector` CR.
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 {prodname} MongoDB connector in a JSON file by setting the configuration properties that are available for the connector.
<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.
|Unique name for the connector. Attempting to register again with the same name will fail. (This property is required by all Kafka Connect connectors.)
|Specifies a https://www.mongodb.com/docs/manual/reference/connection-string/[connection string] that the connector uses to connect to a MongoDB replica set.
Connectors that capture changes from a sharded MongoDB cluster use this connection string only during the initial shard discovery process when xref:mongodb-property-mongodb-connection-mode[`mongodb.connection.mode`] is set to `replica_set`.
|Specifies the URL parameters of the https://www.mongodb.com/docs/manual/reference/connection-string/[connection string], including read preferences, that the connector uses to connect to individual shards of a MongoDB sharded cluster.
[NOTE]
====
This property applies only when the xref:mongodb-property-mongodb-connection-mode[`mongodb.connection.mode`] is set to `replica_set`.
`sharded`:: The connector establishes a single connection to the database, based on the value of the xref:mongodb-property-mongodb-connection-string[`mongodb.connection.string`].
|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 {prodname} 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. +
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.
|Database (authentication source) containing MongoDB credentials. This is required only when MongoDB is configured to use authentication with another authentication database than `admin`.
|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.
|An optional comma-separated list of regular expressions that match database names to be monitored.
By default, all databases are monitored. +
When `database.include.list` is set, the connector monitors only the databases that the property specifies.
Other databases are excluded from monitoring.
To match the name of a database, {prodname} applies the regular expression that you specify as an _anchored_ regular expression.
That is, the specified expression is matched against the entire name string of the database; it does not match substrings that might be present in a database name. +
If you include this property in the configuration, do not also set the `database.exclude.list` property.
|An optional comma-separated list of regular expressions that match database names to be excluded from monitoring.
When `database.exclude.list` is set, the connector monitors every database except the ones that the property specifies.
To match the name of a database, {prodname} applies the regular expression that you specify as an _anchored_ regular expression.
That is, the specified expression is matched against the entire name string of the database; it does not match substrings that might be present in a database name. +
If you include this property in the configuration, do not set the `database.include.list` property.
|An optional comma-separated list of regular expressions that match fully-qualified namespaces for MongoDB collections to be excluded from monitoring.
When `collection.exclude.list` is set, the connector monitors every collection except the ones that the property specifies.
Collection identifiers are of the form _databaseName_._collectionName_. +
To match the name of a namespace, {prodname} applies the regular expression that you specify as an _anchored_ regular expression.
That is, the specified expression is matched against the entire name string of the namespace; it does not match substrings that might be present in a database name. +
If you include this property in the configuration, do not set the `collection.include.list` property.
When the connector starts, it skips the snapshot process and immediately begins to stream change events for operations that the database records to the oplog.
|Specifies the method that the connector uses to capture `update` event changes from a MongoDB server.
Set this property to one of the following values:
`change_streams`:: `update` event messages do not include the full document.
Messages do not include a field that represents the state of the document `before` the change.
`change_streams_update_full`:: `update` event messages include the full document.
Messages do not include a `before` field that represents the state of the document before the update.
The event message returns the full state of the document in the `after` field.
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[NOTE]
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In some situations, when `capture.mode` is configured to return full documents, the `updateDescription` and `after` fields of the update event message might report inconsistent values.
Such discrepancies can result after multiple updates are applied to a document in rapid succession.
The connector requests the full document from the MongoDB database only after it receives the update described in the event's `updateDescription` field.
If a later update modifies the source document before the connector can retrieve it from the database, the connector receives the document that is modified by this later update.
When you configure `capture.mode` to return the full document, you might notice a discrepancy between the content in the `updateDescription` and `after` fields of the message.
Discrepancies can result when multiple changes are applied to a document in rapid succession.
Because the connector submits the request for the full document only after an update is applied, later updates can modify the source document after the update that is described in the `updateDescription` field.
The full document that the connector then receives in response to its query reflects the result of the later change.
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`change_streams_update_full_with_pre_image`::
`update` event event messages include the full document, and include a field that represents the state of the document `before` the change.
`change_streams_with_pre_image`::
`update` events do not include the full document, but include a field that represents the state of the document `before` the change.
|An optional, comma-separated list of regular expressions that match the fully-qualified names (`_<databaseName>_._<collectionName>_`) of the schemas that you want to include in a snapshot.
The specified items must be named in the connectors's xref:mongodb-property-collection-include-list[`collection.include.list`] property.
This property takes effect only if the connector's xref:mongodb-property-snapshot-mode[`snapshot.mode`] property is set to a value other than `never`. +
This property does not affect the behavior of incremental snapshots. +
To match the name of a schema, {prodname} applies the regular expression that you specify as an _anchored_ regular expression.
That is, the specified expression is matched against the entire name string of the schema; it does not match substrings that might be present in a schema name.
|Specifies the https://www.mongodb.com/docs/manual/changeStreams/#watch-a-collection--database--or-deployment[scope of the change streams] that the connector opens.
`deployment`:: Opens a change stream cursor for a deployment (either a replica set or a sharded cluster) to watch for changes to all non-system collections across all databases, except for `admin`, `local`, and `config`.
If you set the value of this property to `database`, to use {prodname} signaling, you the signaling collection that you create must reside in the database that is specified by the xref:mongodb-property-capture-target[`capture.target`] property.
|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.
|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.
But when a cluster contains multiple shards, to enable Kafka Connect to distribute the work for each replica set, specify a value that is equal to or greater than the number of shards in the cluster.
The MongoDB connector can then use a separate task to connect to the replica set for each shard in the cluster.
This property has an effect only when the connector is connected to a sharded MongoDB cluster and the xref:mongodb-property-mongodb-connection-mode[`mongodb.connection.mode`] property is set to `replica_set`.
When the xref:mongodb-property-mongodb-connection-mode[`mongodb.connection.mode`] is set to `sharded`, or if the connector is connected to an unsharded MongoDB replica set deployment, the connector ignores this setting, and defaults to using only a single task.
|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.
|Controls whether a _delete_ event is followed by a tombstone event. +
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`true` - a delete operation is represented by a _delete_ event and a subsequent tombstone event. +
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`false` - only a _delete_ event is emitted. +
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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 {link-kafka-docs}/#compaction[log compaction] is enabled for the topic.
* `avro_unicode` replaces the underscore or characters that cannot be used in the Avro type name with corresponding unicode like _uxxxx. Note: _ is an escape sequence like backslash in Java +
|Specifies how field names should be adjusted for compatibility with the message converter used by the connector. Possible settings: +
* `none` does not apply any adjustment. +
* `avro` replaces the characters that cannot be used in the Avro type name with underscore. +
* `avro_unicode` replaces the underscore or characters that cannot be used in the Avro type name with corresponding unicode like _uxxxx. Note: _ is an escape sequence like backslash in Java +
|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. +
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[NOTE]
====
This property is deprecated and should be replaced by xref:mongodb-property-mongodb-connection-string[`+mongodb.connection.string`].
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.
|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.
If xref:mongodb-property-max-queue-size[`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.
|Positive integer value that specifies the number of milliseconds the connector should wait during each iteration for new change events to appear. Defaults to 500 milliseconds, or 0.5 second.
|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).
|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).
|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.
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. +
The operations include: `c` for inserts/create, `u` for updates/replace, `d` for deletes, `t` for truncates, and `none` to not skip any aforementioned operations.
By default, for consistency with other Debezium connectors, truncate operations are skipped (not emitted by this connector). However, since MongoDB https://www.mongodb.com/docs/manual/reference/change-events/#operation-types[does not support] truncate change events, this is effectively the same as specifying `none`.
| 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.
|When streaming changes, this setting applies processing to change stream events as part of the standard MongoDB aggregation stream pipeline. A pipeline is a MongoDB aggregation pipeline composed of instructions to the database to filter or transform data. This can be used customize the data that the connector consumes.
The value of this property must be an array of permitted https://www.mongodb.com/docs/manual/changeStreams/#modify-change-stream-output[aggregation pipeline stages] in JSON format.
Note that this is appended after the internal pipeline used to support the connector (e.g. filtering operation types, database names, collection names, etc.).
|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.
| Fully-qualified name of the data collection that is used to send {link-prefix}:{link-signalling}#debezium-signaling-enabling-source-signaling-channel[signals] to the connector.
|The name of the TopicNamingStrategy class that should be used to determine the topic name for data change, schema change, transaction, heartbeat event etc., defaults to `DefaultTopicNamingStrategy`.
|The size used for holding the topic names in bounded concurrent hash map. This cache will help to determine the topic name corresponding to a given data collection.
|The custom metric tags will accept key-value pairs to customize the MBean object name which should be appended the end of regular name, each key would represent a tag for the MBean object name, and the corresponding value would be the value of that tag the key is. For example: `k1=v1,k2=v2`.
The {prodname} MongoDB connector has two metric types in addition to the built-in support for JMX metrics that Zookeeper, Kafka, and Kafka Connect have.
* xref:mongodb-snapshot-metrics[Snapshot metrics] provide information about connector operation while performing a snapshot.
* xref:mongodb-streaming-metrics[Streaming metrics] provide information about connector operation when the connector is capturing changes and streaming change event records.
The {link-prefix}:{link-debezium-monitoring}#monitoring-debezium[{prodname} monitoring documentation] provides details about how to expose these metrics by using JMX.
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.
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.
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.
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. {prodname} changes are idempotent, so a sequence of events always results in the same state.
{prodname} 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.
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.
Changes that occur while the connector is offline continue to be recorded in MongoDB's oplog.
In most cases, after the connector is restarted, it reads the offset value in the oplog to determine the last operation that it streamed for each replica set, and then resumes streaming changes from that point.
After the restart, database operations that occurred while the connector was stopped are emitted to Kafka as usual, and after some time, the connector catches up with the database.
The amount of time required for the connector to catch up depends on the capabilities and performance of Kafka and the volume of changes that occurred in the database.
However, if the connector remains stopped for a long enough interval, it can occur that MongoDB purges the oplog during the time that the connector is inactive, resulting in the loss of information about the connector's last position.
After the connector restarts, it cannot resume streaming, because the oplog no longer contains the previous offset value that marks the last operation that the connector processed.
The connector also cannot perform a snapshot, as it typically would when the `snapshot.mode` property is set to `initial`, and no offset value is present.
In this case, a mismatch exists, because the oplog does not contain the value of the previous offset, but the offset value is present in the connector's internal Kafka offsets topic.
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.