DOCS-618 - Refactor Connect docs (#233) (2bdd510c) · Commits · REDMIC Project / Confluent / Kafka Connect Elasticsearch

docs/changelog.rst

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		Version 4.1.1
		-------------

		* `PR-191 <https://github.com/confluentinc/kafka-connect-elasticsearch/pull/191>`_ - CC-918 Add Kafka Connect Maven plugin to build
		* `PR-191 <https://github.com/confluentinc/kafka-connect-elasticsearch/pull/191>`_ - CC-918 Add \|kconnect\| Maven plugin to build

		Version 4.1.0
		-------------

docs/elasticsearch_connector.rst

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		.. _elasticsearch-overview:

		Elasticsearch Connector
		=======================
		The Elasticsearch connector allows moving data from Kafka to Elasticsearch. It writes data from
		a topic in Kafka to an `index <https://www.elastic.co/guide/en/elasticsearch/reference/current/_basic_concepts.html#_index>`_
		in Elasticsearch and all data for a topic have the same
		`type <https://www.elastic.co/guide/en/elasticsearch/reference/current/_basic_concepts.html#_type>`_.

		Elasticsearch is often used for text queries, analytics and as an key-value store
		(`use cases <https://www.elastic.co/blog/found-uses-of-elasticsearch>`_). The connector covers
		both the analytics and key-value store use cases. For the analytics use case,
		each message is in Kafka is treated as an event and the connector uses ``topic+partition+offset``
		as a unique identifier for events, which then converted to unique documents in Elasticsearch.
		For the key-value store use case, it supports using keys from Kafka messages as document ids in
		Elasticsearch and provides configurations ensuring that updates to a key are written to Elasticsearch
		in order. For both use cases, Elasticsearch's idempotent write semantics guarantees exactly once
		delivery.

		`Mapping <https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping.html>`_ is the
		process of defining how a document, and the fields it contains, are stored and indexed. Users can
		explicitly define mappings for types in indices. When mapping is not explicitly defined,
		Elasticsearch can determine field names and types from data, however, some types such as timestamp
		and decimal, may not be correctly inferred. To ensure that the types are correctly inferred, the
		connector provides a feature to infer mapping from the schemas of Kafka messages.

		.. _elasticsearch-quickstart:

		Quick Start
		-----------
		This quick start uses the Elasticsearch connector to export data produced by the Avro console
		producer to Elasticsearch.

		For a more detailed Docker-based example of the Confluent Elasticsearch Connector, check out :ref:`Confluent Platform demo<cp-demo>`.
		With this demo you can deploy a Kafka streaming ETL, including Elasticsearch, using KSQL for stream processing.

		Prerequisites:

		- :ref:`Confluent Platform <installation>` is installed and services are running by using the Confluent CLI. This quick start assumes that you are using the Confluent CLI, but standalone installations are also supported. By default ZooKeeper, Kafka, Schema Registry, Kafka Connect REST API, and Kafka Connect are started with the ``confluent start`` command. For more information, see :ref:`installation_archive`.
		- Elasticsearch 2.x, 5.x, or 6.x is installed and running.

		----------------------------
		Add a Record to the Consumer
		----------------------------

		Start the Avro console producer to import a few records to Kafka:

		.. sourcecode:: bash

		<path-to-confluent>/bin/kafka-avro-console-producer --broker-list localhost:9092 --topic test-elasticsearch-sink \
		--property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"f1","type":"string"}]}'

		Then in the console producer, enter:

		.. sourcecode:: bash

		{"f1": "value1"}
		{"f1": "value2"}
		{"f1": "value3"}

		The three records entered are published to the Kafka topic ``test-elasticsearch`` in Avro format.

		--------------------------------
		Load the Elasticsearch Connector
		--------------------------------

		Load the predefined Elasticsearch connector.

		.. tip:: Before starting the connector, you can verify that the configurations in ``etc/kafka-connect-elasticsearch/quickstart-elasticsearch.properties`` are properly set (e.g. ``connection.url`` points to the correct HTTP address).

		#. Optional: View the available predefined connectors with this command:

		.. sourcecode:: bash

		confluent list connectors

		Your output should resemble:

		.. sourcecode:: bash

		Bundled Predefined Connectors (edit configuration under etc/):
		elasticsearch-sink
		file-source
		file-sink
		jdbc-source
		jdbc-sink
		hdfs-sink
		s3-sink

		#. Load the the ``elasticsearch-sink`` connector:

		.. sourcecode:: bash

		confluent load elasticsearch-sink

		Your output should resemble:

		.. sourcecode:: bash

		{
		"name": "elasticsearch-sink",
		"config": {
		"connector.class": "io.confluent.connect.elasticsearch.ElasticsearchSinkConnector",
		"tasks.max": "1",
		"topics": "test-elasticsearch-sink",
		"key.ignore": "true",
		"connection.url": "http://localhost:9200",
		"type.name": "kafka-connect",
		"name": "elasticsearch-sink"
		},
		"tasks": [],
		"type": null
		}

		.. tip:: For non-CLI users, you can load the Elasticsearch connector by running Connect in standalone mode with this command:

		.. sourcecode:: bash

		$ ./bin/connect-standalone etc/schema-registry/connect-avro-standalone.properties \
		etc/kafka-connect-elasticsearch/quickstart-elasticsearch.properties


		#. After the connector finishes ingesting data to Elasticsearch, check that the data is available in Elasticsearch:

		.. sourcecode:: bash

		$ curl -XGET 'http://localhost:9200/test-elasticsearch-sink/_search?pretty'


		Your output should resemble:

		.. sourcecode:: bash

		{
		"took" : 39,
		"timed_out" : false,
		"_shards" : {
		"total" : 5,
		"successful" : 5,
		"skipped" : 0,
		"failed" : 0
		},
		"hits" : {
		"total" : 3,
		"max_score" : 1.0,
		"hits" : [
		{
		"_index" : "test-elasticsearch-sink",
		"_type" : "kafka-connect",
		"_id" : "test-elasticsearch-sink+0+0",
		"_score" : 1.0,
		"_source" : {
		"f1" : "value1"
		}
		},
		{
		"_index" : "test-elasticsearch-sink",
		"_type" : "kafka-connect",
		"_id" : "test-elasticsearch-sink+0+2",
		"_score" : 1.0,
		"_source" : {
		"f1" : "value3"
		}
		},
		{
		"_index" : "test-elasticsearch-sink",
		"_type" : "kafka-connect",
		"_id" : "test-elasticsearch-sink+0+1",
		"_score" : 1.0,
		"_source" : {
		"f1" : "value2"
		}
		}
		]
		}
		}


		Features
		--------
		The Elasticsearch connector offers a bunch of features:

		* Exactly Once Delivery: The connector relies on Elasticsearch's idempotent write semantics to
		ensure exactly once delivery to Elasticsearch. By setting ids in Elasticsearch documents, the
		connector can ensure exactly once delivery. If keys are included in Kafka messages, these keys
		are translated to Elasticsearch document ids automatically. When the keys are not included,
		or are explicitly ignored, the connector will use ``topic+partition+offset`` as the key,
		ensuring each message in Kafka has exactly one document corresponding to it in Elasticsearch.

		* Mapping Inference: The connector can infer mappings from the Kafka Connect schemas. When
		enabled, the connector creates mappings based on schemas of Kafka messages. However, the inference
		is limited to field types and default values when a field is missing. If more customizations are
		needed (e.g. user defined analyzers), we highly recommend to manually create mappings.

		* Schema Evolution: The connector supports schema evolution and can handle backward, forward and
		fully compatible changes of schemas in Kafka Connect. It can also handle some incompatible schema
		changes such as changing a field from integer to string.

		Delivery Semantics
		------------------
		The connector supports batching and pipelined writes to Elasticsearch to boost throughput. It
		accumulates messages in batches and allows concurrent processing of multiple batches.

		Document-level update ordering is ensured by using the partition-level Kafka offset as the
		`document version <https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-index_.html#index-versioning>`_,
		and using ``version_mode=external``.

		Mapping Management
		------------------
		Before using the connector, you need to think carefully on how the data should be tokenized,
		analyzed and indexed, which are determined by mapping. Some changes are not allowed after a mapping
		is already defined. Although you can add new types to an index, or add new fields to a type, you
		can’t add new analyzers or make changes to existing fields. If you were to do so, the data that
		had already been indexed would be incorrect and your searches would no longer work as expected.
		It is highly recommended that to manually define mappings before writing data to Elasticsearch.

		`Index templates <https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-templates.html>`_
		can be helpful when manually define mappings. It allows you to define templates that will
		automatically be applied when new indices are created. The templates include both settings and
		mappings, and a simple pattern template that controls whether the template should be applied to
		the new index.

		Schema Evolution
		----------------
		The Elasticsearch connector writes data from different topics in Kafka to different indices. All
		data for a topic will have the same type in Elasticseearch. This allows independent evolution of
		schemas for data from different topics. This simplifies the schema evolution as Elasticsearch has
		one enforcement on mappings: all fields with the same name in the same index must have the same
		mapping.

		Elasticsearch supports dynamic mapping: when it encounters previously unknown field in a document,
		it uses `dynamic mapping <https://www.elastic.co/guide/en/elasticsearch/guide/current/dynamic-mapping.html>`_
		to determine the datatype for the field and automatically adds the new field to the type mapping.

		When dynamic mapping is enabled, the Elasticsearch connector supports schema evolution as mappings
		in Elasticsearch are more flexible than the schema evolution allowed in Kafka Connect when different
		converters are used. For example, when the Avro converter is used, backward, forward and fully
		compatible schema evolutions are allowed.

		When dynamic mapping is enabled, the Elasticsearch connector allows the following schema changes:

		* Adding Fields: Adding one or more fields to Kafka messages. Elasticsearch will add the new
		fields to the mapping when dynamic mapping is enabled.
		* Removing Fields: Removing one or more fields to Kafka messages. Missing fields will be treated
		as the null value defined for those fields in the mapping.
		* Changing types that can be merged: Changing a field from string type to integer type.
		For example, Elasticsearch can convert integers to strings.

		The following change is not allowed:

		* Changing types that can not be merged: Changing a field from integer type to string type.

		As mappings are more flexible, schema compatibility should be enforced when writing data to Kafka.

		Automatic Retries
		-----------------
		The Elasticsearch connector may experience problems writing to the Elasticsearch endpoint, such as when
		the Elasticsearch service is temporarily overloaded. In many cases, the connector will retry the request
		a number of times before failing. To prevent from further overloading the Elasticsearch service, the connector
		uses an exponential backoff technique to give the Elasticsearch service time to recover. The technique
		adds randomness, called jitter, to the calculated backoff times to prevent a thundering herd, where large
		numbers of requests from many tasks are submitted concurrently and overwhelm the service. Randomness spreads out
		the retries from many tasks and should reduce the overall time required to complete all outstanding requests
		compared to simple exponential backoff. The goal is to spread out the requests to Elasticsearch as much as
		possible.

		The number of retries is dictated by the ``max.retries`` connector configuration property, which defaults
		to 5 attempts. The backoff time, which is the amount of time to wait before retrying, is a function of the
		retry attempt number and the initial backoff time specified in the ``retry.backoff.ms`` connector configuration
		property, which defaults to 500 milliseconds. For example, the following table shows the possible wait times
		before submitting each of the 5 retry attempts:

		.. table:: Range of backoff times for each retry using the default configuration
		:widths: auto

		===== ===================== ===================== ==============================================
		Retry Minimum Backoff (sec) Maximum Backoff (sec) Total Potential Delay from First Attempt (sec)
		===== ===================== ===================== ==============================================
		1 0.0 0.5 0.5
		2 0.0 1.0 1.5
		3 0.0 2.0 3.5
		4 0.0 4.0 7.5
		5 0.0 8.0 15.5
		===== ===================== ===================== ==============================================

		Note how the maximum wait time is simply the normal exponential backoff, calculated as ``${retry.backoff.ms} * 2 ^ (retry-1)``.
		Increasing the maximum number of retries adds more backoff:

		.. table:: Range of backoff times for additional retries
		:widths: auto

		===== ===================== ===================== ==============================================
		Retry Minimum Backoff (sec) Maximum Backoff (sec) Total Potential Delay from First Attempt (sec)
		===== ===================== ===================== ==============================================
		6 0.0 16.0 31.5
		7 0.0 32.0 63.5
		8 0.0 64.0 127.5
		9 0.0 128.0 255.5
		10 0.0 256.0 511.5
		11 0.0 512.0 1023.5
		12 0.0 1024.0 2047.5
		13 0.0 2048.0 4095.5
		===== ===================== ===================== ==============================================

		By increasing ``max.retries`` to 10, the connector may take up to 511.5 seconds, or a little over 8.5 minutes,
		to successfully send a batch of records when experiencing an overloaded Elasticsearch service. Increasing the value
		to 13 quickly increases the maximum potential time to submit a batch of records to well over 1 hour 8 minutes.

		You can adjust both the ``max.retries`` and ``retry.backoff.ms`` connector configuration properties to achieve
		the desired backoff and retry characteristics.


		Reindexing
		----------
		In some cases, the way to index a set of documents may need to be changed. For example, the analyzer,
		tokenizer and which fields are indexed may need to be changed. As those cannot be changed once a
		mapping is defined, we have to reindex the data.
		`Index aliases <https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-aliases.html>`_
		can be used to achieve reindexing with zero downtime. Here are the steps at needs to be performed
		in Elasticsearch:

		1. Create an alias for the index with the old mapping.
		2. The applications that uses the index are pointed to the alias.
		3. Create a new index with the updated mapping.
		4. Move data from old to the new index.
		5. Atomically move the alias to the new index.
		6. Delete the old index.

		For zero downtime reindexing, there are still write requests coming during the reindex period.
		As aliases do not allow writing to both the old and the new index at the same time. To solve this,
		the same data needs to be written both to the old and the new index.

		When the Elasticsearch connector is used to write data to Elasticsearch, we can use two
		connector jobs to achieve double writes:

		1. The connector job that ingest data to the old indices continue writing to the old indices.
		2. Create a new connector job that writes to new indices. This will copy both some old data and
		new data to the new indices as long as the data is in Kafka.
		3. Once the data in the old indices are moved to the new indices by the reindexing process, we
		can stop the old connector job.

		Security
		--------
		The Elasticsearch connector can read data from secure Kafka by following the instructions in the :ref:`Connect security documentation <connect_security>`.

		The Elasticsearch connector can write data to a secure Elasticsearch cluster that supports basic authentication by setting the ``connection.username`` and ``connection.password`` configuration properties.

docs/index.rst

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