Elasticsearch reindex ignore errors


  • How to solve 8 common Elasticsearch errors
  • Kafka Connect Elasticsearch Connector in Action
  • Reindexing Data with Elasticsearch
  • Reindexing Elasticsearch documents using the new Reindex API
  • A Useful Elasticsearch Cheat Sheet in Times of Trouble
  • How To Fix The Forbidden 12 Read-Only API Error In Elasticsearch
  • How to solve 8 common Elasticsearch errors

    That is why Elasticsearch is very good tool for indexing logs. As you progress with your journey with Elasticsearch, Logstash, and Kibana, you will sometimes encounter the issue of having data that you have already indexed of which you want to change the mapping. This can be done, although you will have to reindex the data. Changing mappings can be a big headache if it causes downtime. The question is: does it have to cause downtime? When you decide to make mapping changes, you will have to reindex your data.

    Several strategies exist for reindexing data and preventing potential down time: Using Logstash for reindexing data, I came across an article on this very interesting and creative idea recently while doing research.

    This works very good at scale. The idea would be to use Elasticsearch as input to logstash and Elasticsearch as output, while using a template to define the mapping for the output index. You can do this natively in Elasticsearch. Playing around with this in Elasticsearch directly is a good idea in order for you to understand better how it can be down and how it works conceptually. To change mapping you usually need to reindex data to a new index with a different name to the original index.

    If you are using Elasticsearch in an application, for example in the search engine of a web application, then you will need to change the name of the index that you are using for the search engine if you change its mapping and reindex it to a new index. There seems to be lots of talk about using aliases to change mapping in order to reduce down time. This is how this workflow is supposed to work: Alias is created to point to index in use, which is the index that we plan to change the mapping of.

    We change the mapping of the old index by defining a new mapping and reindexing data from old index to a new index using the new mapping. Check to see if we are happy with mapping in new index created by reindexing old index with new index. Change alias to point from old index to new index. Remember to remove alias from pointing to the old index index because aliases can point to multiple indexes at once. Important: Make use of aliases instead of index names in your configuration files in your applications.

    Important considerations when working with mappings: An alias cannot be created without it pointing to something. Although an alias can be created to something, it is then told to point to nothing for a brief period of time. Think of it as a symlink that is created on unix. You cannot create a symlink to nothing. Remember that an alias can point to multiple indices.

    Which alias points to a certain index? Have a look at the code and the comments for the code:! Could have been different clusters Delete index so we know it doesn't exist. Put the code in a file reindex. The following example will demonstrate to you how to create an index and set a mapping for the index using the Python client API.

    Make sure that you delete the index if you already have an index by that name, before running this code. Save it into a file and run it. You can also do this in Sense. Therefore, setting the mapping for a new index is essential before reindexing data to the new index.

    Now we need to create an index with the data in it. Imagine that this was the index for which we wanted to change the mapping. By reindexing it to the new index with the mapping already set, we will have an index with the data in it but using the correct mapping. In a real world scenario our data would already be in an index.

    We can do that by checking the mapping. We can just do a quick search over the data. Working with mappings in real life is not always an easy task, especially if you want to keep capturing data into Elasticsearch while you are busy changing the mapping.

    An interesting tool that you can look at is called "elasticsearch-reindex". You can read more about it on the github page: elasticsearch-reindex. Discover how easy it is to manage and scale your Elasticsearch environment. Get Started 5 minutes to get started.

    Kafka Connect Elasticsearch Connector in Action

    Alternatively, you can perform real-time analytics on this data or use it with other applications like Kibana. For some background on what Elasticsearch is, you can read this blog post by Sarwar Bhuiyan. You can also learn more about Kafka Connect in this blog post by Tiffany Chang and in this presentation from Robin Moffatt. About the Elasticsearch sink connector The purpose of the Elasticsearch connector is to push events from Apache Kafka into an Elasticsearch index.

    They provide some useful examples which can be found on GitHub. For this blog post, I use this example. ElasticsearchSinkConnector", "tasks. ElasticsearchSinkConnector", "connection. DataException: Failed to deserialize data for topic simple. SerializationException: Error deserializing Avro message for id -1 Caused by: org. SerializationException: Unknown magic byte! When creating our Kafka Connect workers, we set a default converter for our keys and values in the Docker Compose file.

    This time, it failed. We now have to update our connector configuration to include the JSON converter. JsonConverter" "value. A document contains the message contents and a schema that describes the data. To learn more about converters and serialization in the world of Kafka Connect, this article by Robin Moffatt is extremely helpful. When we set this attribute to "true", only the value of the message will be inserted into Elasticsearch.

    JsonConverter", "value. We can utilize the Elasticsearch search API to verify the message has been indexed. We could also go one step further and use the TimestampRouter SMT to include a timestamp in the index name. An external supplier provides product listings in a topic named external. The records are in JSON format and include a key, schema, and the message payload. Also set the key. When the record is inserted into Elasticsearch, the embedded schema creates a mapping for the index instead of Elasticsearch working out the mapping from the data.

    The external suppliers have noticed an issue with one of the listings that requires attention: the price is incorrect. As messages are sent into Kafka with a key, the external supplier is able to send another record through to Kafka with amendments to the price, updating the record in Elasticsearch rather than creating a new record. Some product listing data is being inserted into a MySQL database that we want to send into Elasticsearch to drive a search function on our web application.

    The MySQL table is a pretty basic table with four columns. The ID field is auto incremented so that the connector is aware of new records being added to the table. Robin Moffatt wrote an amazing article on the JDBC source connector if you want to know more about the attributes used. JdbcSourceConnector", "connection.

    More on that later. If your records need a key, take a look at the ValueToKey SMT, which allows you to create a key for the record from attributes inside the record.

    This means the connector is using the default converter specified on the Kafka Connect worker, which in this case is Avro. The connector is taking data from MySQL and serializing it into the Avro format, creating a schema for that data and storing it in the Schema Registry.

    Rotating indexes A common use case for Elasticsearch is storing log data. Typically, log data is streamed into an array of time-bucketed indexes. Perhaps we want to use the Elasticsearch connector to stream data into a new index every day. Because the Elasticsearch connector uses the topic name as the index name in Elasticsearch, configure the connector to suffix the current day to the topic name.

    TimestampRouter", "transforms. Expiring and deleting old indexes can be done using the Elasticsearch Curator or manually using a daily job. Say we start working with a third party who provides us with product listings. What happens if there is an illegal character in one of the field names? The behavior. We get three options for this attribute: ignore, warn, or fail. We can ignore the problematic record, give a warning about it that will be visible in the logs, or fail it, which will cause the connector to catch fire and require fixing.

    Its purpose is to remove all messages in a compacted topic with the associated key. By default, a tombstone record is deleted after a day or based on whatever the value of delete. This way, consumers that might have been relying on this value for state are aware that the value for the key has been deleted. What happens when the Elasticsearch connector comes across a tombstone message? This is where the behavior.

    By default, the connector ignores tombstone messages, but this behavior can be modified so that a delete operation is performed instead.

    The supplier could send a tombstone record, which would delete the document from the index. JsonConverter", "key. StringConverter", "value. These parameters will largely depend on the volume of your Kafka topic, the size of your Elasticsearch cluster, the indexing latency, the size of the documents being indexed, and the memory in your Kafka Connect nodes.

    In addition to the number of tasks in your connector, the three main parameters to tune are: batch. If the task cannot keep up with this rate of indexing, two common events may happen: org. ConnectException: Flush timeout expired with unflushed records: this means the task could not flush all its buffered events to Elasticsearch within the timeout of 10 seconds. In this case, the connector rewinds to the last committed offset and attempts to reindex the whole buffer again. If it continues failing in this way, Elasticsearch may get stuck in a continuous index-fail-rewind loop.

    To fix this issue, either raise the flush. WARN Failed to execute batch of records, retrying after ms io. BulkProcessor java. SocketTimeoutException: Read timed out: this means that a single batch could not be flushed to Elasticsearch within the socket timeout.

    The connector will retry and eventually crash, usually meaning your batch. Of course, every deployment is different so the numbers you settle on will depend on your infrastructure and requirements. Take some time to ensure your connectors are configured to optimize for throughput and will not crash or get stuck, especially when they are catching up on a backlog of data and are indexing at their maximum rate.

    Summary Not all applications are designed and built for integration, and there are many different approaches to integrating multiple applications. Kafka Connect solves these challenges. Confluent provides a wide variety of sink and source connectors for popular databases and filesystems that can be used to stream data in and out of Kafka. With the Elasticsearch sink connector, we can stream data from Kafka into Elasticsearch and utilize the many features Kibana has to offer.

    To get started, you can download the Elasticsearch sink connector from the Confluent Hub. He specializes in cloud-native architecture, microservices, NoSQL, event streaming, and integration solutions. When not architecting, he likes to spend time with his family and keep up to date with current technology. He loves striped shirts, patterned socks, and a good cup of tea. Liz Bennett has specialized in the realm of event streaming infrastructure her whole career.

    While there, she submitted several patches to Kafka Connect, including the Elasticsearch connector. Being such a huge fan of Kafka, she recently decided to join Confluent, where she is now a software engineer working on Confluent Cloud.

    Did you like this blog post? Share it now Subscribe to the Confluent blog Subscribe.

    Reindexing Data with Elasticsearch

    In this article we see how we can change the Elastic Search mapping without losing data. What we do in these situations is to create an alias for our indexes. Each alias refers to a specific version of our index at a time. Now suppose we want to change something about mapping that cannot be done without deleting the index.

    At a high level, what we can do is creating an index with the new mapping using reindex API to move the data to the new index. Then change the alias to refer to our newly created index. Here is an image describing this method. I create some queries which demonstrate the ideas. Suppose we have an index with this specification.

    Reindexing Elasticsearch documents using the new Reindex API

    But if we just try to change the mapping using the put mapping API we receive the following error. In our previous index we had Latitude and Longitude at the root level of our index without having a separate key for it. Now I want to also move those into the Coordinates field.

    When we are re-indexing the Reference is going to be automatically converted to text type. As messages are sent into Kafka with a key, the external supplier is able to send another record through to Kafka with amendments to the price, updating the record in Elasticsearch rather than creating a new record.

    A Useful Elasticsearch Cheat Sheet in Times of Trouble

    Some product listing data is being inserted into a MySQL database that we want to send into Elasticsearch to drive a search function on our web application. The MySQL table is a pretty basic table with four columns.

    The ID field is auto incremented so that the connector is aware of new records being added to the table. Robin Moffatt wrote an amazing article on the JDBC source connector if you want to know more about the attributes used. JdbcSourceConnector", "connection. More on that later. If your records need a key, take a look at the ValueToKey SMT, which allows you to create a key for the record from attributes inside the record.

    This means the connector is using the default converter specified on the Kafka Connect worker, which in this case is Avro. The connector is taking data from MySQL and serializing it into the Avro format, creating a schema for that data and storing it in the Schema Registry. Rotating indexes A common use case for Elasticsearch is storing log data. Typically, log data is streamed into an array of time-bucketed indexes.

    Perhaps we want to use the Elasticsearch connector to stream data into a new index every day. Because the Elasticsearch connector uses the topic name as the index name in Elasticsearch, configure the connector to suffix the current day to the topic name.

    TimestampRouter", "transforms. Expiring and deleting old indexes can be done using the Elasticsearch Curator or manually using a daily job. Say we start working with a third party who provides us with product listings. What happens if there is an illegal character in one of the field names? The behavior. We get three options for this attribute: ignore, warn, or fail.

    We can ignore the problematic record, give a warning about it that will be visible in the logs, or fail it, which will cause the connector to catch fire and require fixing. Its purpose is to remove all messages in a compacted topic with the associated key.

    By default, a tombstone record is deleted after a day or based on whatever the value of delete. This way, consumers that might have been relying on this value for state are aware that the value for the key has been deleted. What happens when the Elasticsearch connector comes across a tombstone message? This is where the behavior.

    How To Fix The Forbidden 12 Read-Only API Error In Elasticsearch

    By default, the connector ignores tombstone messages, but this behavior can be modified so that a delete operation is performed instead. This will output a lot of verbose data. If you look at the end of the output, you will see the reason for the non-allocation. Note: If you drain a node and want to return it to the cluster afterward, you need to call that endpoint again with the IP field blank.

    This will place a sync ID on all indices, and as long as you are not writing to them, the recovery time of those shards will be significantly faster. Setting a higher value will help to rebalance the cluster when a new node joins it. After a certain delay, the shards will be allocated somewhere else. The number of concurrent shards per node that will be recovered is determined by that setting.


    Elasticsearch reindex ignore errors