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This guide will help you check for common problems that cause the log ” Finished segment upgrade ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: index and upgrade.
Overview
In Elasticsearch, an index (plural: indices) contains a schema and can have one or more shards and replicas. An Elasticsearch index is divided into shards and each shard is an instance of a Lucene index.
Indices are used to store the documents in dedicated data structures corresponding to the data type of fields. For example, text fields are stored inside an inverted index whereas numeric and geo fields are stored inside BKD trees.
Examples
Create index
The following example is based on Elasticsearch version 5.x onwards. An index with two shards, each having one replica will be created with the name test_index1
PUT /test_index1?pretty
{
"settings" : {
"number_of_shards" : 2,
"number_of_replicas" : 1
},
"mappings" : {
"properties" : {
"tags" : { "type" : "keyword" },
"updated_at" : { "type" : "date" }
}
}
}List indices
All the index names and their basic information can be retrieved using the following command:
GET _cat/indices?v
Index a document
Let’s add a document in the index with the command below:
PUT test_index1/_doc/1
{
"tags": [
"opster",
"elasticsearch"
],
"date": "01-01-2020"
}Query an index
GET test_index1/_search
{
"query": {
"match_all": {}
}
}Query multiple indices
It is possible to search multiple indices with a single request. If it is a raw HTTP request, index names should be sent in comma-separated format, as shown in the example below, and in the case of a query via a programming language client such as python or Java, index names are to be sent in a list format.
GET test_index1,test_index2/_search
Delete indices
DELETE test_index1
Common problems
- It is good practice to define the settings and mapping of an Index wherever possible because if this is not done, Elasticsearch tries to automatically guess the data type of fields at the time of indexing. This automatic process may have disadvantages, such as mapping conflicts, duplicate data and incorrect data types being set in the index. If the fields are not known in advance, it’s better to use dynamic index templates.
- Elasticsearch supports wildcard patterns in Index names, which sometimes aids with querying multiple indices, but can also be very destructive too. For example, It is possible to delete all the indices in a single command using the following commands:
DELETE /*
To disable this, you can add the following lines in the elasticsearch.yml:
action.destructive_requires_name: true
Overview
Upgrade refers to migrating your Elasticsearch version to a newer version. An upgrade of an existing cluster can be done in two ways: through a rolling upgrade and through a full cluster restart. The benefit of a rolling upgrade is having zero downtime.
Common problems and important points
- The major problem with upgrades is version incompatibility. Elasticsearch supports rolling upgrades only between minor versions. You need to make sure to go through the official documentation to see if your cluster can support a rolling upgrade, otherwise a complete reindexing is required.
- Once you upgrade an Elasticsearch node, a rollback cannot be done. You need to make sure to backup your data before an upgrade.
- Elasticsearch continuously removes or deprecates some of its features with every release, so keep an eye on the change logs of each version before planning an upgrade.
- While doing a rolling upgrade, it is important to disable shard allocation before stopping a node and enable the shard allocation when node is upgraded and restarted. This process helps in avoiding unnecessary IO load in the cluster.
How to easily upgrade versions
To upgrade Elasticsearch versions without risking data loss, you can use Opster’s Multi-Cluster Load Balancer. The MCLB enables version upgrades with no downtime and upgrading from significantly older versions to new ones (such as bringing your Elasticsearch from v1 to v7 for instance).
With Opster’s Load Balancer, you can mirror data to newer versions of Elasticsearch without disabling the older version. This allows you to improve and adjust data structure and application API calls until all of the data in the new version is present and operational, while staying available the entire time.
You can learn more about the Multi-Cluster Load Balancer here, and book a demo to try it out.
Log Context
Log “Finished segment upgrade” classname is InternalEngine.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
if (tryRenewSyncCommit() == false) {
flush(false; true);
}
}
if (upgrade) {
logger.info("finished segment upgrade");
}
} finally {
store.decRef();
}
} catch (AlreadyClosedException ex) {
Find & fix Elasticsearch problems
