In addition to reading this guide, we recommend you run the Elasticsearch Template Optimizer to fix problems in your data modeling.
It will analyze your templates to detect issues and improve search performance, reduce indexing bottlenecks and optimize storage utilization. The Template Optimizer is free and requires no installation.
This guide will help you check for common problems that cause the log to appear. It’s important to understand the issues related to the log, so to get started, read the general overview on common issues and tips related to the Elasticsearch concepts below. Advanced users might want to skip right to the common problems section in each concept or run the template optimizer.
Overview
An Elasticsearch cluster consists of a number of servers (nodes) working together as one. Clustering is a technology which enables Elasticsearch to scale up to hundreds of nodes that together are able to store many terabytes of data and respond coherently to large numbers of requests at the same time.
Search or indexing requests will usually be load-balanced across the Elasticsearch data nodes, and the node that receives the request will relay requests to other nodes as necessary and coordinate the response back to the user.
Notes and good things to know
The key elements to clustering are:
Cluster State – Refers to information about which indices are in the cluster, their data mappings and other information that must be shared between all the nodes to ensure that all operations across the cluster are coherent.
Master Node – Each cluster must elect a single master node responsible for coordinating the cluster and ensuring that each node contains an up-to-date copy of the cluster state.
Cluster Formation – Elasticsearch requires a set of configurations to determine how the cluster is formed, which nodes can join the cluster, and how the nodes collectively elect a master node responsible for controlling the cluster state. These configurations are usually held in the elasticsearch.yml config file, environment variables on the node, or within the cluster state.
Node Roles – In small clusters it is common for all nodes to fill all roles; all nodes can store data, become master nodes or process ingestion pipelines. However as the cluster grows, it is common to allocate specific roles to specific nodes in order to simplify configuration and to make operation more efficient. In particular, it is common to define a limited number of dedicated master nodes.
Replication – Data may be replicated across a number of data nodes. This means that if one node goes down, data is not lost. It also means that a search request can be dealt with by more than one node.
Common problems
Many Elasticsearch problems are caused by operations which place an excessive burden on the cluster because they require an excessive amount of information to be held and transmitted between the nodes as part of the cluster state. For example:
- Shards too small
- Too many fields (field explosion)
Problems may also be caused by inadequate configurations causing situations where the Elasticsearch cluster is unable to safely elect a Master node. This situation is discussed further in:
Backups
Because Elasticsearch is a clustered technology, it is not sufficient to have backups of each node’s data directory. This is because the backups will have been made at different times and so there may not be complete coherency between them. As such, the only way to backup an Elasticsearch cluster is through the use of snapshots, which contain the full picture of an index at any one time.
Cluster resilience
When designing an Elasticsearch cluster, it is important to think about cluster resilience. In particular – what happens when a single node goes down? And for larger clusters where several nodes may share common services such as a network or power supply – what happens if that network or power supply goes down? This is where it is useful to ensure that the master eligible nodes are spread across availability zones, and to use shard allocation awareness to ensure that shards are spread across different racks or availability zones in your data center.
Overview
Deprecation refers to processes and functions that are in the process of being eliminated and (possibly) replaced by newer ones.
Typically, a function will not disappear from one version to the next without warning. Normally this will happen across a number of versions. When you use a deprecated function in intermediate versions, it will continue to work as before, but you will receive warnings that the function in question is intended to disappear in the future.
How it works
There are a number of ways you can find out which functions have been deprecated, including: deprecation logs, reading the breaking pages documentation and paying attention to warnings.
In a deprecation log:
{"type": "deprecation", "timestamp": "2020-01-16T12:50:11,263+0000", "level": "WARN", "component": "o.e.d.r.a.d.RestDeleteAction", "cluster.name": "docker-cluster", "node.name": "es01", "cluster.uuid": "VGTYFgunQ_STTKVz6YHAGg", "node.id": "wh5J7TJ-RD-pJE4JOUjVpw", "message": "[types removal] Specifying types in document index requests is deprecated, use the typeless endpoints instead (/{index}/_doc/{id}, /{index}/_doc, or /{index}/_create/{id})." }Reading the breaking changes documentation for each version:
https://www.elastic.co/guide/en/elasticsearch/reference/7.5/breaking-changes-7.0.html
In kibana you may also see a warning if you run a deprecated command in the development panel:
#! Deprecation: [types removal] Specifying types in document index requests is deprecated, use the typeless endpoints instead (/{index}/_doc/{id}, /{index}/_doc, or /{index}/_create/{id}).It is important to act upon these warnings. Although your application still works, ignoring the warnings will almost certainly cause things to malfunction in a future upgrade.
Deprecation API
There is a depreciation API, which can help point you to deprecated functions on your cluster:
| Version 5.6-6.8 | GET /_xpack/migration/deprecations |
| Version 7 | GET /_migration/deprecations |
However, you should never depend on the deprecation API alone. Just because the API returns with no issues, it does not mean that everything in your setup will work out of the box when migrating! This is to be used in addition to looking through the deprecation log and breaking changes documentation.
Examples
- The removal of document types (“_type”) . Various document types were allowed in a single index in version 6, but this functionality has been removed. You will get warnings if you use document types in queries, and only 1 document type is allowed per index in version 7. The functionality is expected to be completely removed in version 8.
- The discovery.zen.minimum_master_nodes setting is permitted, but ignored, on 7.x nodes.
There are many more examples to be found in the breaking changes documentation.
Notes and good things to know
It is important to visit ALL the breaking changes for each minor version between the version you are using and the version you want to upgrade to.
https://www.elastic.co/guide/en/elasticsearch/reference/7.3/breaking-changes-7.3.html
Contains information that is not mentioned on the next page.
https://www.elastic.co/guide/en/elasticsearch/reference/7.5/breaking-changes-7.4.html
The best way is to go to the “breaking changes” page of the version you want to upgrade to, and then use the links to page look through all of the minor version pages down to the one you want to upgrade from, paying particular attention to the major version change (eg. 7.0 )
Opster supports all Elasticsearch versions so If you need help reach out
Log Context
Log “Failed to parse mapping for type {}: {}” classname is ClusterDeprecationChecks.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
String mappingTypeName = mappingCursor.key;
MappingMetaData mappingMetaData = null;
try {
mappingMetaData = new MappingMetaData(mappingCursor.value);
} catch (IOException e) {
logger.error("failed to parse mapping for type {}: {}"; mappingTypeName; e);
}
if (mappingMetaData != null && defaultFieldSet == false) {
maxFields.set(IndexDeprecationChecks.countFieldsRecursively(mappingMetaData.type(); mappingMetaData.sourceAsMap()));
}
if (maxFields.get() > maxClauseCount) {
Find & fix Elasticsearch problems
