How To Solve Issues Related to Log – Primary-replica resync completed with operations

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Troubleshooting background

To troubleshoot Elasticsearch log “Primary-replica resync completed with operations” it’s important to know common problems related to Elasticsearch concepts: index, shard. See below-detailed explanations complete with common problems, examples and useful tips.

Index in Elasticsearch

What it is

In Elasticsearch, an index (indices in plural) can be thought of as a table inside a database that has 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.

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 below command:

PUT test_index1/_doc/1
  "tags": [
  "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:

To disable this, you can add the following lines in the elasticsearch.yml:

action.destructive_requires_name: true

Shards in Elasticsearch

What it is

Data in an Elasticsearch index can grow to massive proportions. In order to keep it manageable, it is split into a number of shards. Each Elasticsearch shard is an Apache Lucene index, with each individual Lucene index containing a subset of the documents in the Elasticsearch index. Splitting indices in this way keeps resource usage under control. An Apache Lucene index has a limit of 2,147,483,519 documents.


It is when an index is created that the number of shards is set, and this cannot be changed later without reindexing the data. When creating an index, you can set the number of shards and replicas as properties of the index

PUT /sensor
    "settings" : {
        "index" : {
            "number_of_shards" : 6,
            "number_of_replicas" : 2

The ideal number of shards should be determined based on the amount of data in an index. Generally, an optimal shard should hold 30-50GB of data. For example, if you expect to accumulate around 300GB of application logs in a day, having around 10 shards in that index would be reasonable.

During their lifetime, shards can go through a number of states, including:

  • Initializing: An initial state before the shard can be used.
  • Started: A state in which the shard is active and can receive requests.
  • Relocating: A state that occurs when shards are in the process of being moved to a different node. This may be necessary under certain conditions, for example, when the node they are on is running out of disk space.
  • Unassigned: The state of a shard that has failed to be assigned. A reason is provided when this happens, for example, if the node hosting the shard is no longer in the cluster (NODE_LEFT) or due to restoring into a closed index (EXISTING_INDEX_RESTORED).

In order to view all shards, their states, and other metadata, use the following request:

GET _cat/shards

To view shards for a specific index, append the name of the index to the URL, for example

GET _cat/shards/sensor

This command produces output, such as in the following example. By default, the columns shown include the name of the index, the name (i.e. number) of the shard, whether it is a primary shard or a replica, its state, the number of documents, the size on disk, the IP address, and the node ID.

sensor 5 p STARTED    0  283b ziap
sensor 5 r UNASSIGNED                   
sensor 2 p STARTED    1 3.7kb ziap
sensor 2 r UNASSIGNED                   
sensor 3 p STARTED    3 7.2kb ziap
sensor 3 r UNASSIGNED                   
sensor 1 p STARTED    1 3.7kb ziap
sensor 1 r UNASSIGNED                   
sensor 4 p STARTED    2 3.8kb ziap
sensor 4 r UNASSIGNED                   
sensor 0 p STARTED    0  283b ziap
sensor 0 r UNASSIGNED
Notes and good things to know
  • Having shards that are too large is simply inefficient. Moving huge indices across machines is time- and labor-intensive process. First, the Lucene merges would take longer to complete and would require greater resources. Moreover, moving the shards across the nodes for rebalancing would also take longer and recovery time would be extended. Thus by splitting the data and spreading it across a number of machines, it can be kept in manageable chunks and minimize risks.
  • Having the right number of shards is important for performance. It is thus wise to plan in advance. When queries are run across different shards in parallel, they execute faster than an index composed of a single shard, but only if each shard is located on a different node and there are sufficient nodes in the cluster. At the same time, however, shards consume memory and disk space, both in terms of indexed data and cluster metadata. Having too many shards can slow down queries, indexing requests, and management operations, and so maintaining the right balance is critical.

To help troubleshoot related issues we have gathered selected Q&A from the community and issues from Github , please review the following for further information :

Elasticsearch cluster is crashing

Number of Views : 0.12 K  Score on Stackoverflow :

Github Issue Number 34950


Log Context

Log ”Primary-replica resync completed with operations” classname is
We have extracted the following from Elasticsearch source code to get an in-depth context :

<pre class="wp-block-syntaxhighlighter-code">                                 engine.fillSeqNoGaps(newPrimaryTerm);
                                replicationTracker.updateLocalCheckpoint(currentRouting.allocationId().getId(); getLocalCheckpoint());
                                primaryReplicaSyncer.accept(this; new ActionListener<ResyncTask>() {
                                    public void onResponse(ResyncTask resyncTask) {
                              "primary-replica resync completed with {} operations";
                                        boolean resyncCompleted =
                                            primaryReplicaResyncInProgress.compareAndSet(true; false);
                                        assert resyncCompleted : "primary-replica resync finished but was not started";


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