Unable to link C library native methods priv set will be disabled – How to solve this Elasticsearch error

Overview

Elasticsearch has many settings that can cause significant performance problems if not set correctly. To prevent this happening, Elasticsearch carries out “bootstrap checks” to ensure that these important settings have been covered. If any of the checks fail, Elasticsearch will write an error to the logs and will not start. In this guide we cover common bootstrap checks you should know and how to configure your settings correctly to pass the checks successfully.

Bootstrap checks are carried out when the network.host setting in:

network.host: 0.0.0.0

If network host is not set and you use the default localhost, then Elasticsearch will consider the node to be in development mode, and bootstrap checks will not be enforced.

Common issues with bootstrap checks

If you install elasticsearch using RPM or Debian packages (strongly recommended) then most of the important configuration is already done for you, and the only bootstrap checks you are likely to run up against are the following.

Heap size check

The minimum and maximum heap sizes specified in jvm.options (or via environment variables) must be equal to one another.

File descriptors check

Minimum file descriptors must have been set to at least 65535

Memory lock check

There are various methods used to prevent memory swapping, and you must use one of them.  The most common is to set in elasticsearch.yml

bootstrap.memory_lock: true

For this to be effective you must give permission to elasticsearch to enable this. There are various ways to do so, depending upon your operating system.

Discovery configuration checks

At least one of the following properties must be configured in elasticsearch.yml to ensure that your node can form a cluster properly:

• discovery.seed_hosts
• discovery.seed_providers
• cluster.initial_master_nodes

Less common bootstrap check issues

If you are not using RPM or debian packages, you may come across the following issues:

Max number of threads check

You must allow your system to create at least 4096 threads. In linux this is done by editing /etc/security/limits.conf and adjusting the nproc setting.

Max file size check

Your system should be able to create unlimited file sizes. In linux this is done by editing /etc/security/limits.conf and adjusting the fsize setting

Max virtual memory check

The system should be able to create unlimited virtual memory for the elasticsearch user.

This is done by editing /etc/security/limits.conf 

<user> - as unlimited

Max map count check

The system must be able to use mmap effectively.  This is done by running the command 

sysctl vm.max_map_count 262144

Other checks

The following checks are also carried out, but are rarely found:

OsX File Descriptor Check
Client Jvm Check
UseS erial GC Check
System Call Filter Check
Might Fork Check
On Error Check
On Out Of Memory Error Check
Early Access Check
G1GC Check
All Permission Check

Overview

When a query is executed, Elasticsearch by default calculates the relevance score of the matching documents. But in some conditions, it does not require scores to be calculated. For instance, if a document falls in the range of two given timestamps or if a document contains a given list of tags. For all these Yes/No criteria, also known as structured search, a filter clause is used.

When it is not desired or not necessary to compute scores, filters should be used instead of queries, as frequently used filters can be cached automatically by Elasticsearch to improve performance.

There are multiple ways to specify filters, such as when using the `filter` and `must_not` parameters of the `bool` query, the `filter` parameter of the `constant_score` query or the `filter` aggregation.

What it is used for

When a query is executed, Elasticsearch by default calculates the relevance score of the matching documents. But in some conditions it does not require scores to be calculated, for instance if a document falls in the range of two given timestamps. For all these Yes/No criteria, a filter clause is used.

Examples

To return all the documents of a given index that fall between a date range, we can use the `range` filter, as shown below:

GET my_index/_search
{
  "query": {
    "bool": {
      "filter": [
        {
          "range": {
            "created_at": {
              "gte": "2020-01-01",
              "lte": "2020-01-10"
            }
          }
        }
      ]
    }
  }
}

To retrieve all the documents that contain at least one tag from a given list, we can use the `terms` filter, as shown below:

GET my_index/_search
{
  "query": {
    "bool": {
      "filter": [
        {
          "terms": {
            "tags": ["tag1", "tag2", "tag3"]
          }
        }
      ]
    }
  }
}

To retrieve all the documents that contain a given field having a non-null value, we can use the `exists` filter, as shown below:

GET my_index/_search
{
  "query": {
    "bool": {
      "filter": [
        {
          "exists": {
            "field": "field_name"
          }
        }
      ]
    }
  }
}

There are many other filters that we can use in order to reduce the document set that needs to be scored, such as `fuzzy`, `prefix`, `wildcard`, `regexp`, `script`, and many more. 

It is also worth noting that filters can be combined since the `bool/filter` and `bool/must_not` parameters are arrays. In the example below, we retrieve all documents falling within a data range, containing a list of tags and not having a specific field:

GET my_index/_search
{
  "query": {
    "bool": {
      "filter": [
        {
          "range": {
            "created_at": {
              "gte": "2020-01-01",
              "lte": "2020-01-10"
            }
          }
        },
        {
          "terms": {
            "tags": ["tag1", "tag2", "tag3"]
          }
        }
      ],
      "must_not": [
        {
          "exists": {
            "field": "field_name"
          }
        }
      ]
    }
  }
}

Notes

• Queries are used to find out how relevant a document is to a particular query by calculating a score for each document, whereas filters are used to match certain criteria and are cacheable to enable faster execution.
• Filters do not contribute to scoring and thus are faster to execute.
• There are major changes introduced in Elasticsearch version 2.x onward related to how query and filters are written and performed internally and each newer version comes with its load of new improvements.

Common problems

• The most common problem with filters is incorrect use inside the query. If filters are not used correctly, query performance can be significantly affected. So filters must be used wherever there is scope of not calculating the score. 
• Another problem often arises when using date range filters, if “now” is used to represent the current time. It has to be noted that “now” is continuously changing the timestamp and thus Elasticsearch cannot use caching of the response since the data set will keep changing.