How to Ingest Data to Elasticsearch Simplified 101

• May 18th, 2022

Data extraction from data sources has long been an issue for businesses all around the world. Elasticsearch is a popular free and open-source technology for building Ingestion pipelines. It functions as a search and analytics engine that can handle textual, numerical, geographic, structured, and unstructured data.

Ingestion with Elasticsearch Pipelines allows you to change data by applying basic transformations before indexing the document with a searchable reference. After the indexing is complete, you may use the ElasticSearch API to search for and get the document.

In this article, you will learn how to effectively ingest data to Elasticsearch. It covers different methods to ingest data to Elasticsearch.

Table of Contents

What is Elasticsearch?

Ingest Data to Elasticsearch: Elasticsearch logo
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Elasticsearch (also known as Elastic) is a distributed modern search and analytics engine that can work with textual, numerical, geographical, structured, and unstructured data. It was first released in 2010. The ELK Stack is incomplete without ElasticSearch (Elasticsearch, Logstash, and Kibana). Raw data from logs, system metrics, and web applications are parsed, normalized, and enriched before being indexed in Elasticsearch via the Data Ingestion process in Logstash. You can also use an API to transmit data to ElasticSearch in the form of JSON documents. After you’ve finished indexing your data, you may query it and utilize aggregations to create complicated summaries. Finally, you can generate stunning visualizations using Kibana.

Elasticsearch provides a number of Open-source features that are free to use under the SSPL or Elastic License. Paid subscriptions are also available for those who want access to more advanced services. Elasticsearch is widely used for application, website, and enterprise search, as well as log analytics, geospatial data analysis, security analytics, and business analytics.

Key Features of Elasticsearch

  • High Performance: Elasticsearch is a distributed search engine. Elasticsearch documents are organized into shards, which are containers. Shards are replicated to ensure that in the case of a hardware failure, additional copies of the data are available. The distributed nature of Elasticsearch enables you to scale to hundreds (or thousands) of servers to analyze massive volumes of data in parallel and find the best matches for your query quickly.
  • Operation in Near-Real-Time: ElasticSearch is appropriate for time-sensitive scenarios like application monitoring, anomaly detection, security analysis, and infrastructure monitoring since it has a quick wait time of roughly 1 second for reading and writing jobs.
  • Tools and Plugins: Elasticsearch is integrated with Kibana, a prominent visualization, and reporting tool, and is part of the ELK stack. Kibana provides a user interface for real-time display of your ElasticSearch data, allowing you easy access to APM, logs, and infrastructure metric data. It also integrates with Beats and Logstash, making it simple to alter data before putting it into Elasticsearch clusters. Several open-source Elasticsearch plugins, such as language analyzers and suggestion engines, can potentially offer significant functionality to your application.
  • Feature-Rich: Many powerful built-in capabilities, such as data rollup and index lifecycle management, make storing and retrieving data extremely efficient in Elasticsearch.
  • Easy to use: Elasticsearch employs schema-free JSON documents and has a simple REST-based API and HTTP interface, making it simple to start and build applications for many use cases. It supports a wide number of programming languages, including Java, Python, PHP, JavaScript, Node.js, and Ruby, making application creation easier.

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Ingest Data to Elasticsearch Methods 

1) Ingest Data to Elasticsearch: Logstash

Logstash is a strong and versatile data reading, processing, and shipping tool which can be used to ingest data to Elasticsearch. Logstash has a variety of features that aren’t available or are too expensive to use with Beats, such as document enrichment through lookups against other data sources. However, Logstash’s capabilities and adaptability come at a cost. Logstash also has substantially greater hardware needs than Beats. As a result, Logstash should not be used on devices with limited resources. In the event that the functionality of Beats is insufficient for a certain use case, Logstash is utilized as a replacement.

Combining Beats and Logstash is a typical architectural pattern: use Beats to gather data and Logstash to perform any data processing that Beats cannot handle.

Logstash Important Terms

  • Data sources are used as inputs. Files like http, imap, jdbc, kafka, syslog, tcp, and udp are all officially supported data sources.
  • Filters use a variety of methods to process and enrich data. Unstructured log lines must often be processed into a more structured format before being used. As a result, Logstash provides regular expression-based filters for parsing CSV, JSON, key/value pairs, delimited unstructured data, and complicated unstructured data (grok filters). Filters in Logstash can also be used to enhance data by doing DNS lookups, adding geoinformation to IP addresses, or looking up information in a custom dictionary or Elasticsearch index. Additional filters enable a variety of data changes, such as renaming, removing, and copying data fields and values (mutate filter).
  • The final stage of the Logstash processing pipeline, outputs, and writes the parsed and enriched data to data sinks. While there are other output plugins available, you will concentrate on using the elasticsearch output to ingestion into Elasticsearch Service.

Sample Logstash Use Case

  • Step 1: Logstash can be installed using your package manager or by downloading the tgz/zip file and unzipping it.
  • Step 2: Install the RSS input plugin for Logstash, which allows you to read RSS data sources: install logstash-input-rss./bin/logstash-plugin.
  • Step 3: /elastic-rss.conf is used to copy the following Logstash pipeline definition to a new file:
input { 
  rss { 
    url => "/blog/feed" 
    interval => 120 
  } 
} 
filter { 
  mutate { 
    rename => [ "message", "blog_html" ] 
    copy => { "blog_html" => "blog_text" } 
    copy => { "published" => "@timestamp" } 
  } 
  mutate { 
    gsub => [  
      "blog_text", "<.*?>", "", 
      "blog_text", "[nt]", " " 
    ] 
    remove_field => [ "published", "author" ] 
  } 
} 
output { 
  stdout { 
    codec => dots 
  } 
  elasticsearch { 
    hosts => [ "https://<your-elsaticsearch-url>" ] 
    index => "elastic_blog" 
    user => "elastic" 
    password => "<your-elasticsearch-password>" 
  } 
}
  • Step 4: Modify the hosts and password parameters in the preceding file to match your Elasticsearch Service endpoint and elastic user password. The Elasticsearch endpoint URL can be found in the details of your deployment page in Elastic Cloud (Copy Endpoint URL).
Ingest Data to Elasticsearch: Logstash Step 4
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  • Step 5: Start Logstash and run the pipeline: ./bin/logstash -f /elastic-rss.conf. It will take several seconds for Logstash to start up. You’ll start seeing dots (…..) written on the console. Each dot indicates an Elasticsearch document that has been consumed.
  • Step 6: Start Kibana. To validate that 20 documents have been ingested, run the following commands in the Kibana Dev Tools Console: elastic_blog/_search POST.

2) Ingest Data to Elasticsearch: Language Clients

In some cases, integrating data intake with your bespoke application code is desirable. It is recommended to utilize one of the officially supported Elasticsearch clients to ingest data to Elasticsearch. These clients are libraries that abstract away the low-level intricacies of data ingestion so you can concentrate on the important work that your application requires. Java, JavaScript, Go,.NET, PHP, Perl, Python, and Ruby all have official clients. All details and code examples can be found in the documentation for your chosen language. If your application is built in a language that isn’t listed above, there’s a good possibility that a community-contributed client already exists.

3) Ingest Data to Elasticsearch: Kibana Dev Tools

The Kibana Dev Tools Console is a preferred tool for building and debugging Elasticsearch requests as well as to ingest data to Elasticsearch. Dev Tools exposes the entire power and flexibility of the generic Elasticsearch REST API while abstracting the underlying HTTP requests’ technicality. Unsurprisingly, you can PUT raw JSON objects into Elasticsearch using the Dev Tools Console: 

PUT my_first_index/_doc/1 
{ 
    "title" : "How to Ingest Into Elasticsearch Service", 
    "date" : "2019-08-15T14:12:12", 
    "description" : "This is an overview article about the various ways to ingest into Elasticsearch Service" 
}

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4) Ingest Data to Elasticsearch: Elastic Beats

Elastic Beats is a collection of lightweight data shippers for sending data to Elasticsearch Service. It is one of the efficient tools to ingest data to Elasticsearch. Beats have a low runtime overhead, allowing them to run and gather data on devices with minimal hardware resources, such as IoT devices, edge devices, or embedded devices. If you need to gather data but don’t have the capacity to run resource-intensive data collectors, Beats are the way to go. This type of ubiquitous data collecting across all of your networked devices enables you to swiftly discover and respond to system-wide faults and security incidents, for example.

Beats aren’t just for resource-constrained systems, though. They can also be employed on computers with more available hardware resources.

Data Types of Elastic Beats

  • Filebeat lets you read, preprocess, and ship data from sources that are stored as files. Despite the fact that the majority of users use Filebeat to read log files, any nonbinary file format is supported. TCP/UDP, containers, Redis, and Syslog are among the numerous data streams supported by Filebeat. A plethora of modules makes it easier to gather and parse log formats for popular applications like Apache, MySQL, and Kafka.
  • System and service metrics are collected and preprocessed by Metricbeat. System metrics contain CPU, memory, disc, and network utilization numbers, as well as information about ongoing programs. Data can be collected from a variety of services, including Kafka, Palo Alto Networks, Redis, and many others.
  • Packetbeat captures and preprocesses real networking data, allowing for application monitoring, security, and network performance analysis. Packetbeat supports protocols such as DHCP, DNS, HTTP, MongoDB, NFS, and TLS, among others.
  • Winlogbeat is a tool for gathering event logs from Windows operating systems, such as application, hardware, security, and system events. For many use cases, the extensive information available from the Windows event log is of great interest.
  • Auditbeat monitors significant file modifications and gathers events from the Linux Audit Framework. Different modules make it easier to implement, and it’s typically utilized in security analytics scenarios.
  • Heartbeat employs probing to keep track of system and service availability. As a result, heartbeat is valuable in a variety of situations, including infrastructure monitoring and security analytics. The protocols ICMP, TCP, and HTTP are all supported.
  • Functionbeat collects logs and data from AWS Lambda and other serverless environments.

Steps to use Elastic Beats to ingest data to Elasticsearch

  • Step 1: Download and install the Beat of your choice. Most users prefer to either utilize the Elastic-supplied repositories for the operating system’s package manager (DEB/RPM) or just download and unzip the given tgz/zip bundles to install Beats.
  • Step 2: Configure the Beat and turn on any modules you like. 
    • For example, If you installed via the package manager enable the Docker module using sudo metricbeat modules enable docker to collect metrics about Docker containers running on your system. Use /metricbeat modules enable docker instead of unzipping the tgz/zip bundle.
    • The Elasticsearch Service to which the collected data is transmitted is specified using the Cloud ID. In the Metricbeat configuration file (metricbeat.yml), add the Cloud ID and authentication information:
cloud.id: cluster_name:ZXVy...Q2Zg==
cloud.auth: "elastic:YOUR_PASSWORD"
  • Cloud.id and Cloud.auth are the credentials:
    • As previously stated, cloud.id was issued to you when your cluster was created. A login and password that have been granted adequate privileges within the Elasticsearch cluster are concatenated with cloud.auth.
    • Use the elastic superuser and the password provided during cluster formation to get started quickly. If you used the package manager to install, the configuration file is in the /etc/metricbeat directory; if you used the tgz/zip bundle, it is in the unzipped directory.
  • Step 3: Pre-made dashboards can be loaded into Kibana. Most Beats and modules provide pre-built Kibana dashboards. If you used the package manager, load them into Kibana with sudo metricbeat setup; if you used the tgz/zip bundle, load them with ./metricbeat setup in the unzipped directory.
  • Step 4: Follow the beat. If you installed using your package management on a systemd-based Linux system, use sudo systemctl start metricbeat; if you installed using the tgz/zip bundle, use./metricbeat -e.

If everything goes well, data will begin to ingest data to Elasticsearch Service.

What are Ingest Pipelines?

Before indexing (adding a searchable reference to the document), the ingestion pipeline allows you to conduct typical modifications on your data. You can use an Elasticsearch pipeline to drop fields, get values from text, and enrich your data, for example. Processors are a set of configurable jobs in the Elasticsearch Ingest pipeline. Each processor operates in turn, altering the input document in some way. Elasticsearch stores the transformed document in the data stream or index after the processor completes. To develop and manage Elasticsearch Ingest pipelines, use Kibana’s Ingest Pipelines functionality or the Ingest API. The pipeline is saved in a cluster state by Elasticsearch.

What are Ingest Pipelines used for?

Do you want to make some changes to your data but don’t want to utilize Logstash or another data analysis tool? Elasticsearch Ingest Pipelines may be a viable option for you. These Elasticsearch Ingest Pipelines let you customize your data to your specific requirements with minimal effort. The Elasticsearch Ingest pipeline runs on the Elasticsearch node (or the ingestion node, if one is specified) and performs a sequence of operations on the defined data.

Consider the following data structure to better understand how Elasticsearch Ingest Pipelines work:

{
  "id": 5,
  "first_name": "Karl",
  "last_name": "Max",
  "email": "kmax@sample.com",
  "ip_address": "100.1.193.2",
  "activities": "Cricket, Dancing, Reading"
}, 
{
  "id": 6,
  "first_name": "Jim",
  "last_name": "Sanders",
  "email": "jsanders@example.com",
  "ip_address": "122.100.4.22",
  "activities": "Driving, Running, Cycling"
}

Using the above two data structures, you can do the following operations with the Elasticsearch Ingest Pipelines:

  • Rename Fields: the name of the field “first name” to “firstName.”
  • Remove Fields: The field ’email’ has been removed.
  • Split Fields: Instead of using a string, you can use a separator to turn a value into an array. For example, instead of “Cricket, Dancing, Reading,” change “activities” to [“Cricket,” “Dancing,” “Reading”]
  • Lookup a field’s GeoIP.
  • For extra flexibility, run a script: Encoding sensitive data or merging two fields and separating into another.
  • Convert Fields: Changing the type of a field from string to integer.
  • Enhance documents: Use a lookup to add further information to each event, such as “More information underneath.”

When importing data in its raw form, you can also use the Elasticsearch Ingest Pipelines, as demonstrated below:

2022-04-20T18:17:11.003Z new.sample.com There seems to be a problem

2021-05-11T18:17:11.003Z,new.sample.com, There seems to be a problem

The following operations can be used to alter this data:

  • Using grok or dissect to separate fields: “2022-04-20…” in the “date” column; “new.sample.com” in the “origin” field; and “There appears to be an issue” in the “raw message” field.
  • Creating fields from a csv: Use a comma as a separator in the second sample, and label the first value “date,” the second “origin,” and the third “raw message.”

Ingest Data to Elasticsearch: Best Practices 

The following are the best practices to be carried out to ingest data to Elasticsearch:

  • Create a data architecture that meets your requirements. The default hot tier Elasticsearch nodes in your Elasticsearch Service deployment hold your most frequently requested data. You can add warm, cold, and frozen data levels, as well as automated data deletion, based on your own access and retention requirements.
  • Take regular backup snapshots and make your data highly available for production environments or other key data repositories.
  • Existing data can be migrated and uploaded into your deployment.
  • For new data sources, add incoming integrations, you have the option of using Elastic’s integrations or creating your own:
    • Check out the Elastic integrations page to see what Elastic has to offer.
    • Check out these tutorials to integrate with Cloud Service Provider log and metric services: AWS, GCP, and Azure.
    • Choose an ingestion method to write your own.
  • By using the Elastic Common Schema, you may better analyze, visualize, and correlate your events (ECS). Elastic integrations come pre-configured with ECS. ECS is recommended if you’re developing your own integrations.
  • You may control the index lifetime once your data layers are deployed and data is flowing.

Handling Ingest Pipeline Failures

You can use the following options to efficiently handle ElasticSearch Ingest Pipeline faults or failures:

  • The processor follows a set of instructions and will stop if an error occurs. If you set ignore_failure to true, the processor failure will be ignored and the pipeline’s remaining processors will be executed.
PUT _ingest/pipeline/sample-pipeline
{
  "processors": [
    {
      "rename": {
        "description": "Rename 'provider' to 'cloud.provider'",
        "field": "provider",
        "target_field": "cloud.provider",
        "ignore_failure": true
      }
    }
  ]
}
  • The on_failure argument defines a list of processors that will be performed as soon as one of them fails. Even if the on failure configuration is empty, Elasticsearch will run the rest of the processors in the ElasticSearch Ingest pipeline if you have defined the on failure.
PUT _ingest/pipeline/sample-pipeline
{
  "processors": [
    {
      "rename": {
        "description": "Rename 'provider' to 'cloud.provider'",
        "field": "provider",
        "target_field": "cloud.provider",
        "on_failure": [
          {
            "set": {
              "description": "Set 'error.message'",
              "field": "error.message",
              "value": "Field 'provider' does not exist. Cannot rename to 'cloud.provider'",
              "override": false
            }
          }
        ]
      }
    }
  ]
}
  • You can use nest a list of on_failure processors to enable nested error handling.
PUT _ingest/pipeline/sample-pipeline
{
  "processors": [
    {
      "rename": {
        "description": "Rename 'provider' to 'cloud.provider'",
        "field": "provider",
        "target_field": "cloud.provider",
        "on_failure": [
          {
            "set": {
              "description": "Set 'error.message'",
              "field": "error.message",
              "value": "Field 'provider' does not exist. Cannot rename to 'cloud.provider'",
              "override": false,
              "on_failure": [
                {
                  "set": {
                    "description": "Set 'error.message.multi'",
                    "field": "error.message.multi",
                    "value": "Document encountered multiple ingest errors",
                    "override": true
                  }
                }
              ]
            }
          }
        ]
      }
    }
  ]
}
  • You can also specify on_failure in the pipeline, in addition to the processor level. Elasticsearch substitutes this pipeline level parameter for the on failure value if the processor fails without it. Elasticsearch will no longer attempt to execute the remaining processors in the ElasticSearch Ingest pipeline.
PUT _ingest/pipeline/my-pipeline
{
  "processors": [ ... ],
  "on_failure": [
    {
      "set": {
        "description": "Index document to 'failed-<index>'",
        "field": "_index",
        "value": "failed-{{{ _index }}}"
      }
    }
  ]
}
  • The document metadata columns on_failure_message, on_failure_processor_type, on_failure_processor_tag, and on_failure_pipeline can be used to retrieve specific information about the ElasticSearch Ingest pipeline failure. These fields are only accessible from within an on_failure block. For example, the code below includes information about ElasticSearch Ingest pipeline errors in documents using metadata fields.
PUT _ingest/pipeline/sample-pipeline
{
  "processors": [ ... ],
  "on_failure": [
    {
      "set": {
        "description": "Record error information",
        "field": "error_information",
        "value": "Processor {{ _ingest.on_failure_processor_type }} with tag {{ _ingest.on_failure_processor_tag }} in pipeline {{ _ingest.on_failure_pipeline }} failed with message {{ _ingest.on_failure_message }}"
      }
    }
  ]
}

Conclusion

In this article, you learned how to ingest data to Elasticsearch. You can either use Kibana, a sophisticated Data Visualization tool, to establish a pipeline using its user-friendly interface, or send a create pipeline API call for a more technical approach. After you’ve configured your ElasticSearch Ingest Pipeline, you can test it by running it with an example document. You can apply conditions to both processors and ElasticSearch Ingest pipelines using the if statement. To efficiently manage processor faults, you can use parameters like on_failure and ignore_failure.

It’s critical to combine data collected and managed across multiple applications and databases in your business for a comprehensive performance review. Continuously monitoring the Data Connectors, on the other hand, is a time-consuming and resource-intensive task. To do so effectively, set aside some of your technical bandwidth to integrate data from all sources, clean and transform it, and then load it into a Cloud Data Warehouse, BI Tool, or other destination for further Business Analytics. A Cloud-based ETL tool, such as Hevo Data, can easily solve all of these problems.

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