> ## Documentation Index
> Fetch the complete documentation index at: https://docs.open-metadata.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Run the Elasticsearch Connector Externally

> Use YAML to configure Elasticsearch metadata ingestion and track index schemas, mappings, and lineage paths.

export const CodePanel = ({children, fileName = 'config.yaml', showLineNumbers = false}) => {
  const codePanelRef = useRef(null);
  const codeContentRef = useRef(null);
  const isProgrammaticScroll = useRef(false);
  const hoverTimeout = useRef(null);
  useEffect(() => {
    let tries = 0;
    const wrapLines = () => {
      const root = codeContentRef.current;
      if (!root) return;
      const pres = Array.from(root.querySelectorAll('pre'));
      if (!pres.length) {
        if (tries++ < 20) requestAnimationFrame(wrapLines);
        return;
      }
      let globalLine = 1;
      pres.forEach(pre => {
        const code = pre.querySelector('code') || pre;
        if (!code || code.dataset.wrapped === 'true') return;
        const raw = code.textContent || '';
        let lines = raw.split('\n');
        while (lines[0] === '') lines.shift();
        while (lines[lines.length - 1] === '') lines.pop();
        code.innerHTML = lines.map(line => {
          const ln = globalLine++;
          const num = showLineNumbers ? `<span class="line-number">${ln}</span>` : '';
          const safe = line.replace(/</g, '&lt;').replace(/>/g, '&gt;') || ' ';
          return `<span class="code-line" data-line="${ln}">${num}${safe}</span>`;
        }).join('');
        code.dataset.wrapped = 'true';
      });
    };
    wrapLines();
  }, [children, showLineNumbers]);
  useEffect(() => {
    const panel = codePanelRef.current;
    const content = codeContentRef.current;
    if (!panel || !content) return;
    const waitForLines = () => {
      const codeLines = content.querySelectorAll('.code-line');
      if (!codeLines.length) {
        requestAnimationFrame(waitForLines);
        return;
      }
      setupHighlighting(codeLines);
    };
    const setupHighlighting = codeLines => {
      const layout = panel.closest('.split-layout');
      const sections = layout.querySelectorAll('.content-section');
      const parseLines = str => {
        if (!str) return [];
        const out = [];
        str.split(',').forEach(p => {
          if (p.includes('-')) {
            const [s, e] = p.split('-').map(Number);
            for (let i = s; i <= e; i++) out.push(i);
          } else {
            const n = Number(p);
            if (!isNaN(n)) out.push(n);
          }
        });
        return out;
      };
      const clearHighlight = () => {
        codeLines.forEach(l => l.classList.remove('highlighted'));
      };
      const highlight = lines => {
        clearHighlight();
        lines.forEach(n => {
          const el = content.querySelector(`.code-line[data-line="${n}"]`);
          if (el) el.classList.add('highlighted');
        });
      };
      const scrollToLines = lines => {
        if (!lines.length) return;
        const first = lines[0];
        const targetLine = lines.length > 1 ? first : lines[0];
        const el = content.querySelector(`.code-line[data-line="${targetLine}"]`);
        if (!el) return;
        isProgrammaticScroll.current = true;
        const containerRect = content.getBoundingClientRect();
        const elRect = el.getBoundingClientRect();
        const offset = elRect.top - containerRect.top + content.scrollTop;
        const TOP_PADDING = 16;
        content.scrollTo({
          top: Math.max(offset - TOP_PADDING, 0),
          behavior: 'smooth'
        });
        setTimeout(() => {
          isProgrammaticScroll.current = false;
        }, 200);
      };
      const activate = (section, scroll) => {
        if (section.classList.contains('active')) return;
        sections.forEach(s => s.classList.remove('active'));
        section.classList.add('active');
        const lines = parseLines(section.dataset.lines);
        highlight(lines);
        if (scroll) scrollToLines(lines);
      };
      const observer = new IntersectionObserver(entries => {
        if (isProgrammaticScroll.current) return;
        entries.forEach(e => {
          if (e.isIntersecting) activate(e.target, false);
        });
      }, {
        threshold: 0.3,
        rootMargin: '-80px 0px -40% 0px'
      });
      sections.forEach(section => {
        observer.observe(section);
        section.addEventListener('click', () => activate(section, true));
        section.addEventListener('mouseenter', () => {
          clearTimeout(hoverTimeout.current);
          hoverTimeout.current = setTimeout(() => activate(section, true), 80);
        });
      });
      if (sections[0]) activate(sections[0], false);
    };
    waitForLines();
  }, []);
  const handleCopy = e => {
    const btn = e.currentTarget;
    const codeLines = codeContentRef.current?.querySelectorAll('.code-line');
    if (!codeLines || codeLines.length === 0) return;
    const text = Array.from(codeLines).map(line => {
      const clone = line.cloneNode(true);
      const lineNumber = clone.querySelector('.line-number');
      if (lineNumber) lineNumber.remove();
      return clone.textContent;
    }).join('\n');
    if (!text) return;
    navigator.clipboard.writeText(text).then(() => {
      btn.dataset.copied = 'true';
      setTimeout(() => btn.dataset.copied = 'false', 1500);
    });
  };
  return <div className="code-panel" ref={codePanelRef}>
      <div className="code-header">
        {fileName}
        <button className="copy-btn" aria-label="Copy full code" data-copied="false" onClick={handleCopy}>
          <svg className="icon-copy" viewBox="0 0 15 16" fill="currentColor">
            <path d="M10.113 3.124H2.205C1.463 3.124.86 3.655.86 4.31v10.005c0 .654.603 1.186 1.345 1.186h7.908c.742 0 1.345-.532 1.345-1.186V4.31c0-.655-.606-1.186-1.345-1.186Z" />
            <path d="M13.138.5H5.229c-.742 0-1.344.531-1.344 1.186 0 .23.209.414.47.414s.47-.184.47-.414c0-.197.182-.357.404-.357h7.909c.223 0 .404.16.404.357V11.69c0 .196-.181.356-.404.356-.262 0-.47.184-.47.415 0 .23.208.415.47.415.742 0 1.344-.532 1.344-1.186V1.686C14.482 1.03 13.88.5 13.138.5Z" />
          </svg>

          <svg className="icon-check" viewBox="0 0 20 20" fill="currentColor">
            <path fillRule="evenodd" d="M16.707 5.293a1 1 0 010 1.414l-7.25 7.25a1 1 0 01-1.414 0l-3.25-3.25a1 1 0 011.414-1.414l2.543 2.543 6.543-6.543a1 1 0 011.414 0z" clipRule="evenodd" />
          </svg>
        </button>
      </div>

      <div className="code-content" ref={codeContentRef}>
        {children}
      </div>
    </div>;
};

export const ContentSection = ({id, title, lines, children}) => <div className="content-section" data-content-id={id} data-lines={lines}>
    {title && <h4>{title}</h4>}
    {children}
  </div>;

export const ContentPanel = ({children}) => <div className="content-panel">{children}</div>;

export const CodePreview = ({children}) => {
  const [instanceId] = useState(() => `preview-${Math.random().toString(36).slice(2)}`);
  useEffect(() => {
    const nav = document.querySelector('nav') || document.querySelector('header') || document.querySelector('[class*="nav"]');
    if (nav) {
      document.documentElement.style.setProperty('--navbar-height', `${nav.offsetHeight}px`);
    }
  }, []);
  return <div className="split-layout" data-preview-id={instanceId}>
      {children}
    </div>;
};

export const ConnectorDetailsHeader = ({name, icon, stage, availableFeatures, unavailableFeatures = [], availableFeaturesCollate = []}) => {
  const showSubHeading = availableFeatures?.length > 0 || unavailableFeatures?.length > 0 || availableFeaturesCollate?.length > 0;
  const totalAvailableFeatures = [...availableFeatures || [], ...availableFeaturesCollate || []];
  return <div className="container">
      <div className="Heading">
        <div className="flex items-center gap-3">
          {icon && <div className="IconContainer">
              <img src={icon} alt={name} noZoom className="ConnectorIcon" />
            </div>}
          <h1 className="ConnectorName">{name}</h1>
          <span className={`StageBadge ${stage === 'PROD' ? 'prod' : 'beta'}`}>
            {stage}
          </span>
        </div>
      </div>
      {showSubHeading && <div className="SubHeading">
          <div className="FeaturesHeading">Feature List</div>
          <div className="FeaturesList">
            {totalAvailableFeatures.map(feature => <div className="FeatureTag AvailableFeature" key={feature}>
                ✓ {feature}
              </div>)}
            {unavailableFeatures.map(feature => <div className="FeatureTag UnavailableFeature" key={feature}>
                ✕ {feature}
              </div>)}
          </div>
        </div>}
    </div>;
};

<ConnectorDetailsHeader icon="/public/images/connectors/elasticsearch.webp" name="Elasticsearch" stage="PROD" availableFeatures={["Search Indexes", "Sample Data"]} unavailableFeatures={[]} />

In this section, we provide guides and references to use the Elasticsearch connector.
Configure and schedule Elasticsearch metadata and profiler workflows from the OpenMetadata UI:

* [Requirements](#requirements)
* [Metadata Ingestion](#metadata-ingestion)

## How to Run the Connector Externally

To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with
custom Airflow plugins to handle the workflow deployment.

If, instead, you want to manage your workflows externally on your preferred orchestrator, you can check
the following docs to run the Ingestion Framework **anywhere**.

<Columns cols={2}>
  <Card title="External Schedulers" href="/v1.12.x/deployment/ingestion">
    Get more information about running the Ingestion Framework Externally
  </Card>
</Columns>

## Requirements

We support Elasticsearch 7.0 and above.
We extract Elasticsearch's metadata by using its [API](https://www.elastic.co/guide/en/elasticsearch/reference/current/rest-apis.html). To run this ingestion, you just need a user with permissions to the ElasticSearch instance.

### Python Requirements

<Tip>
  We have support for Python versions **3.9-3.11**
</Tip>

To run the Elasticsearch ingestion, you will need to install:

```bash theme={null}
pip3 install "openmetadata-ingestion[elasticsearch]"
```

## Metadata Ingestion

All connectors are defined as JSON Schemas.
[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/search/elasticSearchConnection.json)
you can find the structure to create a connection to ElasticSearch.
In order to create and run a Metadata Ingestion workflow, we will follow
the steps to create a YAML configuration able to connect to the source,
process the Entities if needed, and reach the OpenMetadata server.
The workflow is modeled around the following
[JSON Schema](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json)

### 1. Define the YAML Config

This is a sample config for Elasticsearch:

<CodePreview>
  <ContentPanel>
    <ContentSection id={1} title="hostPort" lines="7">
      <strong>hostPort</strong>: This parameter specifies the host and port of the Elasticsearch instance. This should be specified as a URI string in the format <code>http\://\<hostname>:\<port></code> or <code>https\://\<hostname>:\<port></code>. For example, you might set it to <code>[https://localhost:9200](https://localhost:9200)</code>.
    </ContentSection>

    <ContentSection id={2} title="Basic Authentication" lines="9-10">
      <strong>Basic Authentication</strong><br />
      <strong>username</strong>: Username to connect to Elasticsearch. Required when Basic Authentication is enabled.<br />
      <strong>password</strong>: Password of the user account to connect with Elasticsearch.
    </ContentSection>

    <ContentSection id={3} title="API Key Authentication" lines="11">
      <strong>API Key Authentication</strong><br />
      <strong>apiKey</strong>: API Key to connect to Elasticsearch. Required when API Key Authentication is enabled.<br />
      <strong>apiKeyId</strong>: Optional field. Enter API Key ID if there is any associated with the API Key. Can be skipped if not applicable.
    </ContentSection>

    <ContentSection id={4} title="sslConfig" lines="13-22">
      <strong>sslConfig</strong>:<br />
      <em>1. SSL Certificates By Path</em><br />

      * <strong>caCertPath</strong>: Path to the CA certificate.<br />
      * <strong>clientCertPath</strong>: Path to the client certificate.<br />
      * <strong>privateKeyPath</strong>: Path to the private key.<br /><br />

      <em>2. SSL Certificates By Value</em><br />

      * <strong>caCertValue</strong>: Value of the CA certificate.<br />
      * <strong>clientCertValue</strong>: Value of the client certificate.<br />
      * <strong>privateKeyValue</strong>: Value of the private key.<br />
      * <strong>stagingDir</strong>: Temporary directory for storing certificate values during ingestion.<br /><br />

      <strong>Note</strong>: When using value-based certificates, make sure to replace new lines with <code>\n</code>. Example:<br />

      <code>
        \-----BEGIN CERTIFICATE-----<br />
        MII..<br />
        MBQ...<br />
        CgU..<br />
        8Lt..<br />
        ...<br />
        h+4=<br />
        \-----END CERTIFICATE-----
      </code>

      <br />

      should be converted to:<br />

      <code>
        \-----BEGIN CERTIFICATE-----\nMII..\nMBQ...\nCgU..\n8Lt..\n...\nh+4=\n-----END CERTIFICATE-----\n
      </code>
    </ContentSection>

    <ContentSection id={5} title="connectionTimeoutSecs" lines="23">
      <strong>connectionTimeoutSecs</strong>: Connection timeout duration (in seconds) for connecting to Elasticsearch APIs.
    </ContentSection>

    <ContentSection id={5} title="SourceConfig" lines="24-38">
      #### Source Configuration - Source Config

      The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/searchServiceMetadataPipeline.json):

      <div>
        **includeSampleData**: Set the Ingest Sample Data toggle to control whether to ingest sample data as part of metadata ingestion.
      </div>

      <div>
        **sampleSize**: If include sample data is enabled, 10 records will be ingested by default. Using this field you can customize the size of sample data.
      </div>

      <div>
        **markDeletedSearchIndexes**: Optional configuration to soft delete `search indexes` in OpenMetadata if the source `search indexes` are deleted. After deleting, all the associated entities like lineage, etc., with that `search index` will be deleted.
      </div>

      <div>
        **searchIndexFilterPattern**: Note that the `searchIndexFilterPattern` support regex to include or exclude search indexes during metadata ingestion process.
      </div>

      <div>
        **includeIndexTemplate**: Set the Include Index Templates toggle to control whether to include index templates as part of metadata ingestion.
      </div>

      <div>
        **overrideMetadata**: Set the Override Metadata toggle to control whether to override the metadata if it already exists.
      </div>
    </ContentSection>

    To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.

    <div>
      The main property here is the `openMetadataServerConfig`, where you can define the host and security provider of your OpenMetadata installation.
    </div>

    <div>
      **Logger Level**

      You can specify the `loggerLevel` depending on your needs. If you are trying to troubleshoot an ingestion, running with `DEBUG` will give you far more traces for identifying issues.
    </div>

    <div>
      **JWT Token**

      JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details [here](/deployment/security/enable-jwt-tokens).

      You can refer to the JWT Troubleshooting section [link](/deployment/security/jwt-troubleshooting) for any issues in your JWT configuration.
    </div>

    <div>
      **Store Service Connection**

      If set to `true` (default), we will store the sensitive information either encrypted via the Fernet Key in the database or externally, if you have configured any [Secrets Manager](/deployment/secrets-manager).

      If set to `false`, the service will be created, but the service connection information will only be used by the Ingestion Framework at runtime, and won't be sent to the OpenMetadata server.
    </div>

    <div>
      **SSL Configuration**

      If you have added SSL to the [OpenMetadata server](/deployment/security/enable-ssl), then you will need to handle the certificates when running the ingestion too. You can either set `verifySSL` to `ignore`, or have it as `validate`, which will require you to set the `sslConfig.caCertificate` with a local path where your ingestion runs that points to the server certificate file.

      Find more information on how to troubleshoot SSL issues [here](/deployment/security/enable-ssl/ssl-troubleshooting).
    </div>

    <div>
      **ingestionPipelineFQN**

      Fully qualified name of ingestion pipeline, used to identify the current ingestion pipeline.
    </div>
  </ContentPanel>

  <CodePanel fileName="connector_config.yaml">
    ```yaml theme={null}
    source:
      type: elasticsearch
      serviceName: elasticsearch_source
      serviceConnection:
        config:
          type: ElasticSearch
    hostPort: http://localhost:9200  # REQUIRED - Elasticsearch cluster URL
    authType:
            username: elastic  # Basic auth username
            password: my_own_password  # Basic auth password
    # apiKeyId: <api key id>
            # apiKey: <api key>
    sslConfig:
            certificates:
              caCertPath: /path/to/http_ca.crt
              clientCertPath: /path/to/http_ca.crt
              privateKeyPath: /path/to/http_ca.crt
              # pass certificate values
              # caCertValue: -----BEGIN CERTIFICATE-----\n....\n.....\n-----END CERTIFICATE-----\n
              # clientCertValue: -----BEGIN CERTIFICATE-----\n....\n...-----END CERTIFICATE-----\n
              # privateKeyValue: -----BEGIN RSA PRIVATE KEY-----\n....\n....\n-----END RSA PRIVATE KEY-----\n
              # stagingDir: /tmp/stage
    ```

    ```yaml theme={null}
          connectionTimeoutSecs: 30
    ```

    ```yaml theme={null}
      sourceConfig:
        config:
          type: SearchMetadata
          # markDeletedSearchIndexes: true
          # includeSampleData: false
          # sampleSize: 10
          # searchIndexFilterPattern:
          #   includes:
          #     - index1
          #     - index2
          #   excludes:
          #     - index4
          #     - index3
          # includeIndexTemplates: false
          # overrideMetadata: false
    ```

    ```yaml theme={null}
    sink:
      type: metadata-rest
      config: {}
    ```

    ```yaml theme={null}
    workflowConfig:
      loggerLevel: INFO  # DEBUG, INFO, WARNING or ERROR
      openMetadataServerConfig:
        hostPort: "http://localhost:8585/api"
        authProvider: openmetadata
        securityConfig:
          jwtToken: "{bot_jwt_token}"
        ## Store the service Connection information
        storeServiceConnection: true  # false
        ## Secrets Manager Configuration
        # secretsManagerProvider: aws, azure or noop
        # secretsManagerLoader: airflow or env
        ## If SSL, fill the following
        # verifySSL: validate  # or ignore
        # sslConfig:
        #   caCertificate: /local/path/to/certificate
    # ingestionPipelineFQN: <service name>.<ingestion name> ## e.g., "my_redshift.metadata"
    ```
  </CodePanel>
</CodePreview>

### 2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

```bash theme={null}
metadata ingest -c <path-to-yaml>
```

Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration,
you will be able to extract metadata from different sources.