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About DataHub Search

Feature Availability
Self-Hosted DataHub
Managed DataHub

The search bar is an important mechanism for discovering data assets in DataHub. From the search bar, you can find Datasets, Columns, Dashboards, Charts, Data Pipelines, and more. Simply type in a term and press 'enter'.

Advanced queries and the filter sidebar helps fine tuning queries. For programmatic users Datahub provides a GraphQL API as well.

Search Setup, Prerequisites, and Permissions​

Search is available for all users. Although Search works out of the box, the more relevant data you ingest, the better the results are.

Searching is as easy as typing in relevant business terms and pressing 'enter' to view matching data assets.

By default, search terms will match against different aspects of a data assets. This includes asset names, descriptions, tags, terms, owners, and even specific attributes like the names of columns in a table.

Filters​

The filters sidebar sits on the left hand side of search results, and lets users find assets by drilling down. You can quickly filter by Data Platform (e.g. Snowflake), Tags, Glossary Terms, Domain, Owners, and more with a single click.

Advanced Filters​

Using the Advanced Filter view, you can apply more complex filters. To get there, click 'Advanced' in the top right of the filter panel:

Adding an Advanced Filter​

Currently, Advanced Filters support filtering by Column Name, Container, Domain, Description (entity or column level), Tag (entity or column level), Glossary Term (entity or column level), Owner, Entity Type, Subtype, Environment and soft-deleted status.

To add a new filter, click the add filter menu, choose a filter type, and then fill in the values you want to filter by.

Matching Any Advanced Filter​

By default, all filters must be matched in order for a result to appear. For example, if you add a tag filter and a platform filter, all results will have the tag and the platform. You can set the results to match any filter instead. Click on all filters and select any filter from the drop-down menu.

Negating An Advanced Filter​

After creating a filter, you can choose whether results should or should not match it. Change this by clicking the operation in the top right of the filter and selecting the negated operation.

Results​

Search results appear ranked by their relevance. In self-hosted DataHub ranking is based on how closely the query matched textual fields of an asset and its metadata. In Managed DataHub, ranking is based on a combination of textual relevance, usage (queries / views), and change frequency.

With better metadata comes better results. Learn more about ingestion technical metadata in the metadata ingestion guide.

Advanced queries​

The search bar supports advanced queries with pattern matching, logical expressions and filtering by specific field matches.

The following are use cases with example search phrases. Additionally, an example link is provided for our demo instance. These examples are non exhaustive and using Datasets as a reference.

If you want to:

  • Exact match on term or phrase

    • "datahub_schema" Sample results
    • datahub_schema Sample results
    • Enclosing one or more terms with double quotes will enforce exact matching on these terms, preventing further tokenization.

  • Exclude terms

    • logging -snowflake Sample results
    • Results can be excluded by term using - to negate the term.

  • Term boolean logic with precedence

    • logging + (-snowflake | os_audit_log) Sample results
    • ( ) can be used to set precedence of boolean term expressions

  • Find a dataset with the word mask in the name:

    • /q name: *mask* Sample results
    • This will return entities with mask in the name. Names tends to be connected by other symbols, hence the wildcard symbols before and after the word.

  • Find a dataset with a property, encoding

    • /q customProperties: encoding* Sample results
    • Dataset Properties are indexed in ElasticSearch the manner of key=value. Hence if you know the precise key-value pair, you can search using "key=value". However, if you only know the key, you can use wildcards to replace the value and that is what is being done here.

  • Find a dataset with a column name, latitude

    • /q fieldPaths: latitude Sample results
    • fieldPaths is the name of the attribute that holds the column name in Datasets.

  • Find a dataset with the term latitude in the field description

    • /q editedFieldDescriptions: latitude OR fieldDescriptions: latitude Sample results
    • Datasets has 2 attributes that contains field description. fieldDescription comes from the SchemaMetadata aspect, while editedFieldDescriptions comes from the EditableSchemaMetadata aspect. EditableSchemaMetadata holds information that comes from UI edits, while SchemaMetadata holds data from ingestion of the dataset.

  • Find a dataset with the term logical in the dataset description

    • /q editedDescription: *logical* OR description: *logical* Sample results
    • Similar to field descriptions, dataset descriptions can be found in 2 aspects, hence the need to search 2 attributes.

  • Find a dataset which resides in one of the browsing folders, for instance, the hive folder

    • /q browsePaths: *hive* Sample results
    • BrowsePath is stored as a complete string, for instance /datasets/prod/hive/SampleKafkaDataset, hence the need for wildcards on both ends of the term to return a result.

  • Find a dataset without the name field

    • /q -_exists_:name Sample results
    • the - is negating the existence of the field name.

Videos​

What can you do with DataHub?

GraphQL​

The same GraphQL API that powers the Search UI can be used for integrations and programmatic use-cases. 

# Example query
{
  searchAcrossEntities(
    input: {types: [], query: "*", start: 0, count: 10, filters: [{field: "fieldTags", value: "urn:li:tag:Dimension"}]}
  ) {
    start
    count
    total
    searchResults {
      entity {
        type
        ... on Dataset {
          urn
          type
          platform {
            name
          }
          name
        }
      }
    }
  }
}

Searching at Scale​

For queries that return more than 10k entities we recommend using the scrollAcrossEntities GraphQL API: 

# Example query
{
  scrollAcrossEntities(input: { types: [DATASET], query: "*", count: 10}) {
    nextScrollId
    count
    searchResults {
      entity {
        type
        ... on Dataset {
          urn
          type
          platform {
            name
          }
          name
        }
      }
    }
  }
}

This will return a response containing a nextScrollId value which must be used in subsequent queries to retrieve more data, i.e:

{
  scrollAcrossEntities(input: 
    { types: [DATASET], query: "*", count: 10,
    scrollId: "eyJzb3J0IjpbMy4wLCJ1cm46bGk6ZGF0YXNldDoodXJuOmxpOmRhdGFQbGF0Zm9ybTpiaWdxdWVyeSxiaWdxdWVyeS1wdWJsaWMtZGF0YS5jb3ZpZDE5X2dlb3RhYl9tb2JpbGl0eV9pbXBhY3QucG9ydF90cmFmZmljLFBST0QpIl0sInBpdElkIjpudWxsLCJleHBpcmF0aW9uVGltZSI6MH0="}
  ) {
    nextScrollId
    count
    searchResults {
      entity {
        type
        ... on Dataset {
          urn
          type
          platform {
            name
          }
          name
        }
      }
    }
  }
}

In order to complete scrolling through all of the results, continue to request data in batches until the nextScrollId returned is null or undefined.

DataHub Blog​

It is possible to completely customize search ranking, filtering, and queries using a search configuration yaml file. This no-code solution provides the ability to extend, or replace, the Elasticsearch-based search functionality. The only limitation is that the information used in the query/ranking/filtering must be present in the entities' document, however this does include customProperties, tags, terms, domain, as well as many additional fields.

Additionally, multiple customizations can be applied to different query strings. A regex is applied to the search query to determine which customized search profile to use. This means a different query/ranking/filtering can be applied to a select all/* query or one that contains an actual query.

Search results (excluding select *) are a balance between relevancy and the scoring function. In general, when trying to improve relevancy, focus on changing the query in the boolQuery section and rely on the functionScore for surfacing the importance in the case of a relevancy tie. Consider the scenario where a dataset named orders exists in multiple places. The relevancy between the dataset with the name orders and the term orders is the same, however one location may be more important and thus the function score preferred.

Note: The customized query is a pass-through to Elasticsearch and must comply with their API, syntax errors are possible. It is recommended to test the customized queries prior to production deployment and knowledge of the Elasticsearch query language is required.

The following environment variables on GMS control whether a search configuration is enabled and the location of the configuration file.

Enable Custom Search:

ELASTICSEARCH_QUERY_CUSTOM_CONFIG_ENABLED=true

Custom Search File Location:

ELASTICSEARCH_QUERY_CUSTOM_CONFIG_FILE=search_config.yml

The location of the configuration file can be on the Java classpath or the local filesystem. A default configuration file is included with the GMS jar with the name search_config.yml.

Search Configuration​

The search configuration yaml contains a simple list of configuration profiles selected using the queryRegex. If a single profile is desired, a catch-all regex of .* can be used.

The list of search configurations can be grouped into 4 general sections.

  1. queryRegex - Responsible for selecting the search customization based on the regex matching the search query string. The first match is applied.
  2. Built-in query booleans - There are 3 built-in queries which can be individually enabled/disabled. These include the simple query string[1], match phrase prefix[2], and exact match[3] queries, enabled with the following booleans respectively [simpleQuery, prefixMatchQuery, exactMatchQuery]
  3. boolQuery - The base Elasticsearch boolean query[4]. If enabled in #2 above, those queries will appear in the should section of the boolean query[4].
  4. functionScore - The Elasticsearch function score[5] section of the overall query.

Examples​

These examples assume a match-all queryRegex of .* so that it would impact any search query for simplicity.

Example 1: Ranking By Tags/Terms​

Boost entities with tags of primary or gold and an example glossary term's uuid.

queryConfigurations:
  - queryRegex: .*
    
    simpleQuery: true
    prefixMatchQuery: true
    exactMatchQuery: true

    functionScore:
      functions:

        - filter:
            terms:
              tags.keyword:
                - urn:li:tag:primary
                - urn:li:tag:gold
          weight: 3.0
        
        - filter:
            terms:
              glossaryTerms.keyword:
                - urn:li:glossaryTerm:9afa9a59-93b2-47cb-9094-aa342eec24ad
          weight: 3.0

      score_mode: multiply
      boost_mode: multiply

Example 2: Preferred Data Platform​

Boost the urn:li:dataPlatform:hive platform.

queryConfigurations:
  - queryRegex: .*
    
    simpleQuery: true
    prefixMatchQuery: true
    exactMatchQuery: true

    functionScore:
      functions:
        - filter:
            terms:
              platform.keyword:
                - urn:li:dataPlatform:hive
          weight: 3.0
      score_mode: multiply
      boost_mode: multiply

Example 3: Exclusion & Bury​

This configuration extends the 3 built-in queries with a rule to exclude deprecated entities from search results because they are not generally relevant as well as reduces the score of materialized.

queryConfigurations:
  - queryRegex: .*
    
    simpleQuery: true
    prefixMatchQuery: true
    exactMatchQuery: true
    
    boolQuery:
      must_not:
        term:
          deprecated:
            value: true

    functionScore:
      functions:
        - filter:
            term:
              materialized:
                value: true
          weight: 0.5
      score_mode: multiply
      boost_mode: multiply

FAQ and Troubleshooting​

How are the results ordered?

The order of the search results is based on the weight what Datahub gives them based on our search algorithm. The current algorithm in OSS DataHub is based on a text-match score from Elasticsearch.

Where to find more information?

The sample queries here are non exhaustive. The link here shows the current list of indexed fields for each entity inside Datahub. Click on the fields inside each entity and see which field has the tag Searchable.
However, it does not tell you the specific attribute name to use for specialized searches. One way to do so is to inspect the ElasticSearch indices, for example:
curl http://localhost:9200/_cat/indices returns all the ES indices in the ElasticSearch container.

yellow open chartindex_v2_1643510690325                           bQO_RSiCSUiKJYsmJClsew 1 1   2 0   8.5kb   8.5kb
yellow open mlmodelgroupindex_v2_1643510678529                    OjIy0wb7RyKqLz3uTENRHQ 1 1   0 0    208b    208b
yellow open dataprocessindex_v2_1643510676831                     2w-IHpuiTUCs6e6gumpYHA 1 1   0 0    208b    208b
yellow open corpgroupindex_v2_1643510673894                       O7myCFlqQWKNtgsldzBS6g 1 1   3 0  16.8kb  16.8kb
yellow open corpuserindex_v2_1643510672335                        0rIe_uIQTjme5Wy61MFbaw 1 1   6 2  32.4kb  32.4kb
yellow open datasetindex_v2_1643510688970                         bjBfUEswSoSqPi3BP4iqjw 1 1  15 0  29.2kb  29.2kb
yellow open dataflowindex_v2_1643510681607                        N8CMlRFvQ42rnYMVDaQJ2g 1 1   1 0  10.2kb  10.2kb
yellow open dataset_datasetusagestatisticsaspect_v1_1643510694706 kdqvqMYLRWq1oZt1pcAsXQ 1 1   4 0   8.9kb   8.9kb
yellow open .ds-datahub_usage_event-000003                        YMVcU8sHTFilUwyI4CWJJg 1 1 186 0 203.9kb 203.9kb
yellow open datajob_datahubingestioncheckpointaspect_v1           nTXJf7C1Q3GoaIJ71gONxw 1 1   0 0    208b    208b
yellow open .ds-datahub_usage_event-000004                        XRFwisRPSJuSr6UVmmsCsg 1 1 196 0 165.5kb 165.5kb
yellow open .ds-datahub_usage_event-000005                        d0O6l5wIRLOyG6iIfAISGw 1 1  77 0 108.1kb 108.1kb
yellow open dataplatformindex_v2_1643510671426                    _4SIIhfAT8yq_WROufunXA 1 1   0 0    208b    208b
yellow open mlmodeldeploymentindex_v2_1643510670629               n81eJIypSp2Qx-fpjZHgRw 1 1   0 0    208b    208b
yellow open .ds-datahub_usage_event-000006                        oyrWKndjQ-a8Rt1IMD9aSA 1 1 143 0 127.1kb 127.1kb
yellow open mlfeaturetableindex_v2_1643510677164                  iEXPt637S1OcilXpxPNYHw 1 1   5 0   8.9kb   8.9kb
yellow open .ds-datahub_usage_event-000001                        S9EnGj64TEW8O3sLUb9I2Q 1 1 257 0 163.9kb 163.9kb
yellow open .ds-datahub_usage_event-000002                        2xJyvKG_RYGwJOG9yq8pJw 1 1  44 0 155.4kb 155.4kb
yellow open dataset_datasetprofileaspect_v1_1643510693373         uahwTHGRRAC7w1c2VqVy8g 1 1  31 0  18.9kb  18.9kb
yellow open mlprimarykeyindex_v2_1643510687579                    MUcmT8ASSASzEpLL98vrWg 1 1   7 0   9.5kb   9.5kb
yellow open glossarytermindex_v2_1643510686127                    cQL8Pg6uQeKfMly9GPhgFQ 1 1   3 0    10kb    10kb
yellow open datajob_datahubingestionrunsummaryaspect_v1           rk22mIsDQ02-52MpWLm1DA 1 1   0 0    208b    208b
yellow open mlmodelindex_v2_1643510675399                         gk-WSTVjRZmkDU5ggeFSqg 1 1   1 0  10.3kb  10.3kb
yellow open dashboardindex_v2_1643510691686                       PQjSaGhTRqWW6zYjcqXo6Q 1 1   1 0   8.7kb   8.7kb
yellow open datahubpolicyindex_v2_1643510671774                   ZyTrYx3-Q1e-7dYq1kn5Gg 1 1   0 0    208b    208b
yellow open datajobindex_v2_1643510682977                         K-rbEyjBS6ew5uOQQS4sPw 1 1   2 0  11.3kb  11.3kb
yellow open datahubretentionindex_v2                              8XrQTPwRTX278mx1SrNwZA 1 1   0 0    208b    208b
yellow open glossarynodeindex_v2_1643510678826                    Y3_bCz0YR2KPwCrrVngDdA 1 1   1 0   7.4kb   7.4kb
yellow open system_metadata_service_v1                            36spEDbDTdKgVlSjE8t-Jw 1 1 387 8  63.2kb  63.2kb
yellow open schemafieldindex_v2_1643510684410                     tZ1gC3haTReRLmpCxirVxQ 1 1   0 0    208b    208b
yellow open mlfeatureindex_v2_1643510680246                       aQO5HF0mT62Znn-oIWBC8A 1 1  20 0  17.4kb  17.4kb
yellow open tagindex_v2_1643510684785                             PfnUdCUORY2fnF3I3W7HwA 1 1   3 1  18.6kb  18.6kb

The index name will vary from instance to instance. Indexed information about Datasets can be found in:
curl http://localhost:9200/datasetindex_v2_1643510688970/_search?=pretty

example information of a dataset:

{
        "_index" : "datasetindex_v2_1643510688970",
        "_type" : "_doc",
        "_id" : "urn%3Ali%3Adataset%3A%28urn%3Ali%3AdataPlatform%3Akafka%2CSampleKafkaDataset%2CPROD%29",
        "_score" : 1.0,
        "_source" : {
          "urn" : "urn:li:dataset:(urn:li:dataPlatform:kafka,SampleKafkaDataset,PROD)",
          "name" : "SampleKafkaDataset",
          "browsePaths" : [
            "/prod/kafka/SampleKafkaDataset"
          ],
          "origin" : "PROD",
          "customProperties" : [
            "prop2=pikachu",
            "prop1=fakeprop"
          ],
          "hasDescription" : false,
          "hasOwners" : true,
          "owners" : [
            "urn:li:corpuser:jdoe",
            "urn:li:corpuser:datahub"
          ],
          "fieldPaths" : [
            "[version=2.0].[type=boolean].field_foo_2",
            "[version=2.0].[type=boolean].field_bar",
            "[version=2.0].[key=True].[type=int].id"
          ],
          "fieldGlossaryTerms" : [ ],
          "fieldDescriptions" : [
            "Foo field description",
            "Bar field description",
            "Id specifying which partition the message should go to"
          ],
          "fieldTags" : [
            "urn:li:tag:NeedsDocumentation"
          ],
          "platform" : "urn:li:dataPlatform:kafka"
        }
      },