Data Science Studio can both read and write datasets on Elasticsearch versions 5.0 to 8.4.

Please note that support for Elasticsearch 1.x and 2.x is now deprecated and will be removed in a future release.

Append Mode (to append to an elasticsearch dataset instead of replacing) is not supported.

Define an Elasticsearch connection

  • Go to Administration > Connections

  • Click the “New connection” button and pick Elasticsearch

  • Enter a name for the new connection, and the required connection parameters, then test and save the new connection


The port parameter should be Elasticsearch’s HTTP API port (9200 by default), not the Java API port.

Managed Elasticsearch datasets

If you allow DSS to write managed dataset into the Elasticsearch connection, you can use this connection to create output datasets for recipes.

Creating such a dataset creates a new index on your Elasticsearch server with the name of the dataset by default. For Elasticsearch 6 and below, a mapping type is also created with the name of the dataset by default. For example, if your Elasticsearch server is hosted on localhost:9200, a managed dataset named Articles stores its data into localhost:9200/articles/articles. For Elasticsearch 7 and above, it will be stored into localhost:9200/articles. This name will not change if you rename the dataset in case you are relying on its presence, so if you rename the dataset and want those names to remain similar, you should edit the index and type names after renaming the dataset, then rebuild it and manually delete the previous index.


For Elasticsearch 6 and below, you should not create other types in the index that are managed by DSS, they might be deleted or altered.

By default, fields get the default Elasticsearch mapping, e.g. string are analyzed and indexed (mapped to text in Elasticsearch 5+). If you want access to a non-analyzed version (mapped to keyword in Elasticsearch 5+) of some or all of your columns, you can list those columns (comma-separated, or * for all string columns) in the dataset settings. You can also specify your own complete mapping.

If your dataset is partitioned, then one index per partition is created (prefixed by the index name) and the index name is actually an Elasticsearch alias that points to all the partition’s indices. You can still search or delete from the alias normally.

If you want the index to have non-default settings, you can use an index template before building the managed dataset for the first time.

External Elasticsearch datasets

You can also import existing data from Elasticsearch into DSS. Simply create an Elasticsearch dataset and specify the index of the data (and the type name for Elasticsearch 6 and below). If the connection is writable, DSS can also overwrite that data, but the type mapping will not be modified by DSS and the index/type will not be created if they don’t already exist.

Your index may be an alias if it’s only used for reading, or for writing if it only points to one index (otherwise Elasticsearch refuses the write operation).


You can partition your external dataset in DSS. The partitioning model can either be field-based or indices-based.


The field-based model is similar to the column-based model and the indices-based model is similar to the files-based model. See Working with partitions for more details.


Simply specify the partitioning column and the type of partitioning (value or time-based). You can only partition on one column for external datasets.


The partitioning column must have fielddata enabled, which is the case by default for keyword fields in Elasticsearch 5+ but not for text.



Support for indices-based partitioning is experimental

Specify a partitioning format that will match all the indices you want to import. This string will overwrite the index name value. For each dimension, the pattern must contain a specifier that will be replaced by a wildcard when matching existing indices. DSS will capture the value from each matching index name to set the value of the dimension corresponding to the specifier. This field supports wildcards (*).


See Partitioning files-based datasets for more details.

Search view

On Elasticsearch datasets, an additionnal “Search” tab is available in the dataset view. From there you can:

  • Write & perform search queries based on the Elasticsearch query_string syntax

  • Use a set of visual filters, range conditions or wildcards, to build advanced queries

  • Persist your query on the current dataset for later reuse

  • Deploy your query to the Flow as a filter recipe (see Sampling datasets)


The search applies to the whole dataset. Sample settings that may be defined on the other tabs do not impact the search results

In the following example, the query string will be replaced and sent to Elastisearch

GET /_search
  "query": {
    "query_string": {
      "query": "${Your search query}",

This fully functional search view can be exposed on a DSS dashboard. See Dataset table tile display for more details.

By default, DSS will retrieve the exact number of hits when executing the search. The query sent to Elasticsearch enables the track_total_hits parameter. This behavior may be disabled by adding the custom property dku.connection.elasticsearch.interactiveSearch.exactTotalHits set to false in the connection settings. The count returned when searching will then be capped depending on the Elasticsearch configuration (10000 hits by default, see index.max_result_window for more details).


  • This feature is only available for Elasticsearch versions with a dialect compatible with 7.x

  • Since results are not batched, DSS uses the from field property to paginate & display results.

  • Thus, this may lead to some duplicate rows if some data have been appended to the Elasticsearch index while scrolling.

  • Highlighting service is handled by Elasticsearch on text fields. So, date or ip type fields are not supported by the highlighter. Also, depending on the tokenization, some parts of a word may not be highlighted. DSS does not apply any further control.