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Using lakeFS with Presto/Trino

Presto and Trino are a distributed SQL query engine designed to query large data sets distributed over one or more heterogeneous data sources.

Table of contents

  1. Using lakeFS with Presto/Trino
    1. Table of contents
    2. Configuration
      1. Configure Hive connector
      2. Configure Hive
    3. Examples
      1. Example with schema
      2. Example with External table
      3. Example of copying a table with metastore tools:

Querying data in lakeFS from Presto/Trino is the same as querying data in S3 from Presto/Trino. It is done using the Presto Hive connector or Trino Hive connector.

Note In the following examples we set AWS credentials at runtime, for clarity. In production, these properties should be set using one of Hadoop’s standard ways of Authenticating with S3.


Configure Hive connector

Create /etc/catalog/ with the following contents to mount the hive-hadoop2 connector as the hive catalog, replacing with the correct host and port for your Hive metastore Thrift service:

Add to /etc/catalog/ the lakeFS configurations in the corresponding S3 configuration properties:

Configure Hive

Presto/Trino uses Hive metastore service (HMS), or a compatible implementation of the Hive metastore, such as AWS Glue Data Catalog to write data to S3. In case you are using Hive metastore, you will need to configure Hive as well. In file hive-site.xml add to the configuration:



Here are some examples based on examples from the Presto Hive connector examples and Trino Hive connector examples

Example with schema

Create a new schema named main that will store tables in a lakeFS repository named example branch: master:

WITH (location = 's3a://example/main')

Create a new Hive table named page_views in the web schema that is stored using the ORC file format, partitioned by date and country, and bucketed by user into 50 buckets (note that Hive requires the partition columns to be the last columns in the table):

CREATE TABLE main.page_views (
  view_time timestamp,
  user_id bigint,
  page_url varchar,
  ds date,
  country varchar
  format = 'ORC',
  partitioned_by = ARRAY['ds', 'country'],
  bucketed_by = ARRAY['user_id'],
  bucket_count = 50

Example with External table

Create an external Hive table named request_logs that points at existing data in lakeFS:

CREATE TABLE main.request_logs (
  request_time timestamp,
  url varchar,
  ip varchar,
  user_agent varchar
  format = 'TEXTFILE',
  external_location = 's3a://example/main/data/logs/'

Example of copying a table with metastore tools:

Copy the created table page_views on schema main to schema example_branch with location s3a://example/example_branch/page_views/

lakectl metastore copy --from-schema main --from-table page_views --to-branch example_branch