SQL Lab

SQL Lab is a modern, feature-rich SQL IDE written in React.


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Feature Overview

  • Connects to just about any database backend
  • A multi-tab environment to work on multiple queries at a time
  • A smooth flow to visualize your query results using Superset’s rich visualization capabilities
  • Browse database metadata: tables, columns, indexes, partitions
  • Support for long-running queries
    • uses the Celery distributed queue to dispatch query handling to workers
    • supports defining a “results backend” to persist query results
  • A search engine to find queries executed in the past
  • Supports templating using the Jinja templating language which allows for using macros in your SQL code

Extra features

  • Hit alt + enter as a keyboard shortcut to run your query

Templating with Jinja

SELECT *
FROM some_table
WHERE partition_key = '{{ presto.first_latest_partition('some_table') }}'

Templating unleashes the power and capabilities of a programming language within your SQL code.

Templates can also be used to write generic queries that are parameterized so they can be re-used easily.

Available macros

We expose certain modules from Python’s standard library in Superset’s Jinja context:

  • time: time
  • datetime: datetime.datetime
  • uuid1: uuid1
  • uuid3: uuid3
  • uuid4: uuid4
  • uuid5: uuid5
  • random: random
  • relativedelta: dateutil.relativedelta.relativedelta

Jinja’s builtin filters can be also be applied where needed.

Extending macros

As mentioned in the Installation & Configuration documentation, it’s possible for administrators to expose more more macros in their environment using the configuration variable JINJA_CONTEXT_ADDONS. All objects referenced in this dictionary will become available for users to integrate in their queries in SQL Lab.

Customize templating

As mentioned in the Installation & Configuration documentation, it’s possible for administrators to overwrite Jinja templating with your customized template processor using the configuration variable CUSTOM_TEMPLATE_PROCESSORS. The template processors referenced in the dictionary will overwrite default Jinja template processors of the specified database engines.

Query cost estimation

Some databases support EXPLAIN queries that allow users to estimate the cost of queries before executing this. Currently, Presto is supported in SQL Lab. To enable query cost estimation, add the following keys to the “Extra” field in the database configuration:

{
    "version": "0.319",
    "cost_estimate_enabled": true
    ...
}

Here, “version” should be the version of your Presto cluster. Support for this functionality was introduced in Presto 0.319.

You also need to enable the feature flag in your superset_config.py, and you can optionally specify a custom formatter. Eg:

def presto_query_cost_formatter(cost_estimate: List[Dict[str, float]]) -> List[Dict[str, str]]:
    """
    Format cost estimate returned by Presto.

    :param cost_estimate: JSON estimate from Presto
    :return: Human readable cost estimate
    """
    # Convert cost to dollars based on CPU and network cost. These coefficients are just
    # examples, they need to be estimated based on your infrastructure.
    cpu_coefficient = 2e-12
    network_coefficient = 1e-12

    cost = 0
    for row in cost_estimate:
        cost += row.get("cpuCost", 0) * cpu_coefficient
        cost += row.get("networkCost", 0) * network_coefficient

    return [{"Cost": f"US$ {cost:.2f}"}]


DEFAULT_FEATURE_FLAGS = {
    "ESTIMATE_QUERY_COST": True,
    "QUERY_COST_FORMATTERS_BY_ENGINE": {"presto": presto_query_cost_formatter},
}

Create Table As (CTAS)

You can use CREATE TABLE AS SELECT ... statements on SQLLab. This feature can be toggled on and off at the database configuration level.

Note that since CREATE TABLE.. belongs to a SQL DDL category. Specifically on PostgreSQL, DDL is transactional, this means that to properly use this feature you have to set autocommit to true on your engine parameters:

{
    ...
    "engine_params": {"isolation_level":"AUTOCOMMIT"},
    ...
}