SQL to Elasticsearch DSL: A Guide with Python Examples

Elasticsearch DSL is a Python library for building Elasticsearch queries. It provides a more Pythonic and readable way to interact with Elasticsearch compared to the raw JSON format. This guide demonstrates how to convert common SQL queries into their Elasticsearch DSL equivalents using Python code.

Basic Queries

SELECT * FROM my_index

from elasticsearch_dsl import Search

s = Search().index('my_index')
response = s.execute()

SELECT field1, field2 FROM my_index WHERE field1 = 'value1'

from elasticsearch_dsl import Search, Q

s = Search().index('my_index').source(['field1', 'field2'])
q = Q('match', field1='value1')
s = s.query(q)
response = s.execute()

Filtering

SELECT * FROM my_index WHERE field1 = 'value1' AND field2 = 'value2'

from elasticsearch_dsl import Search, Q

s = Search().index('my_index')
q = Q('bool', must=[Q('match', field1='value1'), Q('match', field2='value2')])
s = s.query(q)
response = s.execute()

Aggregation

SELECT COUNT(*) FROM my_index WHERE field1 = 'value1'

from elasticsearch_dsl import Search, Q

s = Search().index('my_index')
q = Q('match', field1='value1')
s = s.query(q)
response = s.execute()
count = response.hits.total

SELECT field1, AVG(field2) FROM my_index GROUP BY field1

from elasticsearch_dsl import Search, A

s = Search().index('my_index').source(['field1'])
a = A('avg', field='field2')
s.aggs.bucket('by_field1', 'terms', field='field1').metric('avg_field2', a)
response = s.execute()
buckets = response.aggregations.by_field1.buckets
for bucket in buckets:
    field1_value = bucket.key
    avg_field2_value = bucket.avg_field2.value

This guide provides a starting point for using Elasticsearch DSL with Python. You can find more advanced queries and examples in the official Elasticsearch DSL documentation.

SQL to Elasticsearch DSL: A Guide with Python Examples