ElasticSearch Connector for Python Read / write ElasticSearch data inside your app; perform many ElasticSearch operations without coding, just use easy to use high performance API Connector for ElasticSearch In this article you will learn how to quickly and efficiently integrate ElasticSearch data in Python. We will use high-performance ElasticSearch Connector to easily connect to ElasticSearch and then access the data inside Python. Let's follow the steps below to see how we can accomplish that! Download Documentation ElasticSearch Connector for Python is based on ZappySys API Driver which is part of ODBC PowerPack. It is a collection of high-performance ODBC drivers that enable you to integrate data in SQL Server, SSIS, a programming language, or any other ODBC-compatible application. ODBC PowerPack supports various file formats, sources and destinations, including REST/SOAP API, SFTP/FTP, storage services, and plain files, to mention a few.

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Create ODBC Data Source (DSN) based on ZappySys API Driver

Step-by-step instructions

To get data from ElasticSearch using Python we first need to create a DSN (Data Source) which will access data from ElasticSearch. We will later be able to read data using Python. Perform these steps:

1. Download and install ODBC PowerPack.

2. Open ODBC Data Sources (x64):

   

3. Create a User data source (User DSN) based on ZappySys API Driver

   ZappySys API Driver

   
   • Create and use User DSN if the client application is run under a User Account. This is an ideal option in design-time, when developing a solution, e.g. in Visual Studio 2019. Use it for both type of applications - 64-bit and 32-bit.
   • Create and use System DSN if the client application is launched under a System Account, e.g. as a Windows Service. Usually, this is an ideal option to use in a production environment. Use ODBC Data Source Administrator (32-bit), instead of 64-bit version, if Windows Service is a 32-bit application.

4. When the Configuration window appears give your data source a name if you haven't done that already, then select "ElasticSearch" from the list of Popular Connectors. If "ElasticSearch" is not present in the list, then click "Search Online" and download it. Then set the path to the location where you downloaded it. Finally, click Continue >> to proceed with configuring the DSN:

   ElasticsearchDSN

   ElasticSearch

   

5. Now it's time to configure the Connection Manager. Select Authentication Type, e.g. Token Authentication. Then select API Base URL (in most cases, the default one is the right one). More info is available in the Authentication section.

    Basic Authentication (UserId/Password) [Http]

   Steps how to get and use ElasticSearch credentials
   

   For Local / Hosted Instance by you

   1. Get your userid / password and enter on the connection UI

   For Managed Instance (By Bonsai search)

   If your instance is hosted by bonsai then perform these steps to get your credentials for API call

   1. Go to https://app.bonsai.io/clusters/{your-instance-id}/tokens
   2. Copy Access Key and Access Secret and enter on the connection UI. Click Test connection.
   3. If your Cluster has no data you can generate sample data by visiting this URL and click Add Sample Data https://{your-cluster-id}.apps.bonsaisearch.net/app/home#/tutorial_directory

   Fill in all required parameters and set optional parameters if needed:

   ElasticsearchDSN

   ElasticSearch

   Basic Authentication (UserId/Password) [Http]

   http://localhost:9200

   Optional Parameters
   User Name (or Access Key)
   Password (or Access Secret)
   Ignore certificate related errors

   

    Windows Authentication (No Password) [Http]

   Fill in all required parameters and set optional parameters if needed:

   ElasticsearchDSN

   ElasticSearch

   Windows Authentication (No Password) [Http]

   http://localhost:9200

   Optional Parameters
   Ignore certificate related errors

   

6. Once the data source connection has been configured, it's time to configure the SQL query. Select the Preview tab and then click Query Builder button to configure the SQL query:

   

   ZappySys API Driver - ElasticSearch

   Read / write ElasticSearch data inside your app; perform many ElasticSearch operations without coding, just use easy to use high performance API Connector for ElasticSearch

   ElasticsearchDSN

   

7. Start by selecting the Table or Endpoint you are interested in and then configure the parameters. This will generate a query that we will use in Python to retrieve data from ElasticSearch. Hit OK button to use this query in the next step.

   SELECT * FROM Indexes

   
   Some parameters configured in this window will be passed to the ElasticSearch API, e.g. filtering parameters. It means that filtering will be done on the server side (instead of the client side), enabling you to get only the meaningful data much faster.

8. Now hit Preview Data button to preview the data using the generated SQL query. If you are satisfied with the result, use this query in Python:

   

   ZappySys API Driver - ElasticSearch

   Read / write ElasticSearch data inside your app; perform many ElasticSearch operations without coding, just use easy to use high performance API Connector for ElasticSearch

   ElasticsearchDSN

   SELECT * FROM Indexes

   
   You can also access data quickly from the tables dropdown by selecting <Select table>.
   A WHERE clause, LIMIT keyword will be performed on the client side, meaning that the whole result set will be retrieved from the ElasticSearch API first, and only then the filtering will be applied to the data. If possible, it is recommended to use parameters in Query Builder to filter the data on the server side (in ElasticSearch servers).

9. Click OK to finish creating the data source.

Video Tutorial

Read data in Python

Using ODBC DSN

1. Python code to get the data:
   

   ElasticsearchDSN')

   

2. When you run the code it will make the API call and read the data:
   

3. Here is Python program's code in text format:

   
       import pyodbc
       conn = pyodbc.connect('DSN=ElasticsearchDSN')
   
       cursor = conn.cursor()
   
       #execute query to fetch data from API service
       cursor.execute("SELECT id,title FROM products")
   
       row = cursor.fetchone()
       while row:
           print(row)
           row = cursor.fetchone()
   
       ##For loop example
       #for row in cursor:
       #    print(row)
   

Using a full ODBC connection string

If you want to avoid being dependent on a DSN and creating multiple DSNs for each platform (x86, x64), then you can use a fully qualified connection string. Simply go to your DSN and copy the Connection String:

1. Open ODBC data source configuration and click Copy settings:
   

   ZappySys API Driver - ElasticSearch

   Read / write ElasticSearch data inside your app; perform many ElasticSearch operations without coding, just use easy to use high performance API Connector for ElasticSearch

   ElasticsearchDSN

   
2. The window opens, telling us the connection string was successfully copied to the clipboard:

3. Then in your Python code use Connection String when initializing OdbcConnection object, for example:

   conn = pyodbc.connect('DRIVER={ZappySys API Driver};ServiceUrl=https://yourservices.provider.com/api/xxxx....;AuthName=Http;')

How to install `pyodbc` in the Python?

Advanced topics

Create Custom Stored Procedure in ZappySys Driver

You can create procedures to encapsulate custom logic and then only pass handful parameters rather than long SQL to execute your API call.

Steps to create Custom Stored Procedure in ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here

1. Go to Custom Objects Tab and Click on Add button and Select Add Procedure:
   

2. Enter the desired Procedure name and click on OK:
   

3. Select the created Stored Procedure and write the your desired stored procedure and Save it and it will create the custom stored procedure in the ZappySys Driver:
   Here is an example stored procedure for ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here
   

   CREATE PROCEDURE [usp_get_orders]
       @fromdate = '<<yyyy-MM-dd,FUN_TODAY>>'
    AS
       SELECT * FROM Orders where OrderDate >= '<@fromdate>';
   

   
   

4. That's it now go to Preview Tab and Execute your Stored Procedure using Exec Command. In this example it will extract the orders from the date 1996-01-01:
   

   Exec usp_get_orders '1996-01-01';

   
   

5. Let's generate the SQL Server Query Code to make the API call using stored procedure. Go to Code Generator Tab, select language as SQL Server and click on Generate button the generate the code.
   As we already created the linked server for this Data Source, in that you just need to copy the Select Query and need to use the linked server name which we have apply on the place of [MY_API_SERVICE] placeholder.
   

   SELECT * FROM OPENQUERY([LS_TO_ELASTICSEARCH_IN_GATEWAY], 'EXEC usp_get_orders @fromdate=''1996-07-30''')

   
   

6. Now go to SQL served and execute that query and it will make the API call using stored procedure and provide you the response.
   

Create Custom Virtual Table in ZappySys Driver

ZappySys API Drivers support flexible Query language so you can override Default Properties you configured on Data Source such as URL, Body. This way you don't have to create multiple Data Sources if you like to read data from multiple EndPoints. However not every application support supplying custom SQL to driver so you can only select Table from list returned from driver.

If you're dealing with Microsoft Access and need to import data from an SQL query, it's important to note that Access doesn't allow direct import of SQL queries. Instead, you can create custom objects (Virtual Tables) to handle the import process.

Many applications like MS Access, Informatica Designer wont give you option to specify custom SQL when you import Objects. In such case Virtual Table is very useful. You can create many Virtual Tables on the same Data Source (e.g. If you have 50 URLs with slight variations you can create virtual tables with just URL as Parameter setting.

1. Go to Custom Objects Tab and Click on Add button and Select Add Table:
   

2. Enter the desired Table name and click on OK:
   

3. And it will open the New Query Window Click on Cancel to close that window and go to Custom Objects Tab.

4. Select the created table, Select Text Type AS SQL and write the your desired SQL Query and Save it and it will create the custom table in the ZappySys Driver:
   Here is an example SQL query for ZappySys Driver. You can insert Placeholders also. Read more about placeholders here
   

   SELECT
     "ShipCountry",
     "OrderID",
     "CustomerID",
     "EmployeeID",
     "OrderDate",
     "RequiredDate",
     "ShippedDate",
     "ShipVia",
     "Freight",
     "ShipName",
     "ShipAddress",
     "ShipCity",
     "ShipRegion",
     "ShipPostalCode"
   FROM "Orders"
   Where "ShipCountry"='USA'

   
   

5. That's it now go to Preview Tab and Execute your custom virtual table query. In this example it will extract the orders for the USA Shipping Country only:
   

   SELECT * FROM "vt__usa_orders_only"

   
   

6. Let's generate the SQL Server Query Code to make the API call using stored procedure. Go to Code Generator Tab, select language as SQL Server and click on Generate button the generate the code.
   As we already created the linked server for this Data Source, in that you just need to copy the Select Query and need to use the linked server name which we have apply on the place of [MY_API_SERVICE] placeholder.
   

   SELECT * FROM OPENQUERY([LS_TO_ELASTICSEARCH_IN_GATEWAY], 'EXEC [usp_get_orders] ''1996-01-01''')

   
   

7. Now go to SQL served and execute that query and it will make the API call using stored procedure and provide you the response.
   

Actions supported by ElasticSearch Connector

ElasticSearch Connector Examples for Python Connection

This page offers a collection of SQL examples designed for seamless integration with the ZappySys API ODBC Driver under ODBC Data Source (36/64) or ZappySys Data Gateway, enhancing your ability to connect and interact with Prebuilt Connectors effectively.

SELECT * FROM create_index WITH(Name='my_new_index_name', ContineOnErrorForStatusCode=1)

SELECT * FROM delete_index WITH(Name='my_index_name', ContineOn404Error=1 )

SELECT * FROM generic_request 
WITH(Url='/my_index_name'
	, RequestMethod='PUT'
--	, Body='{}'
--	, Headers='X-Hdr1:aaa || x-HDR2: bbb'
	, Meta='acknowledged:bool'
	)

SELECT * FROM Indexes

SELECT * FROM get_index_metadata WITH (Index='my_index_name')

SELECT * FROM MyIndexOrAliasName --WITH(Query='{"match": { "PartNumber" : "P50" } }')

SELECT * FROM MyIndexOrAliasName --WITH(Query='{"match": { "PartNumber" : "P50" } }')

SELECT * FROM MyIndexOrAliasName WITH(Query='{"match": { "comment" : "TV" } }')
		
		--or use below - slight faster (avoids table / alias list validation)
		--SELECT * FROM search WITH(Index='MyIndexName', Query='{"match": { "comment" : "TV" } }')
		--SELECT * FROM search WITH(Index='MyIndexName', Alias='MyAliasName', Query='{"match": { "comment" : "TV" } }')

SELECT * FROM MyAliasName WITH(Query='{"match": { "comment" : "TV" } }')
		
		--or use search endpoint then you must supply both Index name and Alias name 
		--calling /search endpoint in FROM clause is slight faster (avoids table / alias list validation)
		--SELECT * FROM search WITH(Index='MyIndexName',Index='MyAliasName', Query='{"match": { "comment" : "TV" } }')

SELECT * FROM count WITH(Index='MyIndexOrAliasName') --//get count of documents in index / alias named MyIndexOrAliasName
SELECT * FROM count WITH(Index='*') --//get count of documents in all indices (total distinct _id found across all indices + alias) 
SELECT * FROM count WITH(Index='MyIndex1,MyIndex2,MyAlias1,MyAlias2')--//get count of documents in indices named MyIndex1, MyIndex2 and Alias named MyAlias1,MyAlias2
SELECT * FROM count WITH(Index='MyIndexOrAliasName', Query='{"match": { "comment" : "TV" } }') --//get count of documents in MyIndex where comment field contains word "TV"

SELECT * FROM count WITH(Index='MyIndexOrAliasName') --//get count of documents in index / alias named MyIndexOrAliasName
SELECT * FROM count WITH(Index='*') --//get count of documents in all indices (total distinct _id found across all indices + alias)
SELECT * FROM count WITH(Index='MyIndexOrAlias1,MyIndexOrAlias2') --//get count of documents in MyIndex1 and MyIndex2
SELECT * FROM count WITH(Index='MyIndex', Query='{"match": { "comment" : "TV" } }') --//get count of documents in Index named MyIndex where comment field contains word "TV"
SELECT * FROM count WITH(Index='MyAlias', Query='{"match": { "comment" : "TV" } }') --//get count of documents in Alias named MyAlias where comment field contains word "TV"

SELECT _id
        , JSON_ARRAY_FIRST(colors) as first_color
        , JSON_ARRAY_LAST(colors) as last_color
        , JSON_ARRAY_NTH(colors,3) as third_color
        , JSON_VALUE(locationList,'$.locationList[0].country') as first_preferred_country
        , JSON_VALUE(locationList,'$.locationList[?(@country=='India')].capital as capital_of_india
FROM shop WHERE _Id='1'

INSERT INTO MyIndex([MyCol1], [MyCol2] ) VALUES (100, 'A1')

INSERT INTO MyIndex(_id, [MyCol1], [MyCol2] ) VALUES ('A1234', 100, 'A1')

INSERT INTO MyIndexName ([Location.City], [Location.ZipCode], [raw::ColorsArray], [raw::SomeNestedDoc] )
--raw JSON must be in one line		
VALUES ('Atlanta', '30060', '["red","green","blue"]', '{"Col1":"aaa","Col2":"bbb","Col3":"ccc"}' )

INSERT INTO my_index (
	binary_field,
	boolean_field,
	byte_field,
	date_field,
	double_field,
	float_field,
	geo_point_field,  --raw
	--OR--
	--"geo_point_field.lat",
	--"geo_point_field.lon",
	
	geo_shape_field,  --raw
	--OR--
	--"geo_shape_field.type",
	--"geo_shape_field.coordinates",
	
	integer_field,
	ip_field,
	keyword_field,
	long_field,
	nested_field, --raw

	object_field, --raw
	--OR--
	--"object_field.field1",
	--"object_field.field2",
	
	short_field,
	text_field
)
VALUES(
	'SGVsbG8gd29ybGQ=', --binary_field  --base64 value of "Hello world"
	false, --bool
	117, --byte_field
	'2012-12-31T23:59:59.123', --date_field
	1.123456789, --double_field
	1.123456789, --float_field
	--raw JSON must be in one line
	'{ "lat": 40.7128, "lon": -74.0060 }', --geo_point_field
	--OR--
	-- 40.7128, -74.0060,
	
	'{ "type": "polygon", "coordinates": [[[-74.0060, 40.7128], [-73.9960, 40.7128], [-73.9960, 40.7028], [-74.0060, 40.7028], [-74.0060, 40.7128]]] }', --geo_shape_field
	--OR--
	--'polygon',
	--'[[[-74.0060, 40.7128], [-73.9960, 40.7128], [-73.9960, 40.7028], [-74.0060, 40.7028], [-74.0060, 40.7128]]]',
	
	123, --integer_field
	'127.0.0.1', --ip_field
	'thhi is text', --keyword_field
	1234567890, --long_field
	--raw JSON must be in one line
	'[{"nested_property_1":"nested text 1", "nested_property_2":100}, {"nested_property_1":"nested text 2", "nested_property_2":101}]', --nested_field
	'{"field1":"A","field2":"B"}', --object_field (Raw Value)
	--OR--
	--'object field keyword 1', --object_field.field1
	--123,                       --object_field.field2	
	1, --short_field
	'text field ' --text_field

)

UPDATE my_index
SET
	binary_field='SGVsbG8gd29ybGQ=', --base64 value of "Hello world"
	boolean_field=false, 
	byte_field=117, 
	date_field='2012-12-31T23:59:59.123', 
	double_field=1.123456789, 
	float_field=1.123456789, 
	--raw JSON must be in one line
	geo_point_field='{ "lat": 40.7128, "lon": -74.0060 }',
	--OR--
	--"geo_point_field.lat"=40.7128, 
	--"geo_point_field.lon"=-74.0060, 
		
	--raw JSON must be in one line
	geo_shape_field='{ "type": "polygon", "coordinates": [[[-74.0060, 40.7128], [-73.9960, 40.7128], [-73.9960, 40.7028], [-74.0060, 40.7028], [-74.0060, 40.7128]]] }',
	
	integer_field=123,
	ip_field='127.0.0.1',
	keyword_field='thhi is text',
	long_field=1234567890,
	
	--raw JSON must be in one line
	nested_field='[{"nested_property_1":"nested text 1", "nested_property_2":100}, {"nested_property_1":"nested text 2", "nested_property_2":101}]',
	
	--raw JSON must be in one line
	object_field='{"field1":"A","field2":"B"}', 
	--OR--
	--[object_field.field1]='object field keyword 1',
	--[object_field.field1]=123,
	
	short_field=1, 
	text_field='text field '
WHERE _id=2 --user defined key
--OR-- use auto-generated key
--WHERE _id='MtsicZQBuOa42vmvEtWJ'

UPSERT INTO datatype_test (
    _id, 
	binary_field,
	boolean_field,
	byte_field,
	date_field,
	double_field,
	float_field,
	geo_point_field,  --raw
	--OR--
	--"geo_point_field.lat",
	--"geo_point_field.lon",
	
	geo_shape_field,  --raw
	--OR--
	--"geo_shape_field.type",
	--"geo_shape_field.coordinates",
	
	integer_field,
	ip_field,
	keyword_field,
	long_field,
	nested_field, --raw

	object_field, --raw
	--OR--
	--"object_field.field1",
	--"object_field.field2",
	
	short_field,
	text_field
)
VALUES(
    2, -- _id (Optional - if not supplied then it inserts with auto-generated _id)
	'SGVsbG8gd29ybGQ=', --binary_field  --base64 value of "Hello world"
	false, --bool
	117, --byte_field
	'2012-12-31T23:59:59.123', --date_field
	1.123456789, --double_field
	1.123456789, --float_field
	--raw JSON must be in one line
	'{ "lat": 40.7128, "lon": -74.0060 }', --geo_point_field
	--OR--
	-- 40.7128, -74.0060,
	
	'{ "type": "polygon", "coordinates": [[[-74.0060, 40.7128], [-73.9960, 40.7128], [-73.9960, 40.7028], [-74.0060, 40.7028], [-74.0060, 40.7128]]] }', --geo_shape_field
	--OR--
	--'polygon',
	--'[[[-74.0060, 40.7128], [-73.9960, 40.7128], [-73.9960, 40.7028], [-74.0060, 40.7028], [-74.0060, 40.7128]]]',
	
	123, --integer_field
	'127.0.0.1', --ip_field
	'thhi is text', --keyword_field
	1234567890, --long_field
	--raw JSON must be in one line
	'[{"nested_property_1":"nested text 1", "nested_property_2":100}, {"nested_property_1":"nested text 2", "nested_property_2":101}]', --nested_field
	'{"field1":"A","field2":"B"}', --object_field (Raw Value)
	--OR--
	--'object field keyword 1', --object_field.field1
	--123,                       --object_field.field2	
	1, --short_field
	'text field ' --text_field

)

INSERT INTO shop(_RAWDOC_) 
--raw JSON must be in one line		
VALUES(@'{"create":{"_index":"shop","_id":"1"}}\n{"name":"record-1","colors":["yellow","orange"]}\n{"create":{"_index":"shop","_id":"2"}}\n{"name":"record-2","colors":["red","blue"]}\n')

UPDATE MyIndex
  SET Col1 = 'NewValue-1', Col2 = 'NewValue-2'
  WHERE _Id = 'A1234'

UPDATE shop 
		--raw JSON must be in one line
		SET _rawdoc_ = @'{"update": {"_index": "shop", "_id": "1"}}\n{ "doc": {"colors":["yellow","orange"] } }\n{"update": {"_index": "shop", "_id": "2"}}\n{ "doc": {"colors":["yellow","blue"] } }\n'

UPDATE MyIndex
  --raw JSON must be in one line		
  SET name = 'abcd', [raw::colors]='["yellow","red"]', [raw::location]='{x:10, y:20}' 
  WHERE _id='1'

DELETE MyIndex WHERE _id = 'A1234'

Conclusion

In this article we showed you how to connect to ElasticSearch in Python and integrate data without any coding, saving you time and effort. It's worth noting that ZappySys API Driver allows you to connect not only to ElasticSearch, but to any Java application that supports JDBC (just use a different JDBC driver and configure it appropriately).

We encourage you to download ElasticSearch Connector for Python and see how easy it is to use it for yourself or your team.

If you have any questions, feel free to contact ZappySys support team. You can also open a live chat immediately by clicking on the chat icon below.

Download ElasticSearch Connector for Python Documentation

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