ElasticSearch Connector for MS Access In this article you will learn how to integrate ElasticSearch data in MS Access without coding in just a few clicks (live / bi-directional connection to 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. Using ElasticSearch Connector you will be able to connect, read, and write data from within MS Access. Follow the steps below to see how we would accomplish that. Download Documentation The driver mentioned above is part of ODBC PowerPack which is a collection of high-performance Drivers for various API data source (i.e. REST API, JSON, XML, CSV, Amazon S3 and many more). Using familiar SQL query language you can make live connections and read/write data from API sources or JSON / XML / CSV Files inside SQL Server (T-SQL) or your favorite Reporting (i.e. Power BI, Tableau, Qlik, SSRS, MicroStrategy, Excel, MS Access), ETL Tools (i.e. Informatica, Talend, Pentaho, SSIS). You can also call our drivers from programming languages such as JAVA, C#, Python, PowerShell etc. If you are new to ODBC and ZappySys ODBC PowerPack then check the following links to get started. ODBC PowerPack - System Requirements and Installation Getting Started - Using ODBC PowerPack ODBC API Driver Help File

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

Step-by-step instructions

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

1. Install ZappySys ODBC PowerPack.

2. Open ODBC Data Sources (x64):
   

3. Create a User Data Source (User DSN) based on ZappySys API Driver

   ZappySys API Driver

   
   You should create a System DSN (instead of a User DSN) if the client application is launched under a Windows System Account, e.g. as a Windows Service. If the client application is 32-bit (x86) running with a System DSN, use ODBC Data Sources (32-bit) instead of the 64-bit version.

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]

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

   ElasticSearchDSN

   ElasticSearch

   Basic Authentication (UserId/Password) [Http]

   http://localhost:9200

   Required Parameters
   Optional Parameters
   UserName	Fill in the parameter...
   Password	Fill in the parameter...
   IgnoreSSLCertificateErrors	Fill in the parameter...

   

    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

   Required Parameters
   Optional Parameters
   IgnoreSSLCertificateErrors	Fill in the parameter...

   

6. Once the data source has been configured, you can preview data. Select the Preview tab and use settings similar to the following to preview data:
   

7. Click OK to finish creating the data source.

Video instructions

Read data in Microsoft Access from the ODBC data source

1. First of all, open MS Access and create a new MS Access database.

2. In the next step, start loading ODBC data source we created:

3. Then click next until data source selection window appears. Select the data source we created in one of the previous steps and hit OK:

   ElasticSearchDSN

   

4. Continue with tables and views selection. You can extract multiple tables or views:
   

5. Finally, wait while data is being loaded and once done you should see a similar view:

Using Linked Table for Live Data (Slow)

Linked tables in Microsoft Access are crucial for online databases because they enable real-time access to centralized data, support scalability, facilitate collaboration, enhance data security, ease maintenance tasks, and allow integration with external systems. They provide a flexible and efficient way to work with data stored in online databases, promoting cross-platform compatibility and reducing the need for data duplication.

1. Real-Time Data Access:
   Access can interact directly with live data in online databases, ensuring that users always work with the most up-to-date information.
2. Centralized Data Management:
   Online databases serve as a centralized repository, enabling efficient management of data from various locations.
3. Ease of Maintenance:
   Updates or modifications to the online database structure are automatically reflected in Access, streamlining maintenance tasks.
4. Adaptability to Changing Requirements:
   Linked tables provide flexibility, allowing easy adaptation to changing data storage needs or migration to different online database systems.

Let's create the linked table.

1. Launch Microsoft Access and open the database where you want to create the linked table.

2. Go to the "External Data" tab on the Ribbon. >> "New Data Source" >> "From Other Sources" >> "ODBC Database"

3. Select the option "Link to Data Source by creating a linked table:

4. Continue by clicking 'Next' until the Data Source Selection window appears. Navigate to the Machine Data Source tab and select the desired data source established in one of the earlier steps. Click 'OK' to confirm your selection.

   ElasticSearchDSN

   

5. Proceed to the selection of Tables and Views. You have the option to extract multiple tables or views:
   

6. When prompted to select Unique Key column DO NOT select any column(s) and just click OK:

7. Finally, Simply double-click the newly created Linked Table to load the data:

Guide to Effectively Addressing Known Issues

Discover effective strategies to address known issues efficiently in this guide. Get solutions and practical tips to streamline troubleshooting and enhance system performance, ensuring a smoother user experience.

Fewer Rows Imported

The reason for this is that MS Access has a default query timeout of 60 seconds, which means it stops fetching data if the query takes longer than that. As a result, only a limited number of rows are fetched within this time frame.

To address this, we can adjust the Query Timeout by following the steps below.

The path may vary depending on the MS Access bitness, such as 32-bit versus 64-bit.

\HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Jet\4.0\Engines\ODBC

\HKEY_LOCAL_MACHINE\SOFTWARE\WOW6432Node\Microsoft\Jet\4.0\Engines\ODBC

\HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Office\ClickToRun\REGISTRY\MACHINE\Software\Microsoft\Office\16.0\Access Connectivity Engine\Engines\ODBC

We can identify this issue by examining the Fiddler Log, as MS Access doesn't display any error regarding partial import, which is quite unusual

Please refer to this link : How to use Fiddler to analyze HTTP web requests

#Deleted word appears for column value in MS Access for Linked Table mode

If you used Linked Table mode to get external data and it shows #deleted word rather than actual value for column after you open then most likely its following issue.

Make sure to re-create Linked Table and DO NOT select any key column when prompted (Just click OK)
How to Fix

Table Selection UI Opening Delays

The Table selection UI takes a significant amount of time to open after clicking the 'New Data Source' -> 'Other Data Sources' -> 'ODBC'

The reason for this issue is that MS Access sends a dummy query, leading to several unnecessary pagination cycles before an error is thrown. To mitigate this, we can prevent wasted cycles by configuring the 'Throw error if no match' setting on the Filter Options Tab.

Enhancing Performance through Metadata Addition (Reduces Query Time)

We can optimize query performance by creating Virtual Tables (i.e. views with custom SQL) on Datasource and incorporating META=static columns. Learn how to capture static metadata in this guide.
Performance Options - Generate Metadata Manually

Execute the query initially, save the metadata by selecting 'Save to Meta' (choose Compact Format), and then click 'Save to Clipboard.' Utilize the resulting list by pasting it into the META attribute as follows: 'META=paste here.'

SELECT * FROM products
    WITH(
        META='id:String(20); title:String(100);  description:String(500);'
    )

Optimize Workflow with Automated Import

Employ Automated Import when Linked Tables are not feasible, and we need to depend on Imported Tables with static data.

While using Linked Tables sometime it encounter errors, and we are left with no alternative but to utilize Imported Tables, Automatic Refresh becomes crucial in such scenarios.

Here's a guide on automating refreshes. We can set up automatic refresh on different events, such as when the database opens, a form is opened, or a button is clicked.

To initiate the import process, follow these steps:

• Perform the data import using the standard manual steps.
• In the final step, we'll encounter a checkbox labeled 'Save Import Steps.' Ensure to check this option.
• After saving the steps, we can locate their name in the Save Imports UI. Identify the name associated with the saved steps.
• "Now, we can execute the code as shown below:"

Private Sub cmdYes_Click()
    Label0.Visible = True
    DoCmd.RunSavedImportExport "Import-DATA.products"
    Label0.Visible = False
End Sub

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([MY_API_SERVICE], '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([MY_API_SERVICE], '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 MS Access 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] )
VALUES ('A1234', 'Atlanta', '30060', '["red","green","blue"]', '{"Col1":"aaa","Col2":"bbb","Col3":"ccc"}' )

INSERT INTO shop(_RAWDOC_) 
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 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
  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 discussed how to connect to ElasticSearch in MS Access and integrate data without any coding. Click here to Download ElasticSearch Connector for MS Access and try yourself see how easy it is. If you still have any question(s) then ask here or simply click on live chat icon below and ask our expert (see bottom-right corner of this page).

Download ElasticSearch Connector for MS Access Documentation 

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Download ElasticSearch Connector for MS Access Documentation

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