Summary | Files | Setup | Usage | Issues | References

This Azure Function App enables the export of big data (10M+ records per hour) from Azure Log Analytics to Blob Storage via Python SDKs, FastAPI, and API Management. In testing, 50M records with 10 columns were successfully exported in approximately 1 hour.

Inputs and Outputs:

• Input: log analytics workspace table(s), columns, and date range
• Output: JSON (line delimited) (default), CSV, or PARQUET files

Azure FastAPI HTTP Functions:

1. azure_ingest_test_data(): creates and ingests test data (optional)
2. azure_submit_query(): submits single query that is split into smaller queries/jobs and sends to queue
3. azure_submit_query_parallel(): breaks up initial query and submits multiple queries in parallel (use for large query date ranges)
4. azure_get_status(): gives high-level status of query (number of sub-queries, successes, failures, row counts, file sizes)

Azure Queue Functions:

1. azure_queue_query(): processes split queries
2. azure_queue_process(): processes subqueries and saves output to storage blobs
3. azure_queue_query_poison(): processes invalid messages in query queue and saves to table log
4. azure_queue_process_poison(): processes invalid message in process queue and saves to table log

• azure-log-analytics-data-export.ipynb: python notebook for development, testing, or interactive use
• function_app.py: Azure Function App python source code
• host.json: Azure Function App settings
• requirements.txt: python package requirements file
• function.app: zip push deployment
• zip-push-deployment.txt: instructions on deploying streamlined function without APIM via CLI

You will need to have access to or provision the following Azure Resources:

1. Log Analytics Workspace (data source)
2. Storage Account

• 1 Container (data output destination)
• 4 Queues (temp storage for split query messages/jobs)
• 3 Tables (logging)

3. Azure Function App (Python 3.11+, premium)

• Clone this repo, use VS Code, install Azure Functions tools/extension, deploy to Azure subscription
• Reference: Create a function in Azure with Python using Visual Studio Code

4. Azure API Management (consumption, DO NOT use developer) (optional)

Authentication (Managed Identity) Roles Setup:

• Azure Portal -> Function App -> Identity -> System Assigned -> On -> Add Azure Role Assignments

1. Monitoring Metrics Publisher: Ingest to Log Analytics (optional)
2. Log Analytics Contributor: Query Log Analytics
3. Storage Queue Data Contributor: Storage Queue Send/Get/Delete
4. Storage Queue Data Message Processor: Storage Queue Trigger for Azure Function
5. Storage Blob Data Contributor: Upload to Blob Storage
6. Storage Table Data Contributor: Logging

Environment Variables for Queue Triggers via Managed Identity:

• Setup via Azure Portal -> Function App -> Settings -> Configuration -> Environment Variables

1. storageAccountConnectionString__queueServiceUri -> https://<STORAGE_ACCOUNT>.queue.core.windows.net/
2. storageAccountConnectionString__credential -> managedidentity
3. QueueQueryName -> <STORAGE_QUEUE_NAME_FOR_QUERIES>
4. QueueProcessName -> <STORAGE_QUEUE_NAME_FOR_PROCESSING>
5. LogAnalyticsEndpoint -> <LAW_ENDPOINT_URL>

Optional Environment Variables (reduces number of params in requests):

• Setup via Azure Portal -> Function App -> Settings -> Configuration -> Environment Variables

1. QueueURL -> <STORAGE_QUEUE_URL>
2. TableURL -> <STORAGE_TABLE_URL>
3. TableIngestName -> <STORAGE_TABLE_INGEST_LOG_NAME>
4. TableQueryName -> <STORAGE_TABLE_QUERY_LOG_NAME>
5. TableProcessName -> <STORAGE_TABLE_PROCESS_LOG_NAME>

Azure Storage Setup:

1. Create 1 container for data output files
   • <STORAGE_CONTAINER_NAME>
2. Create 4 queues for messages/jobs
   • <STORAGE_QUEUE_NAME_FOR_QUERIES>
   • <STORAGE_QUEUE_NAME_FOR_PROCESSING>
   • <STORAGE_QUEUE_NAME_FOR_QUERIES>-poison for failed messages
   • <STORAGE_QUEUE_NAME_FOR_PROCESSING>-poison for failed messages
3. Create 3 tables for logging (i.e. ingestlog, querylog, and processlog)
   • <STORAGE_TABLE_INGEST_LOG_NAME>
   • <STORAGE_TABLE_QUERY_LOG_NAME>
   • <STORAGE_TABLE_PROCESS_LOG_NAME>

API Management (APIM) Setup (Optional):

• Note: APIM is used to access the FastAPI Swagger/OpenAPI docs

1. Create APIM Service -> Consumption Pricing Tier (DO NOT use developer)
2. Add new API -> Function App
   • Function App: <YOUR_FUNCTION>
   • Display Name: Protected API Calls
   • Name: protected-api-calls
   • Suffix: api
3. Remove all operations besides POST
   • Edit POST operation
     • Display name: azure_ingest_test_data
     • URL: POST /azure_ingest_test_data
   • Clone and Edit new POST operation
     • Display name: azure_submit_query
     • URL: POST /azure_submit_query
   • Clone and Edit new POST operation
     • Display name: azure_submit_query_parallel
     • URL: POST /azure_submit_query_parallel
   • Clone and Edit new POST operation
     • Display name: azure_get_status_post
     • URL: POST /azure_get_status
   • Clone azure_get_status operation
     • Change from POST to GET
     • Display name: azure_get_status
     • URL: GET /azure_get_status
   • Edit OpenAPI spec json operation ids to match above
4. Add new API -> Function App
   • Function App: <YOUR_FUNCTION>
   • Display Name: Public Docs
   • Name: public-docs
   • Suffix: public
5. Remove all operations besides GET
   • Settings -> uncheck 'subscription required'
   • Edit GET operation
     • Display name: Documentation
     • URL: GET /docs
   • Clone and Edit new GET operation
     • Display name: OpenAPI Schema
     • URL: GET /openapi.json
   • Edit OpenAPI spec json operation ids to match above
   • Test at https://<APIM_ENDPOINT_NAME>.azure-api.net/public/docs

Queue Trigger Setup::

• To fix message encoding errors (default is base64), add "extensions": {"queues": {"messageEncoding": "none"}} to host.json
• Note: Failed messages/jobs are sent to <QUEUE_NAME>-poison

Optional Data Collection Endpoint and Rule Setup for Log Analytics Ingest:

1. Azure Portal -> Monitor -> Data Collection Endpoints -> Create
2. Azure Portal -> Log Analytics -> Tables -> Create New Custom Table

• Create new Data Collection Rule (DCR)
• Create data_sample.json with sample schema of {"TimeGenerated" : "2024-01-01 00:00:00", "DataColumn1" : "string", "DataColumn2" : "string", ... "DataColumn10" : "string"}
• Azure Portal -> Monitor -> Data Collection Rules -> JSON View -> immutableId, outputStream
• For the DCE+DCR, assign the Monitoring Metrics Publisher role

4. If changing or updating number of columns later:

• azure portal -> log analytics -> settings - tables -> ... -> edit schema
• azure portal -> data collection rules -> export template -> deploy -> edit

3. Reference: Tutorial: Send data to Azure Monitor Logs with Logs ingestion API (Azure portal)

Optional Security Settings:

1. Restrict Azure Function App and APIM to specific IP address range(s)

• Networking -> Public Access -> Select Virtual Networks or IPs

Azure Default Credential Microsoft Entra Endpoints Set AZURE_AUTHORITY_HOST enviorment variable

• Public Cloud: https://login.microsoftonline.com (default)
• US Gov Cloud: https://login.microsoftonline.us

Swagger UI Docs at https://<APIM_ENDPOINT_NAME>.azure-api.net/public/docs

• API calls require a APIM subscription key
• APIM -> Subscription -> Create Subscription -> Copy Key
• Paste in "Ocp-Apim-Subscription-Key" header field

1. azure_submit_query() or azure_submit_query_parallel():

• HTTP POST Example:

{ "subscription_id" : "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX", "resource_group_name" : "XXXXXXXXXXXXXXXXXXXXXXX", "log_analytics_worksapce_name" : "XXXXXXXXXXXXXXXX", "log_analytics_workspace_id" : "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX", "storage_blob_url" : "https://XXXXXXXXXXXXXXXXXXXXX.blob.core.windows.net/", "storage_blob_container_name" : "XXXXXXXXXXXXX", "table_names_and_columns" : { "XXXXXXXXXXXXXXX": ["TimeGenerated","DataColumn1","DataColumn2","DataColumn3","DataColumn4","DataColumn5","DataColumn6","DataColumn7","DataColumn8","DataColumn9", "DataColumn10"]}, "start_datetime" : "2024-03-19 00:00:00", "end_datetime" : "2024-03-20 00:00:00" }

• HTTP Response Examples:
  • azure_submit_query()
  • azure_submit_query_parallel()

{ "query_uuid": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX", "submit_status": "Success", "table_names": "XXXXXXXXXXX", "start_datetime": "2024-03-19 00:00:00.000000", "end_datetime": "2024-03-20 00:00:00.000000", "total_row_count": 23000000, "subqueries_generated": 95, "subqueries_sent_to_queue": 95, "runtime_seconds": 92.1, "submit_datetime": "2024-03-26 16:24:38.771336" }

{ "query_uuid": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX", "split_status": "Success", "table_names": "XXXXXXXXXXX", "start_datetime": "2024-04-04 00:00:00.000000", "end_datetime": "2024-04-10 00:00:00.000000", "number_of_messages_generated": 6, "number_of_messages_sent": 6, "total_row_count": 2010000, "runtime_seconds": 0.9, "split_datetime": "2024-04-12 14:06:41.688752" }

2. azure_get_status():

• HTTP POST Request Example:

{ "query_uuid" : "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX" }

• HTTP Response Example:

{ "query_uuid": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX", "query_partitions" : 1, "submit_status": "Success", "processing_status": "Partial", "percent_complete": 29.5, "runtime_since_submit_seconds": 463.6, "estimated_time_remaining_seconds": 1107.9, "number_of_subqueries": 95, "number_of_subqueries_success": 28, "number_of_subqueries_failed": 0, "query_row_count": 23000000, "output_row_count": 6972002, "output_file_size": 2.05, "output_file_units" : "GB" }

Note: need to extend or add hidden columns (_ prefix) manually

let cols = toscalar(<TABLE_NAME> | extend _ItemId | getschema | summarize make_list(ColumnName) ); print(cols)

1. Azure Function App stops processing sub-queries, queue trigger not processing messages in queue:

   • Manually restart Azure Function App in Azure Portal
   • Use Premium Plan

2. Submit exceed 10 min Azure Function limit and fails

   • Use azure_submit_query_parallel() function
   • Reduce the datetime range of the query (recommend less than 100M records per request)
   • Decrease break_up_query_freq value in azure_submit_query()
   • Decrease parallel_process_break_up_query_freq value in azure_submit_query_parallel()
   • Use Premium Plan with no time limit, change in hosts.json

3. Table row count values exceeding 2,147,483,647

   • Change type from int32 to int64

4. Hidden columns are not auto-detected

   • Add hidden columns manually in list of columns

2.2.0 (12/20/2024):

• Changed output file format to better mimic countinous export

2.1.3 (12/13/2024):

• Added changes for US Gov CLoud
• Added _ItemId hidden column by default
• Added error checking for table/column names before processing
• fixed logging bugs
• updated documenation for data ingestion
• lowered defaults (number of rows) to avoid azure errors

2.1.0:

• Updated azure queue triggers to use blueprints
• Added Zip Deployment
• Fixed uuid bug, fixed table query bug

2.0.0:

• Changed Azure Function code to use FastAPI in order to use Swagger UI
• Added pydantic input/output JSON schemas
• Updated documentation

1.5.0:

• Added azure_submit_queries() function for larger datetime ranges and parallel processing

1.4.0:

• Refactored code and made pylint edits
• Changed logging to % formatting from f-strings

1.3.1:

• Fixed UTC time zone bug
• Added estimated time remaining to get_status() response
• Added option to put storage queue and table params in env variables

1.3.0:

• Added pydantic input validation
• Added Open API yaml file for Azure API Management

1.2.0:

• Added get_status() azure function

1.1.0:

• Added logging to Azure Table Storage
• Added row count checks

1.0.0:

• Initial release

1. How to use logic apps to handle large amounts of data from log analtyics
2. FastAPI on Azure Functions