# Azure Data Explorer Output Plugin

This plugin writes data collected by any of the Telegraf input plugins to [Azure
Data Explorer](https://azure.microsoft.com/en-au/services/data-explorer/).
Azure Data Explorer is a distributed, columnar store, purpose built for any type
of logs, metrics and time series data.

## Pre-requisites

- [Create Azure Data Explorer cluster and
  database](https://docs.microsoft.com/en-us/azure/data-explorer/create-cluster-database-portal)
- VM/compute or container to host Telegraf - it could be hosted locally where an
  app/service to be monitored is deployed or remotely on a dedicated monitoring
  compute/container.

## Configuration

```toml @sample.conf
# Sends metrics to Azure Data Explorer
[[outputs.azure_data_explorer]]
  ## The URI property of the Azure Data Explorer resource on Azure
  ## ex: endpoint_url = https://myadxresource.australiasoutheast.kusto.windows.net
  endpoint_url = ""

  ## The Azure Data Explorer database that the metrics will be ingested into.
  ## The plugin will NOT generate this database automatically, it's expected that this database already exists before ingestion.
  ## ex: "exampledatabase"
  database = ""

  ## Timeout for Azure Data Explorer operations
  # timeout = "20s"

  ## Type of metrics grouping used when pushing to Azure Data Explorer.
  ## Default is "TablePerMetric" for one table per different metric.
  ## For more information, please check the plugin README.
  # metrics_grouping_type = "TablePerMetric"

  ## Name of the single table to store all the metrics (Only needed if metrics_grouping_type is "SingleTable").
  # table_name = ""

  ## Creates tables and relevant mapping if set to true(default).
  ## Skips table and mapping creation if set to false, this is useful for running Telegraf with the lowest possible permissions i.e. table ingestor role.
  # create_tables = true
```

## Metrics Grouping

Metrics can be grouped in two ways to be sent to Azure Data Explorer. To specify
which metric grouping type the plugin should use, the respective value should be
given to the `metrics_grouping_type` in the config file. If no value is given to
`metrics_grouping_type`, by default, the metrics will be grouped using
`TablePerMetric`.

### TablePerMetric

The plugin will group the metrics by the metric name, and will send each group
of metrics to an Azure Data Explorer table. If the table doesn't exist the
plugin will create the table, if the table exists then the plugin will try to
merge the Telegraf metric schema to the existing table. For more information
about the merge process check the [`.create-merge` documentation][create-merge].

The table name will match the `name` property of the metric, this means that the
name of the metric should comply with the Azure Data Explorer table naming
constraints in case you plan to add a prefix to the metric name.

[create-merge]: https://docs.microsoft.com/en-us/azure/data-explorer/kusto/management/create-merge-table-command

### SingleTable

The plugin will send all the metrics received to a single Azure Data Explorer
table. The name of the table must be supplied via `table_name` in the config
file. If the table doesn't exist the plugin will create the table, if the table
exists then the plugin will try to merge the Telegraf metric schema to the
existing table. For more information about the merge process check the
[`.create-merge` documentation][create-merge].

## Tables Schema

The schema of the Azure Data Explorer table will match the structure of the
Telegraf `Metric` object. The corresponding Azure Data Explorer command
generated by the plugin would be like the following:

```text
.create-merge table ['table-name']  (['fields']:dynamic, ['name']:string, ['tags']:dynamic, ['timestamp']:datetime)
```

The corresponding table mapping would be like the following:

```text
.create-or-alter table ['table-name'] ingestion json mapping 'table-name_mapping' '[{"column":"fields", "Properties":{"Path":"$[\'fields\']"}},{"column":"name", "Properties":{"Path":"$[\'name\']"}},{"column":"tags", "Properties":{"Path":"$[\'tags\']"}},{"column":"timestamp", "Properties":{"Path":"$[\'timestamp\']"}}]'
```

**Note**: This plugin will automatically create Azure Data Explorer tables and
corresponding table mapping as per the above mentioned commands.

## Authentiation

### Supported Authentication Methods

This plugin provides several types of authentication. The plugin will check the
existence of several specific environment variables, and consequently will
choose the right method.

These methods are:

1. AAD Application Tokens (Service Principals with secrets or certificates).

    For guidance on how to create and register an App in Azure Active Directory
    check [this article][register], and for more information on the Service
    Principals check [this article][principal].

2. AAD User Tokens

    - Allows Telegraf to authenticate like a user. This method is mainly used
      for development purposes only.

3. Managed Service Identity (MSI) token

    - If you are running Telegraf from Azure VM or infrastructure, then this is
      the prefered authentication method.

[register]: https://docs.microsoft.com/en-us/azure/active-directory/develop/quickstart-register-app#register-an-application

[principal]: https://docs.microsoft.com/en-us/azure/active-directory/develop/app-objects-and-service-principals

Whichever method, the designated Principal needs to be assigned the `Database
User` role on the Database level in the Azure Data Explorer. This role will
allow the plugin to create the required tables and ingest data into it.  If
`create_tables=false` then the designated principal only needs the `Database
Ingestor` role at least.

### Configurations of the chosen Authentication Method

The plugin will authenticate using the first available of the following
configurations, **it's important to understand that the assessment, and
consequently choosing the authentication method, will happen in order as
below**:

1. **Client Credentials**: Azure AD Application ID and Secret.

    Set the following environment variables:

    - `AZURE_TENANT_ID`: Specifies the Tenant to which to authenticate.
    - `AZURE_CLIENT_ID`: Specifies the app client ID to use.
    - `AZURE_CLIENT_SECRET`: Specifies the app secret to use.

2. **Client Certificate**: Azure AD Application ID and X.509 Certificate.

    - `AZURE_TENANT_ID`: Specifies the Tenant to which to authenticate.
    - `AZURE_CLIENT_ID`: Specifies the app client ID to use.
    - `AZURE_CERTIFICATE_PATH`: Specifies the certificate Path to use.
    - `AZURE_CERTIFICATE_PASSWORD`: Specifies the certificate password to use.

3. **Resource Owner Password**: Azure AD User and Password. This grant type is
   *not recommended*, use device login instead if you need interactive login.

    - `AZURE_TENANT_ID`: Specifies the Tenant to which to authenticate.
    - `AZURE_CLIENT_ID`: Specifies the app client ID to use.
    - `AZURE_USERNAME`: Specifies the username to use.
    - `AZURE_PASSWORD`: Specifies the password to use.

4. **Azure Managed Service Identity**: Delegate credential management to the
   platform. Requires that code is running in Azure, e.g. on a VM. All
   configuration is handled by Azure. See [Azure Managed Service Identity][msi]
   for more details. Only available when using the [Azure Resource
   Manager][arm].

[msi]: https://docs.microsoft.com/en-us/azure/active-directory/msi-overview
[arm]: https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview

## Querying data collected in Azure Data Explorer

Examples of data transformations and queries that would be useful to gain
insights -

### Using SQL input plugin

Sample SQL metrics data -

name | tags | timestamp | fields
-----|------|-----------|-------
sqlserver_database_io|{"database_name":"azure-sql-db2","file_type":"DATA","host":"adx-vm","logical_filename":"tempdev","measurement_db_type":"AzureSQLDB","physical_filename":"tempdb.mdf","replica_updateability":"READ_WRITE","sql_instance":"adx-sql-server"}|2021-09-09T13:51:20Z|{"current_size_mb":16,"database_id":2,"file_id":1,"read_bytes":2965504,"read_latency_ms":68,"reads":47,"rg_read_stall_ms":42,"rg_write_stall_ms":0,"space_used_mb":0,"write_bytes":1220608,"write_latency_ms":103,"writes":149}
sqlserver_waitstats|{"database_name":"azure-sql-db2","host":"adx-vm","measurement_db_type":"AzureSQLDB","replica_updateability":"READ_WRITE","sql_instance":"adx-sql-server","wait_category":"Worker Thread","wait_type":"THREADPOOL"}|2021-09-09T13:51:20Z|{"max_wait_time_ms":15,"resource_wait_ms":4469,"signal_wait_time_ms":0,"wait_time_ms":4469,"waiting_tasks_count":1464}

Since collected metrics object is of complex type so "fields" and "tags" are
stored as dynamic data type, multiple ways to query this data-

1. Query JSON attributes directly: Azure Data Explorer provides an ability to
   query JSON data in raw format without parsing it, so JSON attributes can be
   queried directly in following way:

  ```text
  Tablename
  | where name == "sqlserver_azure_db_resource_stats" and todouble(fields.avg_cpu_percent) > 7
  ```

  ```text
  Tablename
  | distinct tostring(tags.database_name)
  ```

  **Note** - This approach could have performance impact in case of large
  volumes of data, use belwo mentioned approach for such cases.

1. Use [Update
   policy](https://docs.microsoft.com/en-us/azure/data-explorer/kusto/management/updatepolicy)**:
   Transform dynamic data type columns using update policy. This is the
   recommended performant way for querying over large volumes of data compared
   to querying directly over JSON attributes:

  ```json
  // Function to transform data
  .create-or-alter function Transform_TargetTableName() {
        SourceTableName
        | mv-apply fields on (extend key = tostring(bag_keys(fields)[0]))
        | project fieldname=key, value=todouble(fields[key]), name, tags, timestamp
  }

  // Create destination table with above query's results schema (if it doesn't exist already)
  .set-or-append TargetTableName <| Transform_TargetTableName() | limit 0

  // Apply update policy on destination table
  .alter table TargetTableName policy update
  @'[{"IsEnabled": true, "Source": "SourceTableName", "Query": "Transform_TargetTableName()", "IsTransactional": true, "PropagateIngestionProperties": false}]'
  ```

### Using syslog input plugin

Sample syslog data -

name | tags | timestamp | fields
-----|------|-----------|-------
syslog|{"appname":"azsecmond","facility":"user","host":"adx-linux-vm","hostname":"adx-linux-vm","severity":"info"}|2021-09-20T14:36:44Z|{"facility_code":1,"message":" 2021/09/20 14:36:44.890110 Failed to connect to mdsd: dial unix /var/run/mdsd/default_djson.socket: connect: no such file or directory","procid":"2184","severity_code":6,"timestamp":"1632148604890477000","version":1}
syslog|{"appname":"CRON","facility":"authpriv","host":"adx-linux-vm","hostname":"adx-linux-vm","severity":"info"}|2021-09-20T14:37:01Z|{"facility_code":10,"message":" pam_unix(cron:session): session opened for user root by (uid=0)","procid":"26446","severity_code":6,"timestamp":"1632148621120781000","version":1}

There are multiple ways to flatten dynamic columns using 'extend' or
'bag_unpack' operator. You can use either of these ways in above mentioned
update policy function - 'Transform_TargetTableName()'

- Use
  [extend](https://docs.microsoft.com/en-us/azure/data-explorer/kusto/query/extendoperator)
  operator - This is the recommended approach compared to 'bag_unpack' as it is
  faster and robust. Even if schema changes, it will not break queries or
  dashboards.

  ```text
  Tablenmae
  | extend facility_code=toint(fields.facility_code), message=tostring(fields.message), procid= tolong(fields.procid), severity_code=toint(fields.severity_code),
  SysLogTimestamp=unixtime_nanoseconds_todatetime(tolong(fields.timestamp)), version= todouble(fields.version),
  appname= tostring(tags.appname), facility= tostring(tags.facility),host= tostring(tags.host), hostname=tostring(tags.hostname), severity=tostring(tags.severity)
  | project-away fields, tags
  ```

- Use [bag_unpack
  plugin](https://docs.microsoft.com/en-us/azure/data-explorer/kusto/query/bag-unpackplugin)
  to unpack the dynamic type columns automatically. This method could lead to
  issues if source schema changes as its dynamically expanding columns.

  ```text
  Tablename
  | evaluate bag_unpack(tags, columnsConflict='replace_source')
  | evaluate bag_unpack(fields, columnsConflict='replace_source')
  ```
