Log Policy

This document explains the detailed usage of a specific policy. If you are using the Apinizer policy framework for the first time or want to learn about the general working principles of policies, we recommend reading the What is a Policy? page first.

Overview

Purpose

The Log policy captures a snapshot of the current message state at any point in the message processing pipeline and sends it to the configured connectors.
Unlike standard traffic logging, it can be placed at intermediate points in the pipeline (for example, before and after a transformation policy) to observe message changes.
It works with HTTP, WebSocket, and gRPC protocols.
It can send data to multiple connectors simultaneously.

How It Works

Policy Trigger: Runs when the log policy’s turn comes in the message processing pipeline.
Snapshot Creation: The current message state is captured per protocol (headers, parameters, body information).
Send to Connectors: The snapshot is sent to each defined connector individually.
Privacy Application: If privacy settings are enabled, sensitive data is processed before delivery (mask, delete, hash, encrypt).

Use Cases

Monitor message state at specific pipeline stages (before/after policy comparison)
Collect detailed logs for debugging and troubleshooting
Meet audit and compliance requirements
Send real-time data to external systems (SIEM, log analysis platforms)

Supported Targets

Target	Protocol
Elasticsearch	REST/HTTP
Database (Oracle, MySQL/MariaDB, PostgreSQL, SQL Server, MongoDB)	JDBC / MongoDB Driver
Graylog	GELF
Webhook	HTTP POST
Syslog	Syslog Protocol
Kafka	Kafka Producer
RabbitMQ	AMQP

Protocol Support

Protocol	Header	Parameter	Body
HTTP	✓	✓	✓
WebSocket	✓	—	✓
gRPC	—	—	✓

In the WebSocket and gRPC protocols, header and parameter information may be limited due to the nature of the protocol.

Configuration Fields

General Settings

Field	Description
Name	Identifier name of the policy
Description	Description explaining the purpose of the policy
Active	Whether the policy is active or passive
Execution Mode	Synchronous or Asynchronous execution mode (detailed below)
Condition	Condition that determines when the policy runs (optional)

Connector Selection

You can select the connectors the policy will send logs to. Connectors are selected through their connection definition and work environment-independent — the same connection definition may correspond to different connector instances across environments.

Definitions marked as global policies can be used in multiple environments; the connector corresponding to the same connection definition must be configured in each environment.

Logged Fields

You can choose which of the following field groups are included in the log record:

Field Group	Description
From-Client Request Headers	Request header information from the client to the API Proxy
From-Client Request Parameters	Request parameters from the client to the API Proxy
From-Client Request Body	Request body from the client to the API Proxy
To-Backend Request Headers	Request header information from the API Proxy to the Backend API
To-Backend Request Parameters	Request parameters from the API Proxy to the Backend API
To-Backend Request Body	Request body from the API Proxy to the Backend API
From-Backend Response Headers	Response header information from the Backend API
From-Backend Response Body	Response body from the Backend API
To-Client Response Headers	Response header information sent to the client
To-Client Response Body	Response body sent to the client
Metadata	Request metadata such as IP address, HTTP method, URI, port
Metrics	Performance metrics such as duration, size, cache state

Body Logging Mode

Mode	Description
Full	The body data is logged in full
Partial	The body data is logged up to the specified maximum size. Data is truncated when the size is exceeded

Execution Modes

Mode	Delivery	Error Behavior	Pipeline Effect
Synchronous (default)	Sequential, waits until completion	Error is raised	Pipeline is interrupted
Asynchronous	In the background, does not wait	Error is logged and swallowed	Pipeline continues

In synchronous mode, a connector error interrupts the pipeline and returns an error to the client. If uninterrupted operation is required in production, prefer the asynchronous mode.

In asynchronous mode, when a connector error occurs, the error information is written to the application logs, but the client request is not affected.

Data Structure

The Log policy uses a lightweight data structure that is different from standard API traffic logs. The following table lists the fields sent and their Elasticsearch/database equivalents.

Field Name	Short Name (ES/JSON)	Type	Description
Timestamp	@timestamp	Date	Log record creation time
Correlation ID	aci	String	Apinizer request tracking ID
Environment ID	ei	String	Gateway environment identifier
API Proxy ID	api	String	API Proxy identifier
API Proxy Name	apn	String	API Proxy name
API Proxy Method ID	apmi	String	API Proxy method identifier
API Proxy Method Name	apmn	String	API Proxy method name
User/Key	uok	String	Authenticated username or API key
HTTP Status Code	sc	Number	HTTP response status code
Result Type	rt	String	Operation result (SUCCESS, ERROR, etc.)
Error Type	et	String	Error type (if any)
Request Headers	fcrh	List	Request headers as key-value pairs
Request Parameters	fcrp	List	Query parameters as key-value pairs
Request Body	fcrb	String	Request body content
Pipeline Position	cr	String	Pipeline region where the log policy is placed
Attachment Scope	cl	String	Hierarchy level where the log policy is attached

Example JSON Output

{
  "@timestamp": "2026-03-31T12:30:45.123Z",
  "aci": "550e8400-e29b-41d4-a716-446655440000",
  "ei": "env-production-001",
  "api": "proxy-payment-api",
  "apn": "Payment API",
  "apmi": "method-create-payment",
  "apmn": "POST /payments",
  "uok": "merchant-api-key-123",
  "sc": 200,
  "rt": "SUCCESS",
  "fcrh": [
    { "k": "Content-Type", "v": "application/json" },
    { "k": "Authorization", "v": "Bearer eyJhbGciOi..." },
    { "k": "X-Request-ID", "v": "req-abc-123" }
  ],
  "fcrp": [
    { "k": "currency", "v": "TRY" },
    { "k": "lang", "v": "tr" }
  ],
  "fcrb": "{\"amount\": 150.00, \"merchantId\": \"M-001\"}",
  "cr": "FROM_CLIENT",
  "cl": "API_PROXY_METHOD"
}

Each entry in the header and parameter fields consists of k (key) and v (value) pairs. This structure is indexed as nested type in Elasticsearch.

Pipeline Position (`cr`) Values

Value	Description
`FROM_CLIENT`	First pipeline stage where the incoming client request is processed
`TO_BACKEND`	Last pipeline stage before the request is forwarded to the backend
`FROM_BACKEND`	First pipeline stage where the backend response is processed
`TO_CLIENT`	Last pipeline stage before the response is sent to the client

Attachment Scope (`cl`) Values

Value	Description
`API_PROXY_GROUP`	Policy is attached to a proxy group; applies to all proxies in the group
`API_PROXY`	Policy is attached directly to an API Proxy
`API_PROXY_METHOD`	Policy is attached to a specific API Proxy method

Elasticsearch Integration

A separate index template must be created for log policy data in Elasticsearch. This template is different from the standard API traffic log template and contains only the fields sent by the log policy.

The Elasticsearch index template and ILM policy for log policy data are not automatically created from the Apinizer UI. You need to manually apply the following steps on Elasticsearch.

Step 1: Create ILM Policy

Create an ILM policy for index lifecycle management. The following example creates a policy that rolls over at 30 GB or 1 day and deletes after 30 days. Adjust the values according to your needs.

PUT _ilm/policy/apinizer-log-policy-capture-ilm
{
  "policy": {
    "phases": {
      "hot": {
        "actions": {
          "rollover": {
            "max_size": "30gb",
            "max_age": "1d"
          }
        }
      },
      "delete": {
        "min_age": "30d",
        "actions": {
          "delete": {}
        }
      }
    }
  }
}

Step 2: Create Index Template

Run the following command using Elasticsearch Kibana Dev Tools or curl. The template includes data stream support.

The field types in the template must exactly match the JSON structure sent by the log policy. Do not change field types.

PUT _index_template/apinizer-log-policy-capture-template
{
  "index_patterns": ["apinizer-log-policy-capture*"],
  "data_stream": {},
  "template": {
    "settings": {
      "index": {
        "lifecycle": {
          "name": "apinizer-log-policy-capture-ilm"
        },
        "number_of_shards": 1,
        "number_of_replicas": 0,
        "refresh_interval": "5s"
      }
    },
    "mappings": {
      "properties": {
        "@timestamp": {
          "type": "date",
          "format": "yyyy-MM-dd'T'HH:mm:ss.S'Z'||yyyy-MM-dd'T'HH:mm:ss.SS'Z'||yyyy-MM-dd'T'HH:mm:ss.SSS'Z'"
        },
        "aci": {
          "type": "keyword"
        },
        "ei": {
          "type": "keyword"
        },
        "api": {
          "type": "keyword"
        },
        "apn": {
          "type": "keyword"
        },
        "apmi": {
          "type": "keyword"
        },
        "apmn": {
          "type": "keyword"
        },
        "uok": {
          "type": "keyword",
          "ignore_above": 50
        },
        "sc": {
          "type": "short"
        },
        "rt": {
          "type": "keyword",
          "ignore_above": 7
        },
        "et": {
          "type": "keyword",
          "ignore_above": 75
        },
        "fcrh": {
          "type": "nested",
          "properties": {
            "k": {
              "type": "keyword"
            },
            "v": {
              "type": "keyword"
            }
          }
        },
        "fcrp": {
          "type": "nested",
          "properties": {
            "k": {
              "type": "keyword"
            },
            "v": {
              "type": "keyword"
            }
          }
        },
        "fcrb": {
          "type": "text"
        },
        "cr": {
          "type": "keyword"
        },
        "cl": {
          "type": "keyword"
        }
      }
    }
  }
}

Step 3: Create Data Stream

After the template is created, the data stream is automatically created when the first data arrives. To create manually:

PUT _data_stream/apinizer-log-policy-capture

Connector Configuration

Enter apinizer-log-policy-capture in the Elasticsearch connector’s Index Name field. This name must match the index_patterns in the template.

If you want to use a different index name, update the index_patterns field in the template accordingly. For example, for project-based separation you can use apinizer-log-policy-capture-projectname.

Database Integration

The Database connector writes each log policy capture as a single row into the log_PolicyCapture table on the target relational database. Supported database types are Oracle, MySQL/MariaDB, PostgreSQL, and SQL Server. For MongoDB, the collection is created automatically on first write — no manual setup is required.

For relational databases, the log_PolicyCapture table is not created automatically — you must create it manually before enabling the connector. See Apinizer Log Table Creation Commands for the CREATE TABLE statement, recommended indexes, and partitioning guidance for each supported database type.

The correlation_id column links request and response rows of the same call. When the log policy is placed at multiple pipeline stages (for example, FROM_CLIENT and TO_CLIENT), records can be joined on correlation_id to trace a transaction end to end. The index on this column is therefore strongly recommended.

For high-traffic APIs, we recommend configuring the log policy in Asynchronous mode so that database write latency does not block the request pipeline.

Privacy Settings

You can configure privacy settings at the policy level so that sensitive data is processed before being written to the log record.

Operation	Description
Mask	Partially hides the sensitive data (e.g. `****1234`)
Delete	Completely removes the sensitive data
Hash	Converts the sensitive data to a one-way hash value
Encrypt	Encrypts the sensitive data

Privacy settings are applied at two levels: first the policy-level settings, then the connector-level settings. If both levels are enabled, each is applied in sequence.

Masking is applied before data is sent to connectors. Connector-level privacy settings are applied separately and independently. This feature can be used to meet data protection requirements such as GDPR.

Usage Scenarios

Scenario	Description	Recommended Mode
Before/after transformation comparison	Add a log policy before and after a transformation policy to observe message changes	Asynchronous
Debugging	Add a temporary log point to the pipeline to inspect the input and output of a specific policy	Synchronous
Audit trail	Keep a full record of each request on sensitive APIs	Asynchronous
Multi-target logging	Send the same message to Elasticsearch and Kafka simultaneously	Asynchronous
Conditional logging	Log only requests matching a specific header value or path	Asynchronous

Deleting the Policy

For the steps to delete this policy and the actions to be taken when it is in use, see the What is a Policy? page.

Exporting/Importing the Policy

For the export steps and available options for this policy, see the What is a Policy? page.

Attaching the Policy to an API

For the process of attaching this policy to APIs, see the Adding Policy to Flow section on the Policy Management page.

API Development

Routing

Policies

Monitoring and Analytics

Global Settings

Test and Tools

Deployment and Version

Overview

Purpose

How It Works

Use Cases

Supported Targets

Protocol Support

Configuration Fields

General Settings

Connector Selection

Logged Fields

Body Logging Mode

Execution Modes

Data Structure

Example JSON Output

Pipeline Position (`cr`) Values

Attachment Scope (`cl`) Values

Elasticsearch Integration

Step 1: Create ILM Policy

Step 2: Create Index Template

Step 3: Create Data Stream

Connector Configuration

Database Integration

Privacy Settings

Usage Scenarios

Deleting the Policy

Exporting/Importing the Policy

Attaching the Policy to an API

Next Steps

Script Policy

API Call Policy

API Development

Routing

Policies

Monitoring and Analytics

Global Settings

Test and Tools

Deployment and Version

Documentation Index

​Overview

​Purpose

​How It Works

​Use Cases

​Supported Targets

​Protocol Support

​Configuration Fields

​General Settings

​Connector Selection

​Logged Fields

​Body Logging Mode

​Execution Modes

​Data Structure

​Example JSON Output

​Pipeline Position (cr) Values

​Attachment Scope (cl) Values

​Elasticsearch Integration

​Step 1: Create ILM Policy

​Step 2: Create Index Template

​Step 3: Create Data Stream

​Connector Configuration

​Database Integration

​Privacy Settings

​Usage Scenarios

​Related Pages

​Deleting the Policy

​Exporting/Importing the Policy

​Attaching the Policy to an API

​Next Steps

Script Policy

API Call Policy

Overview

Purpose

How It Works

Use Cases

Supported Targets

Protocol Support

Configuration Fields

General Settings

Connector Selection

Logged Fields

Body Logging Mode

Execution Modes

Data Structure

Example JSON Output

Pipeline Position (`cr`) Values

Attachment Scope (`cl`) Values

Elasticsearch Integration

Step 1: Create ILM Policy

Step 2: Create Index Template

Step 3: Create Data Stream

Connector Configuration

Database Integration

Privacy Settings

Usage Scenarios

Related Pages

Deleting the Policy

Exporting/Importing the Policy

Attaching the Policy to an API

Next Steps