# Import and Export to Word On-Premises logs

The logs from Import and Export to Word On-Premises are written to `stdout` and `stderr`. Most of them are formatted in JSON. They can be used for monitoring or debugging purposes. In production environments, we recommend storing the logs to files or using a distributed logging system (like ELK or CloudWatch).

<a id="monitoring-import-and-export-to-word-with-logs">

## Monitoring Import and Export to Word with logs

To get more insight into how the Import and Export to Word On-Premises is performing, we built logs that can be used for monitoring. To enable these, just add the `ENABLE_METRIC_LOGS=true` environment variable.

<a id="log-structure">

### Log structure

The log structure contains the following information:

* `handler` – A unified identifier of action. Use this field to identify calls.
* `traceId` – A unique RPC call ID.
* `tags` – A semicolon-separated list of tags. Use this field to filter metrics logs.
* `data` – An object containing additional information. It might vary between different transports.
* `data.duration` – The request duration in milliseconds.
* `data.transport` – The type of the request transport. It could be `http` or `ws` (websocket).
* `data.status` – The request status. It can be equal to `success`, `fail`, `warning`.
* `data.statusCode` – The response status in HTTP status code standard.

Additionally, for the HTTP transport, the following information is included:

* `data.url` – The URL path.
* `data.method` – The request method.

In case of an error, `data.status` will be equal to `failed` and `data.message` will contain the error message.

An example log for HTTP transport:

```
{
  "level": 30,
  "time": "2021-03-09T11:15:09.154Z",
  "msg": "Request summary",
  "handler": "postConvert",
  "traceId": "85f13d92-57df-4b3b-98bb-0ca41a5ae601",
  "data": {
    "duration": 752,
    "transport": "http",
    "statusCode": 200,
    "status": "success",
    "url": "/v2/convert/html-docx",
    "method": "POST"
  },
  "tags": "metrics"
}
```

> **Note**
>
> See [example charts](../cs-onpremises/request-summary.md#example-charts) to check how to use logs for monitoring purposes

<a id="docker">

## Docker

The docker has built-in logging mechanisms that capture logs from the output of the containers. The default logging driver writes the logs to files.

When using this driver, you can use the `docker logs` command to show logs from a container. You can add the `-f` flag to view logs in real time. Refer to the [official Docker documentation](https://docs.docker.com/engine/reference/commandline/logs/) for more information about the `logs` command.

> **Note**
>
> When a container is running for a long period of time, the logs can take up a lot of space. To avoid this problem, you should make sure that the log rotation is enabled. This can be set with the `max-size` option.

<a id="distributed-logging">

## Distributed logging

If you are running more than one instance of the Import and Export to Word On-Premises, we recommend using a distributed logging system. It allows you to view and analyze logs from all instances in one place.

<a id="aws-cloudwatch-and-other-cloud-solutions">

### AWS CloudWatch and other cloud solutions

If you are running Import and Export to Word On-Premises in the cloud, the simplest and recommended way is to use a service which is available at the selected provider. Here are some of the available services:

* AWS – [CloudWatch](https://aws.amazon.com/CloudWatch)
* Google Cloud – [Cloud Logging](https://cloud.google.com/logging)
* Azure – [Azure Monitor](https://azure.microsoft.com/en-us/services/monitor/)

To use CloudWatch with AWS ECS, you have to create a log group before and change the log driver to `awslogs`. When the log driver is configured properly, logs will be streamed directly to CloudWatch.

The `logConfiguration` may look similar to this:

```json
"logConfiguration": {
  "logDriver": "awslogs",
  "options": {
	"awslogs-region": "us-west-2",
	"awslogs-group": "cksource",
	"awslogs-stream-prefix": "ck-docx-converter-logs"
  }
}
```

Refer to the [Using the awslogs Log Driver](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/using_awslogs.html) article for more information.

<a id="on-premises-solutions">

### On-Premises solutions

If you are using your own infrastructure or for some reason cannot use the service offered by your provider, you can always use some on-premises distributed logging system.

There are a lot of solutions available, including:

* [ELK](https://www.elastic.co/what-is/elk-stack) + [Filebeat](https://www.elastic.co/guide/en/beats/filebeat/current/filebeat-getting-started.html)\
  This is a stack built on top of Elasticsearch, Logstash and Kibana. In this configuration, Elasticsearch stores logs, Filebeat reads logs from Docker and sends them to Elasticsearch, and Kibana is used to view them. Logstash is not necessary because logs are already structured.

* [Fluentd](https://www.fluentd.org/)\
  It uses a dedicated [Docker log driver](https://docs.docker.com/config/containers/logging/fluentd) to send the logs. It has a built-in frontend, but can also be integrated with Elasticsearch and Kibana for a better filtering.

* [Graylog](https://www.graylog.org)\
  It uses a dedicated [Docker log driver](https://docs.docker.com/config/containers/logging/gelf) to send the logs. It has a built-in frontend and needs Elasticsearch to store the logs as well as a MongoDB database to store the configuration.

<a id="example-configuration">

#### Example configuration

The example configuration uses Fluentd, Elasticsearch and Kibana to capture logs from Docker.

Before running Import and Export to Word On-Premises, you have to prepare the logging services. For the purposes of this example, Docker Compose is used. Create the `fluentd`, `elasticsearch` and `kibana` services inside the `docker-compose.yml` file:

```yaml
version: '3.7'
services:
  fluentd:
	build: ./fluentd
	volumes:
	  - ./fluentd/fluent.conf:/fluentd/etc/fluent.conf
	ports:
	  - "24224:24224"
	  - "24224:24224/udp"

  elasticsearch:
	image: docker.elastic.co/elasticsearch/elasticsearch:6.8.5
	expose:
	  - 9200
	ports:
	  - "9200:9200"

  kibana:
	image: docker.elastic.co/kibana/kibana:6.8.5
	environment:
	  ELASTICSEARCH_HOSTS: "http://elasticsearch:9200"
	ports:
	  - "5601:5601"
```

To integrate Fluentd with Elasticsearch, you first need to install `fluent-plugin-elasticsearch` in the Fluentd image. To do this, create a `fluentd/Dockerfile` with the following content:

```kerfile
FROM fluent/fluentd:v1.10-1

USER root

RUN apk add --no-cache --update build-base ruby-dev \
    && gem install fluent-plugin-elasticsearch \
    && gem sources --clear-all
```

Next, configure the input server and connection to Elasticsearch in the `fluentd/fluent.conf` file:

```xml
<source>
  @type forward
  port 24224
  bind 0.0.0.0
</source>
<match *.**>
  @type copy
  <store>
	@type elasticsearch
	host elasticsearch
	port 9200
	logstash_format true
	logstash_prefix fluentd
	logstash_dateformat %Y%m%d
	include_tag_key true
	type_name access_log
	tag_key @log_name
	flush_interval 1s
  </store>
  <store>
	@type stdout
  </store>
</match>
```

Now you are ready to run the services:

```bash
docker-compose up --build
```

When the services are ready, you can finally start the Import and Export to Word On-Premises.

```bash
docker run --init -p 8080:8080 \
--log-driver=fluentd \
--log-opt fluentd-address=[Fluentd address]:24224 \
[Your config here] \
docker.cke-cs.com/docx-converter:[version]
```

Now open Kibana in your browser. It is available at <http://localhost:5601/>. In the first run, you may be asked about creating an index. Use the `fluentd-*` pattern and press the “Create” button. After this step, your logs should appear in the “Discover” tab.

---

Full index of the Cloud Services documentation: [llms.txt](../../../llms.txt)