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Version: 0.11

Quick Start

This guide will walk you through the process of quickly writing and querying logs.

You can write logs directly or use pipeline to write logs. Writing logs directly is simple but cannot split log text to structured data as the pipeline method does. The following sections will help you understand the differences between these two methods.

Write logs directly​

This is the simplest way to write logs to GreptimeDB.

Create a table​

First, create a table named origin_logs to store your logs. The FULLTEXT specification for the message column in the following SQL creates a full-text index to optimize queries.

CREATE TABLE `origin_logs` (
  `message` STRING FULLTEXT,
  `time` TIMESTAMP TIME INDEX
) WITH (
  append_mode = 'true'
);

Insert logs​

Write logs using the SQL protocol​

Use the INSERT statement to insert logs into the table.

INSERT INTO origin_logs (message, time) VALUES
('127.0.0.1 - - [25/May/2024:20:16:37 +0000] "GET /index.html HTTP/1.1" 200 612 "-" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"', '2024-05-25 20:16:37.217'),
('192.168.1.1 - - [25/May/2024:20:17:37 +0000] "POST /api/login HTTP/1.1" 200 1784 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.96 Safari/537.36"', '2024-05-25 20:17:37.217'),
('10.0.0.1 - - [25/May/2024:20:18:37 +0000] "GET /images/logo.png HTTP/1.1" 304 0 "-" "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:89.0) Gecko/20100101 Firefox/89.0"', '2024-05-25 20:18:37.217'),
('172.16.0.1 - - [25/May/2024:20:19:37 +0000] "GET /contact HTTP/1.1" 404 162 "-" "Mozilla/5.0 (iPhone; CPU iPhone OS 14_0 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0 Mobile/15E148 Safari/604.1"', '2024-05-25 20:19:37.217');

The above SQL inserts the entire log text into a single column, and you must add an extra timestamp for each log.

Write logs using the gRPC protocol​

You can also write logs using the gRPC protocol, which is a more efficient method.

Refer to Write Data Using gRPC to learn how to write logs using the gRPC protocol.

Write logs by Pipeline​

Using a pipeline allows you to automatically parse and transform the log message into multiple columns, as well as create tables automatically.

Write JSON logs using the built-in Pipeline​

GreptimeDB offers a built-in pipeline, greptime_identity, for handling JSON log formats. This pipeline simplifies the process of writing JSON logs.

curl -X "POST" "http://localhost:4000/v1/events/logs?db=public&table=pipeline_logs&pipeline_name=greptime_identity" \
     -H 'Content-Type: application/json' \
     -d $'[
    {"name": "Alice", "age": 20, "is_student": true, "score": 90.5,"object": {"a":1,"b":2}},
    {"age": 21, "is_student": false, "score": 85.5, "company": "A" ,"whatever": null},
    {"name": "Charlie", "age": 22, "is_student": true, "score": 95.5,"array":[1,2,3]}
]'
  • pipeline_name=greptime_identity specifies the built-in pipeline.
  • table=pipeline_logs specifies the target table. If the table does not exist, it will be created automatically. The greptime_identity pipeline will automatically create columns for each field in the JSON log. A successful command execution will return:
{"output":[{"affectedrows":3}],"execution_time_ms":9}

For more details about the greptime_identity pipeline, please refer to the Manage Pipelines document.

Write logs using a custom Pipeline​

If your logs follow a specific pattern, you can create a custom pipeline to parse and transform the log messages into multiple columns, and automatically create tables.

Create a Pipeline​

GreptimeDB provides a dedicated HTTP interface for creating pipelines. Here's how to do it:

First, create a pipeline file, for example, pipeline.yaml.

processors:
  - dissect:
      fields:
        - message
      patterns:
        - '%{ip_address} - - [%{timestamp}] "%{http_method} %{request_line}" %{status_code} %{response_size} "-" "%{user_agent}"'
      ignore_missing: true
  - date:
      fields:
        - timestamp
      formats:
        - "%d/%b/%Y:%H:%M:%S %z"

transform:
  - fields:
      - ip_address
      - http_method
    type: string
    index: tag
  - fields:
      - status_code
    type: int32
    index: tag
  - fields:
      - request_line
      - user_agent
    type: string
    index: fulltext
  - fields:
      - response_size
    type: int32
  - fields:
      - timestamp
    type: time
    index: timestamp

The pipeline splits the message field using the specified pattern to extract the ip_address, timestamp, http_method, request_line, status_code, response_size, and user_agent. It then parses the timestamp field using the format %d/%b/%Y:%H:%M:%S %z to convert it into a proper timestamp format that the database can understand. Finally, it converts each field to the appropriate data type and indexes it accordingly. It is worth noting that the request_line and user_agent fields are indexed as fulltext to optimize full-text search queries. And there must be one time index column specified by the timestamp.

Execute the following command to upload the configuration file:

curl -X "POST" "http://localhost:4000/v1/events/pipelines/nginx_pipeline" -F "[email protected]"

After successfully executing this command, a pipeline named nginx_pipeline will be created, and the result will be returned as:

{"name":"nginx_pipeline","version":"2024-06-27 12:02:34.257312110Z"}.

You can create multiple versions for the same pipeline name. All pipelines are stored at the greptime_private.pipelines table. Please refer to Query Pipelines to view the pipeline data in the table.

Write logs​

The following example writes logs to the pipeline_logs table and uses the nginx_pipeline pipeline to format and transform the log messages.

curl -X "POST" "http://localhost:4000/v1/events/logs?db=public&table=pipeline_logs&pipeline_name=nginx_pipeline" \
     -H 'Content-Type: application/json' \
     -d $'[
{"message":"127.0.0.1 - - [25/May/2024:20:16:37 +0000] \\"GET /index.html HTTP/1.1\\" 200 612 \\"-\\" \\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36\\""},
{"message":"192.168.1.1 - - [25/May/2024:20:17:37 +0000] \\"POST /api/login HTTP/1.1\\" 200 1784 \\"-\\" \\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.96 Safari/537.36\\""},
{"message":"10.0.0.1 - - [25/May/2024:20:18:37 +0000] \\"GET /images/logo.png HTTP/1.1\\" 304 0 \\"-\\" \\"Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:89.0) Gecko/20100101 Firefox/89.0\\""},
{"message":"172.16.0.1 - - [25/May/2024:20:19:37 +0000] \\"GET /contact HTTP/1.1\\" 404 162 \\"-\\" \\"Mozilla/5.0 (iPhone; CPU iPhone OS 14_0 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0 Mobile/15E148 Safari/604.1\\""}
]'

You will see the following output if the command is successful:

{"output":[{"affectedrows":4}],"execution_time_ms":79}

Differences between writing logs directly and using a pipeline​

In the above examples, the table origin_logs is created by writing logs directly, and the table pipeline_logs is automatically created by writing logs using pipeline. Let's explore the differences between these two tables.

DESC origin_logs;
+---------+----------------------+------+------+---------+---------------+
| Column  | Type                 | Key  | Null | Default | Semantic Type |
+---------+----------------------+------+------+---------+---------------+
| message | String               |      | YES  |         | FIELD         |
| time    | TimestampMillisecond | PRI  | NO   |         | TIMESTAMP     |
+---------+----------------------+------+------+---------+---------------+
DESC pipeline_logs;
+---------------+---------------------+------+------+---------+---------------+
| Column        | Type                | Key  | Null | Default | Semantic Type |
+---------------+---------------------+------+------+---------+---------------+
| ip_address    | String              | PRI  | YES  |         | TAG           |
| http_method   | String              | PRI  | YES  |         | TAG           |
| status_code   | Int32               | PRI  | YES  |         | TAG           |
| request_line  | String              |      | YES  |         | FIELD         |
| user_agent    | String              |      | YES  |         | FIELD         |
| response_size | Int32               |      | YES  |         | FIELD         |
| timestamp     | TimestampNanosecond | PRI  | NO   |         | TIMESTAMP     |
+---------------+---------------------+------+------+---------+---------------+
7 rows in set (0.00 sec)

From the table structure, you can see that the origin_logs table has only two columns, with the entire log message stored in a single column. The pipeline_logs table stores the log message in multiple columns.

It is recommended to use the pipeline method to split the log message into multiple columns, which offers the advantage of explicitly querying specific values within certain columns. Tag matching query proves superior to full-text searching for several key reasons:

  • Performance Efficiency: Tag matching query is typically faster than full-text searching.
  • Resource Consumption: Due to GreptimeDB's columnar storage engine, structured data is more conducive to compression. Additionally, the inverted index used for tag matching query typically consumes significantly fewer resources than a full-text index, especially in terms of storage size.
  • Maintainability: Tag matching query is straightforward and easier to understand, write, and debug.

Of course, if you need keyword searching within large text blocks, you must use full-text searching as it is specifically designed for that purpose.

Query logs​

We use the pipeline_logs table as an example to query logs.

Query logs by tags​

With the multiple tag columns in pipeline_logs, you can query data by tags flexibly. For example, query the logs with status_code 200 and http_method GET.

SELECT * FROM pipeline_logs WHERE status_code = 200 AND http_method = 'GET';
+------------+-------------+-------------+----------------------+---------------------------------------------------------------------------------------------------------------------+---------------+---------------------+
| ip_address | http_method | status_code | request_line         | user_agent                                                                                                          | response_size | timestamp           |
+------------+-------------+-------------+----------------------+---------------------------------------------------------------------------------------------------------------------+---------------+---------------------+
| 127.0.0.1  | GET         |         200 | /index.html HTTP/1.1 | Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36 |           612 | 2024-05-25 20:16:37 |
+------------+-------------+-------------+----------------------+---------------------------------------------------------------------------------------------------------------------+---------------+---------------------+
1 row in set (0.02 sec)

Full-Text Search​

For the text fields request_line and user_agent, you can use the MATCHES function to search logs. Remember, we created the full-text index for these two columns when creating a pipeline.
This allows for high-performance full-text searches.

For example, query the logs with request_line containing /index.html or /api/login.

SELECT * FROM pipeline_logs WHERE MATCHES(request_line, 'index.html /api/login');
+-------------+-------------+-------------+----------------------+--------------------------------------------------------------------------------------------------------------------------+---------------+---------------------+
| ip_address  | http_method | status_code | request_line         | user_agent                                                                                                               | response_size | timestamp           |
+-------------+-------------+-------------+----------------------+--------------------------------------------------------------------------------------------------------------------------+---------------+---------------------+
| 127.0.0.1   | GET         |         200 | /index.html HTTP/1.1 | Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36      |           612 | 2024-05-25 20:16:37 |
| 192.168.1.1 | POST        |         200 | /api/login HTTP/1.1  | Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.96 Safari/537.36 |          1784 | 2024-05-25 20:17:37 |
+-------------+-------------+-------------+----------------------+--------------------------------------------------------------------------------------------------------------------------+---------------+---------------------+
2 rows in set (0.00 sec)

You can refer to the Full-Text Search document for detailed usage of the MATCHES function.

Next steps​

You have now experienced GreptimeDB's logging capabilities. You can explore further by following the documentation below:

  • Pipeline Configuration: Provides in-depth information on each specific configuration of pipelines in GreptimeDB.
  • Managing Pipelines: Explains how to create and delete pipelines.
  • Writing Logs with Pipelines: Provides detailed instructions on efficiently writing log data by leveraging the pipeline mechanism.
  • Query Logs: Describes how to query logs using the GreptimeDB SQL interface.