mysql.md

MySQL

Prerequisites

Try Out Basic SQL Operations

Connect

mysql -h 127.0.0.1 -P 4002

Also, you can use PostgreSQL to connect the database:

psql -h 127.0.0.1 -p 4003 -d public

Create table

Note: GreptimeDB offers a schemaless approach to writing data that eliminates the need to manually create tables using additional protocols. See Automatic Schema Generation.

Now we create a table via MySQL. Let's start by creating the system_metrics table which contains system resource metrics, including CPU/memory/disk usage. The data is scraped every 5 seconds.

CREATE TABLE IF NOT EXISTS system_metrics (
    host STRING,
    idc STRING,
    cpu_util DOUBLE,
    memory_util DOUBLE,
    disk_util DOUBLE,
    ts TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    PRIMARY KEY(host, idc),
    TIME INDEX(ts)
);

Field descriptions:

Field	Type	Description
host	string	The hostname
idc	string	The idc name where the host belongs to
cpu_util	double	The percent use of CPU
memory_util	double	The percent use of memory
disk_util	double	The percent use of disks
ts	timestamp	Timestamp column incrementing

• The table can be created automatically if you are using other protocols. See Create Table.
• For more information about creating table SQL, please refer to CREATE.
• For data types, please check data types.

Insert data

Using the INSERT statement is an easy way to add data to your table. The following statement allows us to insert several rows into the system_metrics table.

INSERT INTO system_metrics
VALUES
    ("host1", "idc_a", 11.8, 10.3, 10.3, 1667446797450),
    ("host1", "idc_a", 80.1, 70.3, 90.0, 1667446797550),
    ("host1", "idc_b", 50.0, 66.7, 40.6, 1667446797650),
    ("host1", "idc_b", 51.0, 66.5, 39.6, 1667446797750),
    ("host1", "idc_b", 52.0, 66.9, 70.6, 1667446797850),
    ("host1", "idc_b", 53.0, 63.0, 50.6, 1667446797950),
    ("host1", "idc_b", 78.0, 66.7, 20.6, 1667446798050),
    ("host1", "idc_b", 68.0, 63.9, 50.6, 1667446798150),
    ("host1", "idc_b", 90.0, 39.9, 60.6, 1667446798250);

For more information about the INSERT statement, please refer to INSERT.

Query data

To select all the data from the system_metrics table, use the SELECT statement:

SELECT * FROM system_metrics;

The query result looks like the following:

+-------+-------+----------+-------------+-----------+---------------------+
| host  | idc   | cpu_util | memory_util | disk_util | ts                  |
+-------+-------+----------+-------------+-----------+---------------------+
| host1 | idc_a |     11.8 |        10.3 |      10.3 | 2022-11-03 03:39:57 |
| host1 | idc_a |     80.1 |        70.3 |        90 | 2022-11-03 03:39:57 |
| host1 | idc_b |       50 |        66.7 |      40.6 | 2022-11-03 03:39:57 |
| host1 | idc_b |       51 |        66.5 |      39.6 | 2022-11-03 03:39:57 |
| host1 | idc_b |       52 |        66.9 |      70.6 | 2022-11-03 03:39:57 |
| host1 | idc_b |       53 |          63 |      50.6 | 2022-11-03 03:39:57 |
| host1 | idc_b |       78 |        66.7 |      20.6 | 2022-11-03 03:39:58 |
| host1 | idc_b |       68 |        63.9 |      50.6 | 2022-11-03 03:39:58 |
| host1 | idc_b |       90 |        39.9 |      60.6 | 2022-11-03 03:39:58 |
+-------+-------+----------+-------------+-----------+---------------------+
9 rows in set (0.00 sec)

You can use the count() function to get the number of all rows in the table:

SELECT count(*) FROM system_metrics;

+-----------------+
| COUNT(UInt8(1)) |
+-----------------+
|               9 |
+-----------------+

The avg() function returns the average value of a certain field:

SELECT avg(cpu_util) FROM system_metrics;

+------------------------------+
| AVG(system_metrics.cpu_util) |
+------------------------------+
|            59.32222222222222 |
+------------------------------+

You can use the GROUP BY clause to group rows that have the same values into summary rows. The average memory usage grouped by idc:

SELECT idc, avg(memory_util) FROM system_metrics GROUP BY idc;

+-------+---------------------------------+
| idc   | AVG(system_metrics.memory_util) |
+-------+---------------------------------+
| idc_a |                            40.3 |
| idc_b |              61.942857142857136 |
+-------+---------------------------------+
2 rows in set (0.03 sec)

For more information about the SELECT statement, please refer to SELECT.

Collect Host Metrics

If you have started GreptimeDB using the Prerequisites section, you can use the following command to write data:

curl -L \
   https://raw.githubusercontent.com/GreptimeCloudStarters/quick-start-mysql/main/quick-start.sh |\
   bash -s -- -h 127.0.0.1 -d public -s DISABLED -P 4002

Visualize data

GreptimeDB Dashboard

GreptimeDB provides a user-friendly dashboard to assist users in exploring data. Once GreptimeDB is started as mentioned in the Prerequisites section, you can access the dashboard through the HTTP endpoint http://localhost:4000/dashboard.

Write SQL into the command text, then click Run All. We'll got all data in system_metrics table.

SELECT * FROM system_metrics;

Grafana

Add Data Source

You can access Grafana at http://localhost:3000. Use admin as both the username and password to log in.

GreptimeDB can be configured as a MySQL data source in Grafana. Click the Add data source button and select MySQL as the type.

Fill in the following information:

• Name: GreptimeDB
• Host: greptimedb:4002. The host greptimedb is the name of GreptimeDB container
• Database: public
• SessionTimezone: UTC

Click Save & Test button to test the connection.

For more information on using MySQL as a data source for GreptimeDB, please refer to Grafana-MySQL.

Create a Dashboard

To create a new dashboard in Grafana, click the Create your first dashboard button on the home page. Then, click Add visualization and select GreptimeDB as the data source.

In the Query section, ensure that you select GreptimeDB as the data source, choose Time series as the format, switch to the Code tab, and write the following SQL statement. Note that we are using ts as the time column.

SELECT ts AS "time", idle_cpu, sys_cpu FROM public.monitor

Click Run query to view the metric data.

Next Steps