Bytehouse (via Collector method) - v3.0.0

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About Collectors

Collector Method

Pre-requisites

Collector Server Minimum Requirements

ByteHouse Requirements

Access to the following tables
1. system.databases
2. system.tables
3. system.columns

Step 1: Enabling logging

TBC

Step 2: Create the Source in K

Create an MySQL source in K

Go to Settings, Select Sources and click Add Source
Select “Load from File system” option
Give the source a Name - e.g. MySQLProduction
Add the Host name for the MySQL Server
Click Finish Setup

Step 3: Getting Access to the Source Landing Directory

Collector Method

Step 4: Install the Collector

It is recommended to use a python environment such as pyenv or pipenv if you are not intending to install this package at the system level.

Some python packages also have dependencies on the OS level packages, so you may be required to install additional OS packages if the below fails to install.

You can download the latest Core Library and whl via Platform Settings → Sources → Download Collectors

Run the following command to install the collector

pip install kada_collectors_extractors_<version>-none-any.whl

You will also need to install the common library kada_collectors_lib for this collector to function properly.

pip install kada_collectors_lib-<version>-none-any.whl

Step 5: Configure the Collector

The collector requires a set of parameters to connect to and extract metadata from MySQL.

The ByteHouse collector only extracts metadata and does not extract or process query usage on the database.

FIELD	FIELD TYPE	DESCRIPTION	EXAMPLE

FIELD	FIELD TYPE	DESCRIPTION	EXAMPLE
api_key	string	The API Key for ByteHouse, you can generate one via the Console	“xasdaxcv”
server	string	ByteHouse gateway, these are regionally specific https://docs.byteplus.com/en/docs/bytehouse/docs-supported-regions-and-providers	“gateway.aws-ap-southeast-1.bytehouse.cloud”
port	integer	The port to connect to the ClickHouse instance, generally this is 19000	19000
host	string	The onboarded host in K for the ClickHouse Source, this is generally the gateway address but we suggest you add a differentiator incase you have multiple ByteHouse accounts.	“gateway.aws-ap-southeast-1.bytehouse.cloud”
tenant_account_id	string	This value can be found in the ByteHouse console under the Tenant Management Tab and Basic Information. This is NOT the Login Account Id	“123456778”
meta_only	boolean	Currently we only support meta only as true	true
output_path	string	Absolute path to the output location where files are to be written	“/tmp/output”
mask	boolean	To enable masking or not	true
compress	boolean	To enable compression or not to .csv.gz	true
timeout	boolean	Timeout setting for sending and receiving data in seconds, this is normally defaulted as 80000	80000

These parameters can be added directly into the run or you can use pass the parameters in via a JSON file. The following is an example you can use that is included in the example run code below.

kada_bytehouse_extractor_config.json

{
    "api_key": "",
    "server": "",
    "port": 19000,
    "tenant_account_id": "",
    "host": "",
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true,
    "meta_only": true,
    "timeout": 80000
}

Step 6: Run the Collector

The following code is an example of how to run the extractor. You may need to uplift this code to meet any code standards at your organisation.

This can be executed in any python environment where the whl has been installed. It will produce and read a high water mark file from the same directory as the execution called bytehouse_hwm.txt and produce files according to the configuration JSON.

This is the wrapper script: kada_bytehouse_extractor.py

Advance options:

If you wish to maintain your own high water mark files elsewhere you can use the above section’s script as a guide on how to call the extractor. The configuration file is simply the keyword arguments in JSON format. Refer to this document for more information Collector Integration General Notes | Storing HWM in another location

If you are handling external arguments of the runner yourself, you’ll need to consider additional items for the run method. Refer to this document for more information Collector Integration General Notes | The run method

api_key: api key to sign into Bytehouse server
server: Bytehouse server host or address for the connection
port: Bytehouse server port for the connection, default is 8443
tenant_account_id: The Bytehouse account ID, this can be found in the console.
host: The Bytehouse host or address name, this should be the name you onboarded or will onboard into K with, generally this is the same as the connection server.
sql: The list of SQL queries that will be executed by the program
output_path: full or relative path to where the outputs should go
mask: To mask the META/DATABASE_LOG files or not
compress: To gzip output files or not
meta_only: To extract metadata only
timeout: The timeout for the send/recieve connection default is 80000

Step 7: Check the Collector Outputs

K Extracts

A set of files (eg metadata, databaselog, linkages, events etc) will be generated. These files will appear in the output_path directory you set in the configuration details

High Water Mark File

A high water mark file is created in the same directory as the execution called bytehouse_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.

If you want prefer file managed hwm, you can edit the location of the hwn by following these instructions Collector Integration General Notes

Step 8: Push the Extracts to K

Once the files have been validated, you can push the files to the K landing directory.

You can use Azure Storage Explorer if you want to initially do this manually. You can push the files using python as well (see Airflow example below)

Example: Using Airflow to orchestrate the Extract and Push to K

Collector Method