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ClickHouse

There are 2 sources that provide integration with ClickHouse

Source ModuleDocumentation

clickhouse

This plugin extracts the following:

  • Metadata for tables, views, materialized views and dictionaries
  • Column types associated with each table(except *AggregateFunction and DateTime with timezone)
  • Table, row, and column statistics via optional SQL profiling.
  • Table, view, materialized view and dictionary(with CLICKHOUSE source_type) lineage
tip

You can also get fine-grained usage statistics for ClickHouse using the clickhouse-usage source described below.

Read more...

clickhouse-usage

This plugin has the below functionalities -

  1. For a specific dataset this plugin ingests the following statistics -
    1. top n queries.
    2. top users.
    3. usage of each column in the dataset.
  2. Aggregation of these statistics into buckets, by day or hour granularity.

Usage information is computed by querying the system.query_log table. In case you have a cluster or need to apply additional transformation/filters you can create a view and put to the query_log_table setting.

note

This source only does usage statistics. To get the tables, views, and schemas in your ClickHouse warehouse, ingest using the clickhouse source described above.

Read more...

Module clickhouse

Certified

Important Capabilities

CapabilityStatusNotes
Data ProfilingOptionally enabled via configuration
Detect Deleted EntitiesEnabled via stateful ingestion

This plugin extracts the following:

  • Metadata for tables, views, materialized views and dictionaries
  • Column types associated with each table(except *AggregateFunction and DateTime with timezone)
  • Table, row, and column statistics via optional SQL profiling.
  • Table, view, materialized view and dictionary(with CLICKHOUSE source_type) lineage
tip

You can also get fine-grained usage statistics for ClickHouse using the clickhouse-usage source described below.

CLI based Ingestion

Install the Plugin

pip install 'acryl-datahub[clickhouse]'

Starter Recipe

Check out the following recipe to get started with ingestion! See below for full configuration options.

For general pointers on writing and running a recipe, see our main recipe guide.

source:
type: clickhouse
config:
# Coordinates
host_port: localhost:9000

# Credentials
username: user
password: pass

# Options
platform_instance: DatabaseNameToBeIngested

include_views: True # whether to include views, defaults to True
include_tables: True # whether to include views, defaults to True

sink:
# sink configs

#---------------------------------------------------------------------------
# For the HTTP interface:
#---------------------------------------------------------------------------
source:
type: clickhouse
config:
host_port: localhost:8443
protocol: https

#---------------------------------------------------------------------------
# For the Native interface:
#---------------------------------------------------------------------------

source:
type: clickhouse
config:
host_port: localhost:9440
scheme: clickhouse+native
secure: True

Config Details

Note that a . is used to denote nested fields in the YAML recipe.

FieldDescription
bucket_duration 
Enum
Size of the time window to aggregate usage stats.
Default: DAY
database 
string
database (catalog)
database_alias 
string
[Deprecated] Alias to apply to database when ingesting.
end_time 
string(date-time)
Latest date of usage to consider. Default: Current time in UTC
host_port 
string
ClickHouse host URL.
Default: localhost:8123
include_materialized_views 
boolean
Default: True
include_table_lineage 
boolean
Whether table lineage should be ingested.
Default: True
include_table_location_lineage 
boolean
If the source supports it, include table lineage to the underlying storage location.
Default: True
include_tables 
boolean
Whether tables should be ingested.
Default: True
include_views 
boolean
Whether views should be ingested.
Default: True
options 
object
Any options specified here will be passed to SQLAlchemy.create_engine as kwargs.
password 
string(password)
password
Default:
platform_instance 
string
The instance of the platform that all assets produced by this recipe belong to
platform_instance_map 
map(str,string)
protocol 
string
secure 
boolean
sqlalchemy_uri 
string
URI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.
start_time 
string(date-time)
Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)
username 
string
username
env 
string
The environment that all assets produced by this connector belong to
Default: PROD
domain 
map(str,AllowDenyPattern)
A class to store allow deny regexes
domain.key.allow 
array(string)
domain.key.deny 
array(string)
domain.key.ignoreCase 
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profile_pattern 
AllowDenyPattern
Regex patterns to filter tables (or specific columns) for profiling during ingestion. Note that only tables allowed by the table_pattern will be considered.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
profile_pattern.allow 
array(string)
profile_pattern.deny 
array(string)
profile_pattern.ignoreCase 
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
schema_pattern 
AllowDenyPattern
Regex patterns for schemas to filter in ingestion. Specify regex to only match the schema name. e.g. to match all tables in schema analytics, use the regex 'analytics'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
schema_pattern.allow 
array(string)
schema_pattern.deny 
array(string)
schema_pattern.ignoreCase 
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
table_pattern 
AllowDenyPattern
Regex patterns for tables to filter in ingestion. Specify regex to match the entire table name in database.schema.table format. e.g. to match all tables starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.allow 
array(string)
table_pattern.deny 
array(string)
table_pattern.ignoreCase 
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
view_pattern 
AllowDenyPattern
Regex patterns for views to filter in ingestion. Note: Defaults to table_pattern if not specified. Specify regex to match the entire view name in database.schema.view format. e.g. to match all views starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
view_pattern.allow 
array(string)
view_pattern.deny 
array(string)
view_pattern.ignoreCase 
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profiling 
GEProfilingConfig
Default: {'enabled': False, 'limit': None, 'offset': None, ...
profiling.catch_exceptions 
boolean
Default: True
profiling.enabled 
boolean
Whether profiling should be done.
Default: False
profiling.field_sample_values_limit 
integer
Upper limit for number of sample values to collect for all columns.
Default: 20
profiling.include_field_distinct_count 
boolean
Whether to profile for the number of distinct values for each column.
Default: True
profiling.include_field_distinct_value_frequencies 
boolean
Whether to profile for distinct value frequencies.
Default: False
profiling.include_field_histogram 
boolean
Whether to profile for the histogram for numeric fields.
Default: False
profiling.include_field_max_value 
boolean
Whether to profile for the max value of numeric columns.
Default: True
profiling.include_field_mean_value 
boolean
Whether to profile for the mean value of numeric columns.
Default: True
profiling.include_field_median_value 
boolean
Whether to profile for the median value of numeric columns.
Default: True
profiling.include_field_min_value 
boolean
Whether to profile for the min value of numeric columns.
Default: True
profiling.include_field_null_count 
boolean
Whether to profile for the number of nulls for each column.
Default: True
profiling.include_field_quantiles 
boolean
Whether to profile for the quantiles of numeric columns.
Default: False
profiling.include_field_sample_values 
boolean
Whether to profile for the sample values for all columns.
Default: True
profiling.include_field_stddev_value 
boolean
Whether to profile for the standard deviation of numeric columns.
Default: True
profiling.limit 
integer
Max number of documents to profile. By default, profiles all documents.
profiling.max_number_of_fields_to_profile 
integer
A positive integer that specifies the maximum number of columns to profile for any table. None implies all columns. The cost of profiling goes up significantly as the number of columns to profile goes up.
profiling.max_workers 
integer
Number of worker threads to use for profiling. Set to 1 to disable.
Default: 20
profiling.offset 
integer
Offset in documents to profile. By default, uses no offset.
profiling.partition_datetime 
string(date-time)
For partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this.
profiling.partition_profiling_enabled 
boolean
Default: True
profiling.profile_if_updated_since_days 
number
Profile table only if it has been updated since these many number of days. If set to null, no constraint of last modified time for tables to profile. Supported only in snowflake and BigQuery.
profiling.profile_table_level_only 
boolean
Whether to perform profiling at table-level only, or include column-level profiling as well.
Default: False
profiling.profile_table_row_count_estimate_only 
boolean
Use an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres.
Default: False
profiling.profile_table_row_limit 
integer
Profile tables only if their row count is less then specified count. If set to null, no limit on the row count of tables to profile. Supported only in snowflake and BigQuery
Default: 5000000
profiling.profile_table_size_limit 
integer
Profile tables only if their size is less then specified GBs. If set to null, no limit on the size of tables to profile. Supported only in snowflake and BigQuery
Default: 5
profiling.query_combiner_enabled 
boolean
This feature is still experimental and can be disabled if it causes issues. Reduces the total number of queries issued and speeds up profiling by dynamically combining SQL queries where possible.
Default: True
profiling.report_dropped_profiles 
boolean
Whether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.
Default: False
profiling.turn_off_expensive_profiling_metrics 
boolean
Whether to turn off expensive profiling or not. This turns off profiling for quantiles, distinct_value_frequencies, histogram & sample_values. This also limits maximum number of fields being profiled to 10.
Default: False
stateful_ingestion 
StatefulStaleMetadataRemovalConfig
Base specialized config for Stateful Ingestion with stale metadata removal capability.
stateful_ingestion.enabled 
boolean
The type of the ingestion state provider registered with datahub.
Default: False
stateful_ingestion.ignore_new_state 
boolean
If set to True, ignores the current checkpoint state.
Default: False
stateful_ingestion.ignore_old_state 
boolean
If set to True, ignores the previous checkpoint state.
Default: False
stateful_ingestion.remove_stale_metadata 
boolean
Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.
Default: True

Code Coordinates

  • Class Name: datahub.ingestion.source.sql.clickhouse.ClickHouseSource
  • Browse on GitHub

Module clickhouse-usage

Certified

Important Capabilities

CapabilityStatusNotes
Data ProfilingOptionally enabled via configuration
Detect Deleted EntitiesEnabled via stateful ingestion

This plugin has the below functionalities -

  1. For a specific dataset this plugin ingests the following statistics -
    1. top n queries.
    2. top users.
    3. usage of each column in the dataset.
  2. Aggregation of these statistics into buckets, by day or hour granularity.

Usage information is computed by querying the system.query_log table. In case you have a cluster or need to apply additional transformation/filters you can create a view and put to the query_log_table setting.

note

This source only does usage statistics. To get the tables, views, and schemas in your ClickHouse warehouse, ingest using the clickhouse source described above.

CLI based Ingestion

Install the Plugin

pip install 'acryl-datahub[clickhouse-usage]'

Starter Recipe

Check out the following recipe to get started with ingestion! See below for full configuration options.

For general pointers on writing and running a recipe, see our main recipe guide.

source:
type: clickhouse-usage
config:
# Coordinates
host_port: db_host:port
platform_instance: dev_cluster
email_domain: acryl.io

# Credentials
username: username
password: "password"

sink:
# sink configs

Config Details

Note that a . is used to denote nested fields in the YAML recipe.

FieldDescription
email_domain 
string
bucket_duration 
Enum
Size of the time window to aggregate usage stats.
Default: DAY
database 
string
database (catalog)
database_alias 
string
[Deprecated] Alias to apply to database when ingesting.
end_time 
string(date-time)
Latest date of usage to consider. Default: Current time in UTC
format_sql_queries 
boolean
Whether to format sql queries
Default: False
host_port 
string
ClickHouse host URL.
Default: localhost:8123
include_materialized_views 
boolean
Default: True
include_operational_stats 
boolean
Whether to display operational stats.
Default: True
include_read_operational_stats 
boolean
Whether to report read operational stats. Experimental.
Default: False
include_table_lineage 
boolean
Whether table lineage should be ingested.
Default: True
include_table_location_lineage 
boolean
If the source supports it, include table lineage to the underlying storage location.
Default: True
include_tables 
boolean
Whether tables should be ingested.
Default: True
include_top_n_queries 
boolean
Whether to ingest the top_n_queries.
Default: True
include_views 
boolean
Whether views should be ingested.
Default: True
options 
object
Default: {}
password 
string(password)
password
Default:
platform_instance 
string
The instance of the platform that all assets produced by this recipe belong to
platform_instance_map 
map(str,string)
protocol 
string
query_log_table 
string
Default: system.query_log
secure 
boolean
sqlalchemy_uri 
string
URI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.
start_time 
string(date-time)
Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)
top_n_queries 
integer
Number of top queries to save to each table.
Default: 10
username 
string
username
env 
string
The environment that all assets produced by this connector belong to
Default: PROD
domain 
map(str,AllowDenyPattern)
A class to store allow deny regexes
domain.key.allow 
array(string)
domain.key.deny 
array(string)
domain.key.ignoreCase 
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profile_pattern 
AllowDenyPattern
Regex patterns to filter tables (or specific columns) for profiling during ingestion. Note that only tables allowed by the table_pattern will be considered.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
profile_pattern.allow 
array(string)
profile_pattern.deny 
array(string)
profile_pattern.ignoreCase 
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
schema_pattern 
AllowDenyPattern
Regex patterns for schemas to filter in ingestion. Specify regex to only match the schema name. e.g. to match all tables in schema analytics, use the regex 'analytics'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
schema_pattern.allow 
array(string)
schema_pattern.deny 
array(string)
schema_pattern.ignoreCase 
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
table_pattern 
AllowDenyPattern
Regex patterns for tables to filter in ingestion. Specify regex to match the entire table name in database.schema.table format. e.g. to match all tables starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.allow 
array(string)
table_pattern.deny 
array(string)
table_pattern.ignoreCase 
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
user_email_pattern 
AllowDenyPattern
regex patterns for user emails to filter in usage.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
user_email_pattern.allow 
array(string)
user_email_pattern.deny 
array(string)
user_email_pattern.ignoreCase 
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
view_pattern 
AllowDenyPattern
Regex patterns for views to filter in ingestion. Note: Defaults to table_pattern if not specified. Specify regex to match the entire view name in database.schema.view format. e.g. to match all views starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
view_pattern.allow 
array(string)
view_pattern.deny 
array(string)
view_pattern.ignoreCase 
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profiling 
GEProfilingConfig
Default: {'enabled': False, 'limit': None, 'offset': None, ...
profiling.catch_exceptions 
boolean
Default: True
profiling.enabled 
boolean
Whether profiling should be done.
Default: False
profiling.field_sample_values_limit 
integer
Upper limit for number of sample values to collect for all columns.
Default: 20
profiling.include_field_distinct_count 
boolean
Whether to profile for the number of distinct values for each column.
Default: True
profiling.include_field_distinct_value_frequencies 
boolean
Whether to profile for distinct value frequencies.
Default: False
profiling.include_field_histogram 
boolean
Whether to profile for the histogram for numeric fields.
Default: False
profiling.include_field_max_value 
boolean
Whether to profile for the max value of numeric columns.
Default: True
profiling.include_field_mean_value 
boolean
Whether to profile for the mean value of numeric columns.
Default: True
profiling.include_field_median_value 
boolean
Whether to profile for the median value of numeric columns.
Default: True
profiling.include_field_min_value 
boolean
Whether to profile for the min value of numeric columns.
Default: True
profiling.include_field_null_count 
boolean
Whether to profile for the number of nulls for each column.
Default: True
profiling.include_field_quantiles 
boolean
Whether to profile for the quantiles of numeric columns.
Default: False
profiling.include_field_sample_values 
boolean
Whether to profile for the sample values for all columns.
Default: True
profiling.include_field_stddev_value 
boolean
Whether to profile for the standard deviation of numeric columns.
Default: True
profiling.limit 
integer
Max number of documents to profile. By default, profiles all documents.
profiling.max_number_of_fields_to_profile 
integer
A positive integer that specifies the maximum number of columns to profile for any table. None implies all columns. The cost of profiling goes up significantly as the number of columns to profile goes up.
profiling.max_workers 
integer
Number of worker threads to use for profiling. Set to 1 to disable.
Default: 20
profiling.offset 
integer
Offset in documents to profile. By default, uses no offset.
profiling.partition_datetime 
string(date-time)
For partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this.
profiling.partition_profiling_enabled 
boolean
Default: True
profiling.profile_if_updated_since_days 
number
Profile table only if it has been updated since these many number of days. If set to null, no constraint of last modified time for tables to profile. Supported only in snowflake and BigQuery.
profiling.profile_table_level_only 
boolean
Whether to perform profiling at table-level only, or include column-level profiling as well.
Default: False
profiling.profile_table_row_count_estimate_only 
boolean
Use an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres.
Default: False
profiling.profile_table_row_limit 
integer
Profile tables only if their row count is less then specified count. If set to null, no limit on the row count of tables to profile. Supported only in snowflake and BigQuery
Default: 5000000
profiling.profile_table_size_limit 
integer
Profile tables only if their size is less then specified GBs. If set to null, no limit on the size of tables to profile. Supported only in snowflake and BigQuery
Default: 5
profiling.query_combiner_enabled 
boolean
This feature is still experimental and can be disabled if it causes issues. Reduces the total number of queries issued and speeds up profiling by dynamically combining SQL queries where possible.
Default: True
profiling.report_dropped_profiles 
boolean
Whether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.
Default: False
profiling.turn_off_expensive_profiling_metrics 
boolean
Whether to turn off expensive profiling or not. This turns off profiling for quantiles, distinct_value_frequencies, histogram & sample_values. This also limits maximum number of fields being profiled to 10.
Default: False
stateful_ingestion 
StatefulStaleMetadataRemovalConfig
Base specialized config for Stateful Ingestion with stale metadata removal capability.
stateful_ingestion.enabled 
boolean
The type of the ingestion state provider registered with datahub.
Default: False
stateful_ingestion.ignore_new_state 
boolean
If set to True, ignores the current checkpoint state.
Default: False
stateful_ingestion.ignore_old_state 
boolean
If set to True, ignores the previous checkpoint state.
Default: False
stateful_ingestion.remove_stale_metadata 
boolean
Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.
Default: True

Code Coordinates

  • Class Name: datahub.ingestion.source.usage.clickhouse_usage.ClickHouseUsageSource
  • Browse on GitHub

Questions

If you've got any questions on configuring ingestion for ClickHouse, feel free to ping us on our Slack.