# TiDB ## Overview TiDB is an open-source, distributed SQL database that is compatible with the MySQL protocol and MySQL ecosystem. Learn more in the [official TiDB documentation](https://docs.pingcap.com/tidb/stable/overview/). The DataHub integration for TiDB covers core relational metadata entities such as databases, schemas, tables, views, and schema fields. It also captures table- and column-level lineage, data profiling, and stateful deletion detection. ## Concept Mapping | Source Concept | DataHub Concept | Notes | | --------------------------- | ----------------- | --------------------------------------------- | | TiDB cluster or deployment | Platform Instance | Configure using the `platform_instance` field | | Database | Container | Groups schemas and datasets | | Schema | Container | Groups tables and views | | Table or view | Dataset | Primary ingested technical asset | | Column | SchemaField | Included through schema extraction | | View and table dependencies | Lineage edges | Available when lineage extraction is enabled | ## Module `tidb` ![Alpha](https://img.shields.io/badge/support%20status-Alpha-lightgrey) ### Important Capabilities | Capability | Status | Notes | | ---------- | ------ | ----- | | Asset Containers | ✅ | Enabled by default. Supported for types - Database, Schema. | | Column-level Lineage | ✅ | Enabled by default for views via `include_view_column_lineage`. Column-level lineage is also derived from query history when `include_usage_statistics` is enabled. Supported for types - View, Table. | | [Data Profiling](../../../../metadata-ingestion/docs/dev_guides/sql_profiles.md) | ✅ | Optionally enabled via configuration. | | Dataset Usage | ✅ | Optionally enabled via `include_usage_statistics`. Reads query history from `performance_schema` digests (default) or `mysql.general_log` (`usage_source: general_log`), which also yields query-based table lineage. | | Descriptions | ✅ | Enabled by default. | | [Detect Deleted Entities](../../../../metadata-ingestion/docs/dev_guides/stateful.md#stale-entity-removal) | ✅ | Enabled by default via stateful ingestion. | | [Domains](../../../domains.md) | ✅ | Supported via the `domain` config field. | | [Platform Instance](../../../platform-instances.md) | ✅ | Enabled by default. | | Schema Metadata | ✅ | Enabled by default. | | Table-Level Lineage | ✅ | Enabled by default for views via `include_view_lineage`. Table-level lineage is also derived from query history when `include_usage_statistics` is enabled. Supported for types - View, Table. | | Test Connection | ✅ | Enabled by default. | ### Overview Use the TiDB source to ingest relational metadata from TiDB clusters, including tables, views, and optional lineage/profiling signals. The source reuses DataHub's MySQL-compatible SQLAlchemy extraction path and emits assets under the TiDB platform (`urn:li:dataPlatform:tidb`). ### Prerequisites - Network access to the TiDB host and port (for example, `localhost:4000`). - A TiDB user with metadata access to the target databases and schemas (typically `SELECT` and `SHOW VIEW`). - Authentication details configured in the recipe (`username` and `password`). - If TLS is required, provide TiDB TLS connect arguments (`ssl_ca`, `ssl_cert`, `ssl_key`) under `options.connect_args`. ### Install the Plugin ```shell pip install 'acryl-datahub[tidb]' ``` ### Starter Recipe Check out the following recipe to get started with ingestion! See [below](#config-details) for full configuration options. For general pointers on writing and running a recipe, see our [main recipe guide](../../../../metadata-ingestion/README.md#recipes). ```yaml source: type: tidb config: # Coordinates host_port: localhost:4000 database: dbname # Credentials username: root password: example # If you need to use TLS with TiDB: # options: # connect_args: # ssl_ca: "path_to/server-ca.pem" # ssl_cert: "path_to/client-cert.pem" # ssl_key: "path_to/client-key.pem" # sink configs ``` ### Config Details #### Options Note that a `.` is used to denote nested fields in the YAML recipe.
| Field | Description | |:--- |:--- | |
convert_urns_to_lowercase
boolean
| Whether to convert dataset urns to lowercase. This value is part of each dataset's URN identity, so it must stay fixed for the life of a deployment. Changing it after data has been ingested re-keys every dataset (e.g. `MyDb.MyTable` becomes `mydb.mytable`); with stateful ingestion enabled the old-cased URNs are then soft-deleted as stale while the new-cased ones are created, producing duplicate or orphaned entities. Pick one value before the first run and leave it unchanged.
Default: False
| |
database
One of string, null
| database (catalog)
Default: None
| |
email_domain
One of string, null
| Email domain of your organisation, appended to `general_log` usernames (e.g. LDAP logins) so users display correctly. Ignored if the username already looks like an email. Only used with `usage_source: general_log`.
Default: None
| |
host_port
string
| TiDB host and port. Default port is 4000.
Default: localhost:4000
| |
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_usage_statistics
boolean
| Generate usage statistics and query-based lineage from query history. The source of that history is controlled by `usage_source`.
Default: False
| |
include_view_column_lineage
boolean
| Populates column-level lineage for view->view and table->view lineage using DataHub's sql parser. Requires `include_view_lineage` to be enabled.
Default: True
| |
include_view_lineage
boolean
| Populates view->view and table->view lineage using DataHub's sql parser.
Default: True
| |
include_views
boolean
| Whether views should be ingested.
Default: True
| |
incremental_lineage
boolean
| When enabled, emits lineage as incremental to existing lineage already in DataHub. When disabled, re-states lineage on each run.
Default: False
| |
options
object
| Any options specified here will be passed to [SQLAlchemy.create_engine](https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine) as kwargs. To set connection arguments in the URL, specify them under `connect_args`. | |
password
One of string(password), null
| password
Default: None
| |
platform_instance
One of string, null
| The instance of the platform that all assets produced by this recipe belong to. This should be unique within the platform. See https://docs.datahub.com/docs/platform-instances/ for more details.
Default: None
| |
sqlalchemy_uri
One of string, null
| URI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.
Default: None
| |
usage_source
Enum
| One of: "performance_schema", "general_log" | |
use_file_backed_cache
boolean
| Whether to use a file backed cache for the view definitions.
Default: True
| |
username
One of string, null
| username
Default: None
| |
env
string
| The environment that all assets produced by this connector belong to
Default: PROD
| |
database_pattern
AllowDenyPattern
| A class to store allow deny regexes.

Patterns are matched against the start of the string only, not the entire
string - a pattern does not need to match to the end to be considered a match.
For example, the pattern "prod" matches "prod", "prod_east", and "production".
To require an exact match, anchor your pattern explicitly, e.g. "^prod$". | |
database_pattern.ignoreCase
One of boolean, null
| Whether to ignore case sensitivity during pattern matching.
Default: True
| |
domain
map(str,AllowDenyPattern)
| A class to store allow deny regexes.

Patterns are matched against the start of the string only, not the entire
string - a pattern does not need to match to the end to be considered a match.
For example, the pattern "prod" matches "prod", "prod_east", and "production".
To require an exact match, anchor your pattern explicitly, e.g. "^prod$". | |
domain.`key`.allow
array
| List of regex patterns to include in ingestion. Patterns match from the start of the string only, not the entire string - anchor with '^...$' for an exact match, e.g. '^prod$'.
Default: ['.*']
| |
domain.`key`.allow.string
string
| | |
domain.`key`.ignoreCase
One of boolean, null
| Whether to ignore case sensitivity during pattern matching.
Default: True
| |
domain.`key`.deny
array
| List of regex patterns to exclude from ingestion. Patterns match from the start of the string only, not the entire string - anchor with '^...$' for an exact match, e.g. '^prod$'.
Default: []
| |
domain.`key`.deny.string
string
| | |
profile_pattern
AllowDenyPattern
| A class to store allow deny regexes.

Patterns are matched against the start of the string only, not the entire
string - a pattern does not need to match to the end to be considered a match.
For example, the pattern "prod" matches "prod", "prod_east", and "production".
To require an exact match, anchor your pattern explicitly, e.g. "^prod$". | |
profile_pattern.ignoreCase
One of boolean, null
| Whether to ignore case sensitivity during pattern matching.
Default: True
| |
table_pattern
AllowDenyPattern
| A class to store allow deny regexes.

Patterns are matched against the start of the string only, not the entire
string - a pattern does not need to match to the end to be considered a match.
For example, the pattern "prod" matches "prod", "prod_east", and "production".
To require an exact match, anchor your pattern explicitly, e.g. "^prod$". | |
table_pattern.ignoreCase
One of boolean, null
| Whether to ignore case sensitivity during pattern matching.
Default: True
| |
usage
BaseUsageConfig
| | |
usage.bucket_duration
Enum
| One of: "DAY", "HOUR" | |
usage.end_time
string(date-time)
| Latest date of lineage/usage to consider. Default: Current time in UTC | |
usage.format_sql_queries
boolean
| Whether to format sql queries
Default: False
| |
usage.include_operational_stats
boolean
| Whether to display operational stats.
Default: True
| |
usage.include_read_operational_stats
boolean
| Whether to report read operational stats. Experimental.
Default: False
| |
usage.include_top_n_queries
boolean
| Whether to ingest the top_n_queries.
Default: True
| |
usage.start_time
One of string(date-time), string
| Earliest date of lineage/usage to consider. Default: Last full day in UTC (or hour, depending on `bucket_duration`). You can also specify relative time with respect to end_time such as '-7 days' Or '-7d'.
Default: None
| |
usage.top_n_queries
integer
| Number of top queries to save to each table.
Default: 10
| |
usage.user_email_pattern
AllowDenyPattern
| A class to store allow deny regexes.

Patterns are matched against the start of the string only, not the entire
string - a pattern does not need to match to the end to be considered a match.
For example, the pattern "prod" matches "prod", "prod_east", and "production".
To require an exact match, anchor your pattern explicitly, e.g. "^prod$". | |
usage.user_email_pattern.ignoreCase
One of boolean, null
| Whether to ignore case sensitivity during pattern matching.
Default: True
| |
view_pattern
AllowDenyPattern
| A class to store allow deny regexes.

Patterns are matched against the start of the string only, not the entire
string - a pattern does not need to match to the end to be considered a match.
For example, the pattern "prod" matches "prod", "prod_east", and "production".
To require an exact match, anchor your pattern explicitly, e.g. "^prod$". | |
view_pattern.ignoreCase
One of boolean, null
| Whether to ignore case sensitivity during pattern matching.
Default: True
| |
classification
ClassificationConfig
| | |
classification.enabled
boolean
| Whether classification should be used to auto-detect glossary terms
Default: False
| |
classification.info_type_to_term
map(str,string)
| | |
classification.max_workers
integer
| Number of worker processes to use for classification. Set to 1 to disable.
Default: 4
| |
classification.sample_size
integer
| Number of sample values used for classification.
Default: 100
| |
classification.classifiers
array
| Classifiers to use to auto-detect glossary terms. If more than one classifier, infotype predictions from the classifier defined later in sequence take precedance.
Default: [{'type': 'datahub', 'config': None}]
| |
classification.classifiers.DynamicTypedClassifierConfig
DynamicTypedClassifierConfig
| | |
classification.classifiers.DynamicTypedClassifierConfig.type ❓
string
| The type of the classifier to use. The built-in `datahub` classifier has been removed; register a custom classifier and reference its type here. | |
classification.classifiers.DynamicTypedClassifierConfig.config
One of object, null
| The configuration required for initializing the classifier. If not specified, uses defaults for classifer type.
Default: None
| |
classification.column_pattern
AllowDenyPattern
| A class to store allow deny regexes.

Patterns are matched against the start of the string only, not the entire
string - a pattern does not need to match to the end to be considered a match.
For example, the pattern "prod" matches "prod", "prod_east", and "production".
To require an exact match, anchor your pattern explicitly, e.g. "^prod$". | |
classification.column_pattern.ignoreCase
One of boolean, null
| Whether to ignore case sensitivity during pattern matching.
Default: True
| |
classification.table_pattern
AllowDenyPattern
| A class to store allow deny regexes.

Patterns are matched against the start of the string only, not the entire
string - a pattern does not need to match to the end to be considered a match.
For example, the pattern "prod" matches "prod", "prod_east", and "production".
To require an exact match, anchor your pattern explicitly, e.g. "^prod$". | |
classification.table_pattern.ignoreCase
One of boolean, null
| Whether to ignore case sensitivity during pattern matching.
Default: True
| |
profiling
MySQLProfilingConfig
| | |
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
One of integer, null
| Max number of documents to profile. By default, profiles all documents.
Default: None
| |
profiling.max_distinct_per_statement
integer
| Only used when `query_combiner_flatten_enabled` is on. Maximum COUNT(DISTINCT) columns allowed in one flattened statement. Each one builds a distinct-value tree in server memory, so merging too many trades a scan problem for a memory problem. The default is a starting point, not a measured optimum — raise it if your server has headroom and unique counts dominate profiling time, lower it if profiling causes memory pressure.
Default: 5
| |
profiling.max_number_of_fields_to_profile
One of integer, null
| 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.
Default: None
| |
profiling.max_workers
integer
| Number of worker threads to use for profiling. Set to 1 to disable.
Default: 20
| |
profiling.nested_field_max_depth
integer
| Maximum recursion depth when flattening nested JSON structures during profiling. Lower values prevent recursion errors but may truncate deeply nested data. Applies to connectors that process dynamic JSON content (e.g., Kafka, MongoDB, Elasticsearch).
Default: 10
| |
profiling.offset
One of integer, null
| Offset in documents to profile. By default, uses no offset.
Default: None
| |
profiling.partition_datetime
One of string(date-time), null
| If specified, profile only the partition which matches this datetime. If not specified, profile the latest partition. Only Bigquery supports this.
Default: None
| |
profiling.partition_profiling_enabled
boolean
| Whether to profile partitioned tables. Only BigQuery and Aws Athena supports this. If enabled, latest partition data is used for profiling.
Default: True
| |
profiling.profile_external_tables
boolean
| Whether to profile external tables. Only Snowflake and Redshift supports this.
Default: False
| |
profiling.profile_if_updated_since_days
One of number, null
| 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 in `Snowflake`, `BigQuery`, and `Dremio`. Note: for Dremio this compares against DataHub's last-profiled timestamp (Dremio exposes no table modification time), so it controls profile frequency rather than reacting to upstream change.
Default: None
| |
profiling.profile_nested_fields
boolean
| Whether to profile complex types like structs, arrays and maps.
Default: False
| |
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 and MySQL.
Default: False
| |
profiling.profile_table_row_limit
One of integer, null
| MySQL: profile tables only if their estimated row count is less than this. Defaults to `null` (no limit) — set explicitly to guardrail large tables. The estimate comes from `information_schema.tables.table_rows`, which is a storage-engine stat that can be stale.
Default: None
| |
profiling.profile_table_size_limit
One of integer, null
| MySQL: profile tables only if their size is less than specified GBs. Defaults to `null` (no limit) — set explicitly to guardrail large tables. The size is `data_length` from `information_schema.tables`.
Default: None
| |
profiling.profiling_isolation_level
One of Enum, null
| Isolation level for the profiling connection. Defaults to unset, so the connection keeps the driver default and one transaction spans the whole table profile. Set AUTOCOMMIT if profiling is holding Postgres idle-in-transaction (blocking VACUUM) or pinning an InnoDB read view and growing the undo log on MySQL; each profiling SELECT then runs on its own. Under AUTOCOMMIT or READ_COMMITTED, metrics come from different snapshots, so a profile can be internally inconsistent on a concurrently-written table (e.g. uniqueCount > rowCount). On MySQL InnoDB, REPEATABLE_READ keeps a consistent snapshot; on Postgres, READ COMMITTED already takes a fresh snapshot per statement, so AUTOCOMMIT loses no consistency there.
Default: None
| |
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.query_combiner_flatten_enabled
boolean
| Flattens same-shape aggregate queries into one flat SELECT per FROM group to reduce full table scans on row stores (e.g. MySQL). Requires `query_combiner_enabled`; has no effect on its own. Off by default. COUNT(DISTINCT) columns are capped per statement to bound server memory.
Default: False
| |
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.sample_size
integer
| Number of rows to be sampled from table for column level profiling.Applicable only if `use_sampling` is set to True.
Default: 10000
| |
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
| |
profiling.use_sampling
boolean
| Whether to profile column level stats on sample of table. Only BigQuery and Snowflake support this. If enabled, profiling is done on rows sampled from table. Sampling is not done for smaller tables.
Default: True
| |
profiling.operation_config
OperationConfig
| | |
profiling.operation_config.lower_freq_profile_enabled
boolean
| Whether to do profiling at lower freq or not. This does not do any scheduling just adds additional checks to when not to run profiling.
Default: False
| |
profiling.operation_config.profile_date_of_month
One of integer, null
| Number between 1 to 31 for date of month (both inclusive). If not specified, defaults to Nothing and this field does not take affect.
Default: None
| |
profiling.operation_config.profile_day_of_week
One of integer, null
| Number between 0 to 6 for day of week (both inclusive). 0 is Monday and 6 is Sunday. If not specified, defaults to Nothing and this field does not take affect.
Default: None
| |
profiling.tags_to_ignore_sampling
One of array, null
| Fixed list of tags to ignore sampling. Each entry may be a full tag URN (e.g. `urn:li:tag:my_tag`) or just the tag name (e.g. `my_tag`). If not specified, tables will be sampled based on `use_sampling`.
Default: None
| |
profiling.tags_to_ignore_sampling.string
string
| | |
stateful_ingestion
One of StatefulStaleMetadataRemovalConfig, null
|
Default: None
| |
stateful_ingestion.enabled
boolean
| Whether or not to enable stateful ingest. Default: True if a pipeline_name is set and either a datahub-rest sink or `datahub_api` is specified, otherwise False
Default: False
| |
stateful_ingestion.fail_safe_threshold
number
| Prevents large amount of soft deletes & the state from committing from accidental changes to the source configuration if the relative change percent in entities compared to the previous state is above the 'fail_safe_threshold'.
Default: 75.0
| |
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
|
#### Schema The [JSONSchema](https://json-schema.org/) for this configuration is inlined below. ```javascript { "$defs": { "AllowDenyPattern": { "additionalProperties": false, "description": "A class to store allow deny regexes.\n\nPatterns are matched against the start of the string only, not the entire\nstring - a pattern does not need to match to the end to be considered a match.\nFor example, the pattern \"prod\" matches \"prod\", \"prod_east\", and \"production\".\nTo require an exact match, anchor your pattern explicitly, e.g. \"^prod$\".", "properties": { "allow": { "default": [ ".*" ], "description": "List of regex patterns to include in ingestion. Patterns match from the start of the string only, not the entire string - anchor with '^...$' for an exact match, e.g. '^prod$'.", "items": { "type": "string" }, "title": "Allow", "type": "array" }, "deny": { "default": [], "description": "List of regex patterns to exclude from ingestion. Patterns match from the start of the string only, not the entire string - anchor with '^...$' for an exact match, e.g. '^prod$'.", "items": { "type": "string" }, "title": "Deny", "type": "array" }, "ignoreCase": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": true, "description": "Whether to ignore case sensitivity during pattern matching.", "title": "Ignorecase" } }, "title": "AllowDenyPattern", "type": "object" }, "BaseUsageConfig": { "additionalProperties": false, "properties": { "bucket_duration": { "$ref": "#/$defs/BucketDuration", "default": "DAY", "description": "Size of the time window to aggregate usage stats." }, "end_time": { "description": "Latest date of lineage/usage to consider. Default: Current time in UTC", "format": "date-time", "title": "End Time", "type": "string" }, "start_time": { "anyOf": [ { "format": "date-time", "type": "string" }, { "pattern": "^\\s*-\\s*\\d+(\\.\\d+)?\\s*(ms|milliseconds?|s|secs?|seconds?|m|mins?|minutes?|h|hrs?|hours?|d|days?|w|weeks?|y|years?)\\s*$", "type": "string" } ], "default": null, "description": "Earliest date of lineage/usage to consider. Default: Last full day in UTC (or hour, depending on `bucket_duration`). You can also specify relative time with respect to end_time such as '-7 days' Or '-7d'.", "title": "Start Time" }, "top_n_queries": { "default": 10, "description": "Number of top queries to save to each table.", "exclusiveMinimum": 0, "title": "Top N Queries", "type": "integer" }, "user_email_pattern": { "$ref": "#/$defs/AllowDenyPattern", "default": { "allow": [ ".*" ], "deny": [], "ignoreCase": true }, "description": "regex patterns for user emails to filter in usage." }, "include_operational_stats": { "default": true, "description": "Whether to display operational stats.", "title": "Include Operational Stats", "type": "boolean" }, "include_read_operational_stats": { "default": false, "description": "Whether to report read operational stats. Experimental.", "title": "Include Read Operational Stats", "type": "boolean" }, "format_sql_queries": { "default": false, "description": "Whether to format sql queries", "title": "Format Sql Queries", "type": "boolean" }, "include_top_n_queries": { "default": true, "description": "Whether to ingest the top_n_queries.", "title": "Include Top N Queries", "type": "boolean" } }, "title": "BaseUsageConfig", "type": "object" }, "BucketDuration": { "enum": [ "DAY", "HOUR" ], "title": "BucketDuration", "type": "string" }, "ClassificationConfig": { "additionalProperties": false, "properties": { "enabled": { "default": false, "description": "Whether classification should be used to auto-detect glossary terms", "title": "Enabled", "type": "boolean" }, "sample_size": { "default": 100, "description": "Number of sample values used for classification.", "title": "Sample Size", "type": "integer" }, "max_workers": { "default": 4, "description": "Number of worker processes to use for classification. Set to 1 to disable.", "title": "Max Workers", "type": "integer" }, "table_pattern": { "$ref": "#/$defs/AllowDenyPattern", "default": { "allow": [ ".*" ], "deny": [], "ignoreCase": true }, "description": "Regex patterns to filter tables for classification. This is used in combination with other patterns in parent config. 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.*'" }, "column_pattern": { "$ref": "#/$defs/AllowDenyPattern", "default": { "allow": [ ".*" ], "deny": [], "ignoreCase": true }, "description": "Regex patterns to filter columns for classification. This is used in combination with other patterns in parent config. Specify regex to match the column name in `database.schema.table.column` format." }, "info_type_to_term": { "additionalProperties": { "type": "string" }, "default": {}, "description": "Optional mapping to provide glossary term identifier for info type", "title": "Info Type To Term", "type": "object" }, "classifiers": { "default": [ { "type": "datahub", "config": null } ], "description": "Classifiers to use to auto-detect glossary terms. If more than one classifier, infotype predictions from the classifier defined later in sequence take precedance.", "items": { "$ref": "#/$defs/DynamicTypedClassifierConfig" }, "title": "Classifiers", "type": "array" } }, "title": "ClassificationConfig", "type": "object" }, "DynamicTypedClassifierConfig": { "additionalProperties": false, "properties": { "type": { "description": "The type of the classifier to use. The built-in `datahub` classifier has been removed; register a custom classifier and reference its type here.", "title": "Type", "type": "string" }, "config": { "anyOf": [ {}, { "type": "null" } ], "default": null, "description": "The configuration required for initializing the classifier. If not specified, uses defaults for classifer type.", "title": "Config" } }, "required": [ "type" ], "title": "DynamicTypedClassifierConfig", "type": "object" }, "MySQLProfilingConfig": { "additionalProperties": false, "properties": { "enabled": { "default": false, "description": "Whether profiling should be done.", "title": "Enabled", "type": "boolean" }, "operation_config": { "$ref": "#/$defs/OperationConfig", "description": "Experimental feature. To specify operation configs." }, "limit": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "description": "Max number of documents to profile. By default, profiles all documents.", "title": "Limit" }, "offset": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "description": "Offset in documents to profile. By default, uses no offset.", "title": "Offset" }, "profile_table_level_only": { "default": false, "description": "Whether to perform profiling at table-level only, or include column-level profiling as well.", "title": "Profile Table Level Only", "type": "boolean" }, "include_field_null_count": { "default": true, "description": "Whether to profile for the number of nulls for each column.", "title": "Include Field Null Count", "type": "boolean" }, "include_field_distinct_count": { "default": true, "description": "Whether to profile for the number of distinct values for each column.", "title": "Include Field Distinct Count", "type": "boolean" }, "include_field_min_value": { "default": true, "description": "Whether to profile for the min value of numeric columns.", "title": "Include Field Min Value", "type": "boolean" }, "include_field_max_value": { "default": true, "description": "Whether to profile for the max value of numeric columns.", "title": "Include Field Max Value", "type": "boolean" }, "include_field_mean_value": { "default": true, "description": "Whether to profile for the mean value of numeric columns.", "title": "Include Field Mean Value", "type": "boolean" }, "include_field_median_value": { "default": true, "description": "Whether to profile for the median value of numeric columns.", "title": "Include Field Median Value", "type": "boolean" }, "include_field_stddev_value": { "default": true, "description": "Whether to profile for the standard deviation of numeric columns.", "title": "Include Field Stddev Value", "type": "boolean" }, "include_field_quantiles": { "default": false, "description": "Whether to profile for the quantiles of numeric columns.", "title": "Include Field Quantiles", "type": "boolean" }, "include_field_distinct_value_frequencies": { "default": false, "description": "Whether to profile for distinct value frequencies.", "title": "Include Field Distinct Value Frequencies", "type": "boolean" }, "include_field_histogram": { "default": false, "description": "Whether to profile for the histogram for numeric fields.", "title": "Include Field Histogram", "type": "boolean" }, "include_field_sample_values": { "default": true, "description": "Whether to profile for the sample values for all columns.", "title": "Include Field Sample Values", "type": "boolean" }, "max_workers": { "default": 20, "description": "Number of worker threads to use for profiling. Set to 1 to disable.", "title": "Max Workers", "type": "integer" }, "report_dropped_profiles": { "default": false, "description": "Whether to report datasets or dataset columns which were not profiled. Set to `True` for debugging purposes.", "title": "Report Dropped Profiles", "type": "boolean" }, "turn_off_expensive_profiling_metrics": { "default": false, "description": "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.", "title": "Turn Off Expensive Profiling Metrics", "type": "boolean" }, "field_sample_values_limit": { "default": 20, "description": "Upper limit for number of sample values to collect for all columns.", "title": "Field Sample Values Limit", "type": "integer" }, "max_number_of_fields_to_profile": { "anyOf": [ { "exclusiveMinimum": 0, "type": "integer" }, { "type": "null" } ], "default": null, "description": "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.", "title": "Max Number Of Fields To Profile" }, "profile_if_updated_since_days": { "anyOf": [ { "exclusiveMinimum": 0, "type": "number" }, { "type": "null" } ], "default": null, "description": "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 in `Snowflake`, `BigQuery`, and `Dremio`. Note: for Dremio this compares against DataHub's last-profiled timestamp (Dremio exposes no table modification time), so it controls profile frequency rather than reacting to upstream change.", "schema_extra": { "supported_sources": [ "snowflake", "bigquery", "dremio" ] }, "title": "Profile If Updated Since Days" }, "profile_table_size_limit": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "description": "MySQL: profile tables only if their size is less than specified GBs. Defaults to `null` (no limit) \u2014 set explicitly to guardrail large tables. The size is `data_length` from `information_schema.tables`.", "schema_extra": { "supported_sources": [ "mysql", "mariadb", "doris", "tidb" ] }, "title": "Profile Table Size Limit" }, "profile_table_row_limit": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "description": "MySQL: profile tables only if their estimated row count is less than this. Defaults to `null` (no limit) \u2014 set explicitly to guardrail large tables. The estimate comes from `information_schema.tables.table_rows`, which is a storage-engine stat that can be stale.", "schema_extra": { "supported_sources": [ "mysql", "mariadb", "doris", "tidb" ] }, "title": "Profile Table Row Limit" }, "profile_table_row_count_estimate_only": { "default": false, "description": "Use an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres and MySQL. ", "schema_extra": { "supported_sources": [ "postgres", "mysql" ] }, "title": "Profile Table Row Count Estimate Only", "type": "boolean" }, "query_combiner_enabled": { "default": true, "description": "*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.", "title": "Query Combiner Enabled", "type": "boolean" }, "query_combiner_flatten_enabled": { "default": false, "description": "Flattens same-shape aggregate queries into one flat SELECT per FROM group to reduce full table scans on row stores (e.g. MySQL). Requires `query_combiner_enabled`; has no effect on its own. Off by default. COUNT(DISTINCT) columns are capped per statement to bound server memory.", "title": "Query Combiner Flatten Enabled", "type": "boolean" }, "max_distinct_per_statement": { "default": 5, "description": "Only used when `query_combiner_flatten_enabled` is on. Maximum COUNT(DISTINCT) columns allowed in one flattened statement. Each one builds a distinct-value tree in server memory, so merging too many trades a scan problem for a memory problem. The default is a starting point, not a measured optimum \u2014 raise it if your server has headroom and unique counts dominate profiling time, lower it if profiling causes memory pressure.", "exclusiveMinimum": 0, "title": "Max Distinct Per Statement", "type": "integer" }, "catch_exceptions": { "default": true, "description": "", "title": "Catch Exceptions", "type": "boolean" }, "profiling_isolation_level": { "anyOf": [ { "$ref": "#/$defs/ProfilingIsolationLevel" }, { "type": "null" } ], "default": null, "description": "Isolation level for the profiling connection. Defaults to unset, so the connection keeps the driver default and one transaction spans the whole table profile. Set AUTOCOMMIT if profiling is holding Postgres idle-in-transaction (blocking VACUUM) or pinning an InnoDB read view and growing the undo log on MySQL; each profiling SELECT then runs on its own. Under AUTOCOMMIT or READ_COMMITTED, metrics come from different snapshots, so a profile can be internally inconsistent on a concurrently-written table (e.g. uniqueCount > rowCount). On MySQL InnoDB, REPEATABLE_READ keeps a consistent snapshot; on Postgres, READ COMMITTED already takes a fresh snapshot per statement, so AUTOCOMMIT loses no consistency there.", "schema_extra": { "supported_sources": [ "mysql", "postgres" ] } }, "partition_profiling_enabled": { "default": true, "description": "Whether to profile partitioned tables. Only BigQuery and Aws Athena supports this. If enabled, latest partition data is used for profiling.", "schema_extra": { "supported_sources": [ "athena", "bigquery" ] }, "title": "Partition Profiling Enabled", "type": "boolean" }, "partition_datetime": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "default": null, "description": "If specified, profile only the partition which matches this datetime. If not specified, profile the latest partition. Only Bigquery supports this.", "schema_extra": { "supported_sources": [ "bigquery" ] }, "title": "Partition Datetime" }, "use_sampling": { "default": true, "description": "Whether to profile column level stats on sample of table. Only BigQuery and Snowflake support this. If enabled, profiling is done on rows sampled from table. Sampling is not done for smaller tables. ", "schema_extra": { "supported_sources": [ "bigquery", "snowflake" ] }, "title": "Use Sampling", "type": "boolean" }, "sample_size": { "default": 10000, "description": "Number of rows to be sampled from table for column level profiling.Applicable only if `use_sampling` is set to True.", "schema_extra": { "supported_sources": [ "bigquery", "snowflake" ] }, "title": "Sample Size", "type": "integer" }, "profile_external_tables": { "default": false, "description": "Whether to profile external tables. Only Snowflake and Redshift supports this.", "schema_extra": { "supported_sources": [ "redshift", "snowflake" ] }, "title": "Profile External Tables", "type": "boolean" }, "tags_to_ignore_sampling": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Fixed list of tags to ignore sampling. Each entry may be a full tag URN (e.g. `urn:li:tag:my_tag`) or just the tag name (e.g. `my_tag`). If not specified, tables will be sampled based on `use_sampling`.", "title": "Tags To Ignore Sampling" }, "profile_nested_fields": { "default": false, "description": "Whether to profile complex types like structs, arrays and maps. ", "title": "Profile Nested Fields", "type": "boolean" }, "nested_field_max_depth": { "default": 10, "description": "Maximum recursion depth when flattening nested JSON structures during profiling. Lower values prevent recursion errors but may truncate deeply nested data. Applies to connectors that process dynamic JSON content (e.g., Kafka, MongoDB, Elasticsearch).", "exclusiveMinimum": 0, "title": "Nested Field Max Depth", "type": "integer" } }, "title": "MySQLProfilingConfig", "type": "object" }, "MySQLUsageSource": { "enum": [ "performance_schema", "general_log" ], "title": "MySQLUsageSource", "type": "string" }, "OperationConfig": { "additionalProperties": false, "properties": { "lower_freq_profile_enabled": { "default": false, "description": "Whether to do profiling at lower freq or not. This does not do any scheduling just adds additional checks to when not to run profiling.", "title": "Lower Freq Profile Enabled", "type": "boolean" }, "profile_day_of_week": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "description": "Number between 0 to 6 for day of week (both inclusive). 0 is Monday and 6 is Sunday. If not specified, defaults to Nothing and this field does not take affect.", "title": "Profile Day Of Week" }, "profile_date_of_month": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "description": "Number between 1 to 31 for date of month (both inclusive). If not specified, defaults to Nothing and this field does not take affect.", "title": "Profile Date Of Month" } }, "title": "OperationConfig", "type": "object" }, "ProfilingIsolationLevel": { "enum": [ "AUTOCOMMIT", "READ COMMITTED", "REPEATABLE READ", "SERIALIZABLE" ], "title": "ProfilingIsolationLevel", "type": "string" }, "StatefulStaleMetadataRemovalConfig": { "additionalProperties": false, "description": "Base specialized config for Stateful Ingestion with stale metadata removal capability.", "properties": { "enabled": { "default": false, "description": "Whether or not to enable stateful ingest. Default: True if a pipeline_name is set and either a datahub-rest sink or `datahub_api` is specified, otherwise False", "title": "Enabled", "type": "boolean" }, "remove_stale_metadata": { "default": true, "description": "Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.", "title": "Remove Stale Metadata", "type": "boolean" }, "fail_safe_threshold": { "default": 75.0, "description": "Prevents large amount of soft deletes & the state from committing from accidental changes to the source configuration if the relative change percent in entities compared to the previous state is above the 'fail_safe_threshold'.", "maximum": 100.0, "minimum": 0.0, "title": "Fail Safe Threshold", "type": "number" } }, "title": "StatefulStaleMetadataRemovalConfig", "type": "object" } }, "additionalProperties": false, "properties": { "table_pattern": { "$ref": "#/$defs/AllowDenyPattern", "default": { "allow": [ ".*" ], "deny": [], "ignoreCase": true }, "description": "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.*'" }, "view_pattern": { "$ref": "#/$defs/AllowDenyPattern", "default": { "allow": [ ".*" ], "deny": [], "ignoreCase": true }, "description": "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.*'" }, "classification": { "$ref": "#/$defs/ClassificationConfig", "default": { "enabled": false, "sample_size": 100, "max_workers": 4, "table_pattern": { "allow": [ ".*" ], "deny": [], "ignoreCase": true }, "column_pattern": { "allow": [ ".*" ], "deny": [], "ignoreCase": true }, "info_type_to_term": {}, "classifiers": [ { "config": null, "type": "datahub" } ] }, "description": "For details, refer to [Classification](../../../../metadata-ingestion/docs/dev_guides/classification.md)." }, "incremental_lineage": { "default": false, "description": "When enabled, emits lineage as incremental to existing lineage already in DataHub. When disabled, re-states lineage on each run.", "title": "Incremental Lineage", "type": "boolean" }, "convert_urns_to_lowercase": { "default": false, "description": "Whether to convert dataset urns to lowercase. This value is part of each dataset's URN identity, so it must stay fixed for the life of a deployment. Changing it after data has been ingested re-keys every dataset (e.g. `MyDb.MyTable` becomes `mydb.mytable`); with stateful ingestion enabled the old-cased URNs are then soft-deleted as stale while the new-cased ones are created, producing duplicate or orphaned entities. Pick one value before the first run and leave it unchanged.", "title": "Convert Urns To Lowercase", "type": "boolean" }, "env": { "default": "PROD", "description": "The environment that all assets produced by this connector belong to", "title": "Env", "type": "string" }, "platform_instance": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The instance of the platform that all assets produced by this recipe belong to. This should be unique within the platform. See https://docs.datahub.com/docs/platform-instances/ for more details.", "title": "Platform Instance" }, "stateful_ingestion": { "anyOf": [ { "$ref": "#/$defs/StatefulStaleMetadataRemovalConfig" }, { "type": "null" } ], "default": null }, "options": { "additionalProperties": true, "description": "Any options specified here will be passed to [SQLAlchemy.create_engine](https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine) as kwargs. To set connection arguments in the URL, specify them under `connect_args`.", "title": "Options", "type": "object" }, "profile_pattern": { "$ref": "#/$defs/AllowDenyPattern", "default": { "allow": [ ".*" ], "deny": [], "ignoreCase": true }, "description": "Regex patterns to filter tables (or specific columns) for profiling during ingestion. Note that only tables allowed by the `table_pattern` will be considered." }, "domain": { "additionalProperties": { "$ref": "#/$defs/AllowDenyPattern" }, "default": {}, "description": "Attach domains to databases, schemas or tables during ingestion using regex patterns. Domain key can be a guid like *urn:li:domain:ec428203-ce86-4db3-985d-5a8ee6df32ba* or a string like \"Marketing\".) If you provide strings, then datahub will attempt to resolve this name to a guid, and will error out if this fails. There can be multiple domain keys specified.", "title": "Domain", "type": "object" }, "include_views": { "default": true, "description": "Whether views should be ingested.", "title": "Include Views", "type": "boolean" }, "include_tables": { "default": true, "description": "Whether tables should be ingested.", "title": "Include Tables", "type": "boolean" }, "include_table_location_lineage": { "default": true, "description": "If the source supports it, include table lineage to the underlying storage location.", "title": "Include Table Location Lineage", "type": "boolean" }, "include_view_lineage": { "default": true, "description": "Populates view->view and table->view lineage using DataHub's sql parser.", "title": "Include View Lineage", "type": "boolean" }, "include_view_column_lineage": { "default": true, "description": "Populates column-level lineage for view->view and table->view lineage using DataHub's sql parser. Requires `include_view_lineage` to be enabled.", "title": "Include View Column Lineage", "type": "boolean" }, "use_file_backed_cache": { "default": true, "description": "Whether to use a file backed cache for the view definitions.", "title": "Use File Backed Cache", "type": "boolean" }, "profiling": { "$ref": "#/$defs/MySQLProfilingConfig", "description": "Configuration for profiling tables." }, "username": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "username", "title": "Username" }, "password": { "anyOf": [ { "format": "password", "type": "string", "writeOnly": true }, { "type": "null" } ], "default": null, "description": "password", "title": "Password" }, "host_port": { "default": "localhost:4000", "description": "TiDB host and port. Default port is 4000.", "title": "Host Port", "type": "string" }, "database": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "database (catalog)", "title": "Database" }, "sqlalchemy_uri": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "URI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.", "title": "Sqlalchemy Uri" }, "database_pattern": { "$ref": "#/$defs/AllowDenyPattern", "default": { "allow": [ ".*" ], "deny": [], "ignoreCase": true }, "description": "Regex patterns for databases to filter in ingestion." }, "include_usage_statistics": { "default": false, "description": "Generate usage statistics and query-based lineage from query history. The source of that history is controlled by `usage_source`.", "title": "Include Usage Statistics", "type": "boolean" }, "usage_source": { "$ref": "#/$defs/MySQLUsageSource", "default": "performance_schema", "description": "Where to read query history from. `performance_schema` (default) reads normalized digests from `events_statements_summary_by_digest` (no setup, no per-user attribution). Its `COUNT_STAR` is cumulative since the last counter reset (server restart or table truncation), so the first ingestion after enabling usage can report a large one-day spike attributing all history-to-date to a single timestamp. `general_log` reads literal statements with user and timestamp from `mysql.general_log` (requires `general_log=ON` and `log_output=TABLE`)." }, "usage": { "$ref": "#/$defs/BaseUsageConfig", "description": "Usage statistics config. Only used when `include_usage_statistics` is enabled." }, "email_domain": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Email domain of your organisation, appended to `general_log` usernames (e.g. LDAP logins) so users display correctly. Ignored if the username already looks like an email. Only used with `usage_source: general_log`.", "title": "Email Domain" } }, "title": "TiDBConfig", "type": "object" } ``` ### Capabilities - Ingests TiDB tables and views with schema and column metadata. - Supports optional profiling and SQL-based lineage extraction when enabled. - Supports secure connections through TLS client configuration. #### Migration from MySQL Connector If you were previously ingesting TiDB using the MySQL connector, switch to the dedicated TiDB connector so assets are modeled with the TiDB platform: **Configuration changes:** - Change `type: mysql` -> `type: tidb` - Change port: `3306` -> `4000` unless your TiDB deployment uses a custom port **Important:** Dataset URNs will change from `platform:mysql` to `platform:tidb`. This creates new entities in DataHub. Enable stateful ingestion with `remove_stale_metadata: true` to automatically clean up old MySQL-based entities. ### Limitations - TiDB does not support stored procedures or functions, so stored procedure ingestion is disabled for this source. - TiDB sequences (`CREATE SEQUENCE`) are not ingested. They are a TiDB-native object type with no MySQL equivalent and are not exposed through the MySQL-compatible table reflection path this source relies on. - TiDB-specific table flavors (partitioned, cached, and global temporary tables) are ingested as regular tables, consistent with how TiDB reports them (`TABLE_TYPE = 'BASE TABLE'`). - Lineage completeness depends on available view definitions and SQL parsing coverage. - Profiling on very large schemas or wide tables can increase extraction time. ### Troubleshooting - **Permission errors**: verify the ingestion user has required metadata grants on all included databases and schemas. - **Connection failures**: validate `host_port`, database name, and whether the runtime can reach the TiDB instance. - **TLS handshake issues**: confirm `ssl_ca`, `ssl_cert`, and `ssl_key` paths and certificate compatibility with the server. ### Code Coordinates - Class Name: `datahub.ingestion.source.sql.tidb.TiDBSource` - Browse on [GitHub](https://github.com/datahub-project/datahub/blob/master/metadata-ingestion/src/datahub/ingestion/source/sql/tidb.py) :::tip Questions? If you've got any questions on configuring ingestion for TiDB, feel free to ping us on [our Slack](https://datahub.com/slack). ::: :::note 💡 **Contributing to this documentation** This page is auto-generated from the underlying source code. To make changes, please edit the relevant source files in the [metadata-ingestion](https://github.com/datahub-project/datahub/tree/master/metadata-ingestion) directory. **Tip:** For quick typo fixes or documentation updates, you can click the ✏️ **Edit** icon directly in the GitHub UI to open a Pull Request. For larger changes and PR naming conventions, please refer to our [Contributing Guide](/docs/contributing). :::