Understanding the Functionality of `schema_resolver.py` in `sql_parsing.py`

ingestion
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Original Slack Thread

<@U06TM7M40RK> Please explain in detail the functionality of schema_resolver.py used in sql_parsing.py.

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The schema_resolver.py file in DataHub is a crucial component for resolving schema information for tables involved in SQL parsing and lineage generation. This functionality is essential for understanding the structure of the tables, which is necessary for accurate lineage extraction. Below, I will break down the key functionalities and components of schema_resolver.py as used in sql_parsing.py.

Key Components and Functionalities

  1. SchemaResolver Class:

    • The SchemaResolver class is responsible for resolving schema information for tables. It can fetch schema details from DataHub or other sources and cache this information for efficient access.
    • Initialization: The class is initialized with parameters such as platform, platform_instance, env, and optionally a graph object and a cache filename.
    • Cache Management: It uses a file-backed dictionary to cache schema information, which can be restored from a previous run if a cache filename is provided.

  2. Methods in SchemaResolver:

    • get_urn_for_table: This method generates a URN (Uniform Resource Name) for a given table. It normalizes the table name and constructs the URN using the platform, platform instance, and environment.
    • resolve_table: This method resolves the schema information for a given table. It first tries to fetch the schema from the cache and, if not found, fetches it from the DataHub graph.
    • _resolve_schema_info: This private method fetches schema information from the DataHub graph and saves it to the cache.
    • add_schema_metadata: This method adds schema metadata to the cache.
    • add_raw_schema_info: This method adds raw schema information to the cache.
    • add_graphql_schema_metadata: This method converts GraphQL schema metadata to the internal schema info format and adds it to the cache.
    • with_temp_tables: This method allows the resolver to work with temporary tables by creating a new resolver that includes additional schema information for these tables.

  3. SchemaResolverInterface:

    • This is a protocol that defines the interface for schema resolvers. It includes methods like resolve_table and properties like platform.

  4. _SchemaResolverWithExtras:

    • This is a helper class that extends the base resolver with additional schema information for temporary tables.

Example Usage in SQL Parsing

The schema_resolver.py is used in sql_parsing.py to resolve schema information during SQL parsing and lineage generation. Here’s an example of how it is used:

from datahub.sql_parsing.schema_resolver import SchemaResolver
from datahub.sql_parsing.sqlglot_lineage import sqlglot_lineage

# Initialize the SchemaResolver
schema_resolver = SchemaResolver(
    platform="snowflake",
    platform_instance="azure_instance",
    env="PROD"
)

# Example SQL query
sql_query = "INSERT INTO A(col1) SELECT abc FROM B;"

# Parse the SQL lineage
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Hey there! :wave: Make sure your message includes the following information if relevant, so we can help more effectively!

  1. Are you using UI or CLI for ingestion?
  2. Which DataHub version are you using? (e.g. 0.12.0)
  3. What data source(s) are you integrating with DataHub? (e.g. BigQuery)

<@U06TM7M40RK> Can you help with the questions in this thread?

:information_source: We’re experimenting with a new AI assistant for questions in the <#CUMUWQU66> channel to help you track down helpful resources more quickly. Keep in mind that responses may not always be spot-on; feel free to ask it follow-up questions and don’t forget to provide its responses a :thumbsup: or :thumbsdown: so other folks in the Community know if you’re still looking for help on the topic!

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Hello! I’m here to help with questions specifically about DataHub. Could you please provide more details or rephrase your question to focus on DataHub-related topics? For example, let me know if you’re using the UI or CLI for ingestion, which DataHub version you’re using, and what data source(s) you’re integrating with DataHub. This will help me assist you more effectively. :+1: 1 button :-1: 0 button :bulb: Hint: Mention <@U06TM7M40RK> in the thread for followups.

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result = sqlglot_lineage(
    sql=sql_query,
    schema_resolver=schema_resolver
)

print(result)

Detailed Breakdown of Key Methods

  1. get_urn_for_table:

    • This method constructs a URN for a table by normalizing the table name and combining it with the platform, platform instance, and environment.
    • Example:
      table_name = "my_database.my_schema.my_table"
urn = schema_resolver.get_urn_for_table(table_name)

  2. resolve_table:

    • This method resolves the schema information for a table by first checking the cache and then fetching from the DataHub graph if not found.
    • Example:
      table_name = "my_database.my_schema.my_table"
urn, schema_info = schema_resolver.resolve_table(table_name)

  3. _resolve_schema_info:

    • This private method fetches schema information from the DataHub graph and saves it to the cache.
    • Example:
      urn = "urn:li:dataset:(urn:li:dataPlatform:snowflake,my_table,PROD)"
schema_info = schema_resolver._resolve_schema_info(urn)

Conclusion

The schema_resolver.py file provides essential functionality for resolving schema information in DataHub. It supports efficient schema resolution through caching and integrates with the DataHub graph to fetch schema details. This functionality is critical for accurate SQL parsing and lineage generation, enabling DataHub to understand and visualize data flows between different datasets.

Would you like more detailed steps on any specific part of this process or any additional assistance?

Sources:

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<@U06TM7M40RK> Explain platform, platform_instance, and schemaInfo. If the query ‘INSERT INTO A(col1) SELECT abc FROM B;’ exists on the same platform and platform_instance but with different owners (schemas of the table) and the same table name, how does schema_resolver handle the result?

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Sure thing! I’m looking through the available data to answer your question. Hold on a second… :hourglass_flowing_sand:

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<@U07AECJ2WUW> please keep the troubleshooting in one thread if possible :slightly_smiling_face: sometimes RunLLM has some teething issues but should be back up after a brief intermission

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<@U06TM7M40RK> Explain platform, platform_instance, and schemaInfo. If the query ‘INSERT INTO A(col1) SELECT abc FROM B;’ exists on the same platform and platform_instance but with different owners (schemas of the table) and the same table name, how does schema_resolver handle the result?

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<@U06TM7M40RK> are you still unable to generate a response?

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:slightly_smiling_face: lets see, perhaps <@U03BEML16LB> could be of some assistance ?