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Caching and pipelining

Caching and pipelining

Splink is able to run against multiple SQL backends because all of the core data linking calculations are implemented in SQL. This SQL can therefore be submitted to a chosen SQL backend for execution.

Computations in Splink often take the form of a number of select statements run in sequence.

For example, the predict() step:

  • Inputs __splink__df_concat_with_tf and outputs __splink__df_blocked
  • Inputs __splink__df_blocked and outputs __splink__df_comparison_vectors
  • Inputs __splink__df_comparison_vectors and outputs __splink__df_match_weight_parts
  • Inputs __splink__df_match_weight_parts and outputs __splink__df_predict

To make this run faster, two key optimisations are implmented:

  • Pipelining - combining multiple select statements into a single statemenet using WITH(CTE) queries
  • Caching: saving the results of calculations so they don't need recalculating. This is especially useful because some intermediate calculations are reused multiple times during a typical Splink session

This article discusses the general implementation of caching and pipelining. The implementation needs some alterations for certain backends like Spark, which lazily evaluate SQL by default.

Implementation: Pipelining

A SQLPipeline class manages SQL pipelining.

A SQLPipeline is composed of a number of SQLTask objects, each of which represents a select statement.

The code is fairly straightforward: Given a sequence of select statements, [a,b,c] they are combined into a single query as follows:

a as (a_sql),
b as (b_sql),

To make this work, each statement (a,b,c) in the pipeline must refer to the previous step by name. For example, b_sql probably selects from the a_sql table, which has been aliased a. So b_sql must use the table name a to refer to the result of a_sql.

To make this tractable, each SQLTask has an output_table_name. For example, the output_table_name for a_sql in the above example is a.

For instance, in the predict() pipeline above, the first output_table_name is __splink__df_blocked. By giving each task a meaningful output_table_name, subsequent tasks can reference previous outputs in a way which is semantically clear.

Implementation: Caching

When a SQL pipeline is executed, it has two output names:

  • A physical_name, which is the name of the materialised table in the output database e.g. __splink__df_predict_cbc9833
  • A templated_name, which is a descriptive name of what the table represents e.g. __splink__df_predict

Each time Splink runs a SQL pipeline, the SQL string is hashed. This creates a unique identifier for that particular SQL string, which serves to identify the output.

When Splink is asked to execute a SQL string, before execution, it checks whether the resultant table already exists. If it does, it returns the table rather than recomputing it.

For example, when we run linker.predict(), Splink:

  • Generates the SQL tasks
  • Pipelines them into a single SQL statement
  • Hashes the statement to create a physical name for the outputs __splink__df_predict_cbc9833
  • Checks whether a table with physical name __splink__df_predict_cbc9833 alredy exists in the database
  • If not, executes the SQL statement, creating table __splink__df_predict_cbc9833 in the database.

In terms of implementation, the following happens:

  • SQL statements are generated an put in the queue - see here
  • Once all the tasks have been added to the queue, we call _execute_sql_pipeline() see here
  • The SQL is combined into a single pipelined statement here
  • We call _sql_to_splink_dataframe() which returns the table (from the cache if it already exists, or it executes the sql)
  • The table is returned as a SplinkDataframe, an abstraction over a table in a database. See here.

Some cached tables do not need a hash

A hash is required to uniquely identify some outputs. For example, blocking is used in several places in Splink, with different results. For example, the __splink__df_blocked needed to estimate parameters is different to the __splink__df_blocked needed in the predict() step.

As a result, we cannot materialise a single table called __splink__df_blocked in the database and reues it multiple times. This is why we append the hash of the SQL, so that we can uniquely identify the different versions of __splink__df_blocked which are needed in different contexts.

There are, however, some tables which are globally unique. They only take a single form, and if they exist in the cache they never need recomputing.

An example of this is __splink__df_concat_with_tf, which represents the concatenation of the input dataframes.

To create this table, we can execute _sql_to_splink_dataframe with materialise_as_hash set to False. The resultant materialised table will not have a hash appended, and will simply be called __splink__df_concat_with_tf. This is useful, because when performing calculations Splink can now check the cache for __splink__df_concat_with_tf each time it is needed.

In fact, many Splink pipelines begin with the assumption that this table exists in the database, because the first SQLTask in the pipeline refers to a table named __splink__df_concat_with_tf. To ensure this is the case, a function is used to create this table if it doesn't exist.

At what point should a pipeline of SQLTasks be executed (materialised into a physical table)?

For any individual output, it will usually be fastest to pipeline the full linage of tasks, right from raw data through to the end result.

However, there are many intermediate outputs which are used by many different Splink operations.

Performance can therefore be improved by computing and saving these intermediate outputs to a cache, to ensure they don't need to be computed repeatedly.

This is achieved by enqueueing SQL to a pipline and strategically calling execute_sql_pipeline to materialise results that need to cached.