Package: mack
mackmack provides a variety of helper methods that make it easy for you to perform common Delta Lake operations.SetupInstall mack withpip install mack.Here's an example of how you can perform a Type 2 SCD upsert with a single line of code using Mack:importmackmack.type_2_scd_upsert(path,updatesDF,"pkey",["attr1","attr2"])Type 2 SCD UpsertsThis library provides an opinionated, conventions over configuration, approach to Type 2 SCD management. Let's look at an example before
covering the conventions required to take advantage of the functionality.Suppose you have the following SCD table with thepkeyprimary key:+----+-----+-----+----------+-------------------+--------+
|pkey|attr1|attr2|is_current|     effective_time|end_time|
+----+-----+-----+----------+-------------------+--------+
|   1|    A|    A|      true|2019-01-01 00:00:00|    null|
|   2|    B|    B|      true|2019-01-01 00:00:00|    null|
|   4|    D|    D|      true|2019-01-01 00:00:00|    null|
+----+-----+-----+----------+-------------------+--------+You'd like to perform an upsert with this data:+----+-----+-----+-------------------+
|pkey|attr1|attr2|     effective_time|
+----+-----+-----+-------------------+
|   2|    Z| null|2020-01-01 00:00:00| // upsert data
|   3|    C|    C|2020-09-15 00:00:00| // new pkey
+----+-----+-----+-------------------+Here's how to perform the upsert:mack.type_2_scd_upsert(delta_table,updatesDF,"pkey",["attr1","attr2"])Here's the table after the upsert:+----+-----+-----+----------+-------------------+-------------------+
|pkey|attr1|attr2|is_current|     effective_time|           end_time|
+----+-----+-----+----------+-------------------+-------------------+
|   2|    B|    B|     false|2019-01-01 00:00:00|2020-01-01 00:00:00|
|   4|    D|    D|      true|2019-01-01 00:00:00|               null|
|   1|    A|    A|      true|2019-01-01 00:00:00|               null|
|   3|    C|    C|      true|2020-09-15 00:00:00|               null|
|   2|    Z| null|      true|2020-01-01 00:00:00|               null|
+----+-----+-----+----------+-------------------+-------------------+You can leverage the upsert code if your SCD table meets these requirements:Contains a unique primary key columnAny change in an attribute column triggers an upsertSCD logic is exposed viaeffective_time,end_timeandis_currentcolumn (you can also use date or version columns for SCD upserts)Kill duplicatesThekill_duplicatefunction completely removes all duplicate rows from a Delta table.Suppose you have the following table:+----+----+----+
|col1|col2|col3|
+----+----+----+
|   1|   A|   A| # duplicate
|   2|   A|   B|
|   3|   A|   A| # duplicate
|   4|   A|   A| # duplicate
|   5|   B|   B| # duplicate
|   6|   D|   D|
|   9|   B|   B| # duplicate
+----+----+----+Run thekill_duplicatesfunction:mack.kill_duplicates(deltaTable,["col2","col3"])Here's the ending state of the table:+----+----+----+
|col1|col2|col3|
+----+----+----+
|   2|   A|   B|
|   6|   D|   D|
+----+----+----+Drop duplicates with Primary KeyThedrop_duplicates_pkeyfunction removes all but one duplicate row from a Delta table.Warning:You have to provide a primary column thatmust contain unique values, otherwise the method will default to kill the duplicates.
If you can not provide a unique primary key, you can use thedrop_duplicatesmethod.Suppose you have the following table:+----+----+----+----+
|col1|col2|col3|col4|
+----+----+----+----+
|   1|   A|   A|   C| # duplicate1
|   2|   A|   B|   C|
|   3|   A|   A|   D| # duplicate1
|   4|   A|   A|   E| # duplicate1
|   5|   B|   B|   C| # duplicate2
|   6|   D|   D|   C|
|   9|   B|   B|   E| # duplicate2
+----+----+----+----+Run thedrop_duplicatesfunction:mack.drop_duplicates_pkey(delta_table=deltaTable,primary_key="col1",duplication_columns=["col2","col3"])Here's the ending state of the table:+----+----+----+----+
|col1|col2|col3|col4|
+----+----+----+----+
|   1|   A|   A|   C|
|   2|   A|   B|   C|
|   5|   B|   B|   C|
|   6|   D|   D|   C|
+----+----+----+----+Drop duplicatesThedrop_duplicatesfunction removes all but one duplicate row from a Delta table. It behaves exactly like thedrop_duplicatesDataFrame API.Warning:This method is overwriting the whole table, thus very inefficient. If you can, use thedrop_duplicates_pkeymethod instead.Suppose you have the following table:+----+----+----+----+
|col1|col2|col3|col4|
+----+----+----+----+
|   1|   A|   A|   C| # duplicate
|   1|   A|   A|   C| # duplicate
|   2|   A|   A|   C|
+----+----+----+----+Run thedrop_duplicatesfunction:mack.drop_duplicates(delta_table=deltaTable,duplication_columns=["col1"])Here's the ending state of the table:+----+----+----+----+
|col1|col2|col3|col4|
+----+----+----+----+
|   1|   A|   A|   C| # duplicate
|   2|   A|   A|   C| # duplicate
+----+----+----+----+Copy tableThecopy_tablefunction copies an existing Delta table.
When you copy a table, it gets recreated at a specified target. This target could be a path or a table in a metastore.
Copying includes:DataPartitioningTable propertiesCopyingdoes notinclude the delta log, which means that you will not be able to restore the new table to an old version of the original
table.Here's how to perform the copy:mack.copy_table(delta_table=deltaTable,target_path=path)Validate appendThevalidate_appendfunction provides a mechanism for allowing some columns for schema evolution, but rejecting appends with columns that aren't specificly allowlisted.Suppose you have the following Delta table:+----+----+----+
|col1|col2|col3|
+----+----+----+
|   2|   b|   B|
|   1|   a|   A|
+----+----+----+Here's a appender function that wrapsvalidate_append:defappend_fun(delta_table,append_df):mack.validate_append(delta_table,append_df,required_cols=["col1","col2"],optional_cols=["col4"],)You can append the following DataFrame that contains the required columns and the optional columns:+----+----+----+
|col1|col2|col4|
+----+----+----+
|   3|   c| cat|
|   4|   d| dog|
+----+----+----+Here's what the Delta table will contain after that data is appended:+----+----+----+----+
|col1|col2|col3|col4|
+----+----+----+----+
|   3|   c|null| cat|
|   4|   d|null| dog|
|   2|   b|   B|null|
|   1|   a|   A|null|
+----+----+----+----+You cannot append the following DataFrame which contains the required columns, but also contains another column (col5) that's not specified as an optional column.+----+----+----+
|col1|col2|col5|
+----+----+----+
|   4|   b|   A|
|   5|   y|   C|
|   6|   z|   D|
+----+----+----+Here's the error you'll get when you attempt this write: "TypeError: The column 'col5' is not part of the current Delta table. If you want to add the column to the table you must set the optional_cols parameter."You also cannot append the following DataFrame which is missing one of the required columns.+----+----+
|col1|col4|
+----+----+
|   4|   A|
|   5|   C|
|   6|   D|
+----+----+Here's the error you'll get: "TypeError: The base Delta table has these columns '['col1', 'col4']', but these columns are required '['col1', 'col2']'."Append data without duplicatesTheappend_without_duplicatesfunction helps to append records to a existing Delta table without getting duplicates appended to the
record.Suppose you have the following Delta table:+----+----+----+
|col1|col2|col3|
+----+----+----+
|   1|   A|   B|
|   2|   C|   D|
|   3|   E|   F|
+----+----+----+Here is data to be appended:+----+----+----+
|col1|col2|col3|
+----+----+----+
|   2|   R|   T| # duplicate col1
|   8|   A|   B|
|   8|   C|   D| # duplicate col1
|  10|   X|   Y|
+----+----+----+Run theappend_without_duplicatesfunction:mack.append_without_duplicates(deltaTable,append_df,["col1"])Here's the ending result:+----+----+----+
|col1|col2|col3|
+----+----+----+
|   1|   A|   B|
|   2|   C|   D|
|   3|   E|   F|
|   8|   A|   B|
|  10|   X|   Y|
+----+----+----+Notice that the duplicatecol1value was not appended.  If a normal append operation was run, then the Delta table would contain two rows of data withcol1equal to 2.Delta File SizesThedelta_file_sizesfunction returns a dictionary that contains the total size in bytes, the amount of files and the average file size for a given Delta Table.Suppose you have the following Delta Table, partitioned bycol1:+----+----+----+
|col1|col2|col3|
+----+----+----+
|   1|   A|   A|
|   2|   A|   B|
+----+----+----+Runningmack.delta_file_sizes(delta_table)on that table will return:{"size_in_bytes": 1320,
"number_of_files": 2,
"average_file_size_in_bytes": 660}Show Delta File SizesTheshow_delta_file_sizesfunction prints the amount of files, the size of the table, and the average file size for a delta table.Suppose you have the following Delta Table, partitioned bycol1:+----+----+----+
|col1|col2|col3|
+----+----+----+
|   1|   A|   A|
|   2|   A|   B|
+----+----+----+Runningmack.delta_file_sizes(delta_table)on that table will print:The delta table contains 2 files with a size of 1.32 kB. The average file size is 660.0 BHumanize BytesThehumanize_bytesfunction formats an integer representing a number of bytes in an easily human readable format.mack.humanize_bytes(1234567890)# "1.23 GB"mack.humanize_bytes(1234567890000)# "1.23 TB"It's a lot easier for a human to understand 1.23 GB compared to 1234567890 bytes.Is Composite Key CandidateTheis_composite_key_candidatefunction returns a boolean that indicates whether a set of columns are unique and could form a composite key or not.Suppose you have the following Delta Table:+----+----+----+
|col1|col2|col3|
+----+----+----+
|   1|   A|   A|
|   2|   B|   B|
|   2|   C|   B|
+----+----+----+Runningmack.is_composite_key_candidate(delta_table, ["col1"])on that table will returnFalse.
Runningmack.is_composite_key_candidate(delta_table, ["col1", "col2"])on that table will returnTrue.Find Composite Key Candidates in the Delta tableThefind_composite_key_candidatesfunction helps you find a composite key that uniquely identifies the rows your Delta table.  It returns a list of columns that can be used as a composite key.Suppose you have the following Delta table:+----+----+----+
|col1|col2|col3|
+----+----+----+
|   1|   a|   z|
|   1|   a|   b|
|   3|   c|   b|
+----+----+----+Runningmack.find_composite_key_candidates(delta_table)on that table will return["col1", "col3"].Append md5 columnThewith_md5_colsfunction appends amd5hash of specified columns to the DataFrame.  This can be used as a unique key if the selected columns form a composite key.You can use this function with the columns identified infind_composite_key_candidatesto append a unique key to the DataFrame.Suppose you have the following Delta table:+----+----+----+
|col1|col2|col3|
+----+----+----+
|   1|   a|null|
|   2|   b|   b|
|   3|   c|   c|
+----+----+----+Runningmack.with_md5_cols(delta_table, ["col2", "col3"])on that table will append amd5_col2_col3as follows:+----+----+----+--------------------------------+
|col1|col2|col3|md5_col2_col3                   |
+----+----+----+--------------------------------+
|1   |a   |null|0cc175b9c0f1b6a831c399e269772661|
|2   |b   |b   |1eeaac3814eb80cc40efb005cf0b9141|
|3   |c   |c   |4e202f8309e7b00349c70845ab02fce9|
+----+----+----+--------------------------------+Get Latest Delta Table VersionThelatest_versionfunction gets the most current Delta
Table version number and returns it.delta_table=DeltaTable.forPath(spark,path)mack.latest_version(delta_table)>>2Append data with constraintsTheconstraint_appendfunction helps to append records to an existing Delta table even if there are records in the append dataframe that violate table constraints (both check and not null constraints), these records are appended to an existing quarantine Delta table instead of the target table. If the quarantine Delta table is set toNone, those records that violate table constraints are simply thrown out.Suppose you have the following target Delta table with the following schema and constraints:schema:
col1 int not null
col2 string null
col3 string null

check constraints:
col1_constraint: (col1 > 0)
col2_constraint: (col2 != 'Z')

+----+----+----+
|col1|col2|col3|
+----+----+----+
|   1|   A|   B|
|   2|   C|   D|
|   3|   E|   F|
+----+----+----+Suppose you have a quarantine Delta table with the same schema but without the constraints.Here is data to be appended:+----+----+----+
|col1|col2|col3|
+----+----+----+
|    |   H|   H| # violates col1 not null constraint
|   0|   Z|   Z| # violates both col1_constraint and col2_constraint
|   4|   A|   B|
|   5|   C|   D|
|   6|   E|   F|
|   9|   G|   G|
|  11|   Z|   Z| # violates col2_constraint
+----+----+----+Run theconstraint_appendfunction:mack.constraint_append(delta_table,append_df,quarantine_table)Here's the ending result in delta_table:+----+----+----+
|col1|col2|col3|
+----+----+----+
|   1|   A|   B|
|   2|   C|   D|
|   3|   E|   F|
|   4|   A|   B|
|   5|   C|   D|
|   6|   E|   F|
|   9|   G|   G|
+----+----+----+Here's the ending result in quarantine_table:+----+----+----+
|col1|col2|col3|
+----+----+----+
|    |   H|   H|
|   0|   Z|   Z|
|  11|   Z|   Z|
+----+----+----+Notice that the records that violated either of the constraints are appended to the quarantine table all other records are appended to the target table and the append has not failed.  If a normal append operation was run, then it would have failed on the constraint violation. Ifquarantine_tableis set toNone, records that violated either of the constraints are simply thrown out.Rename a Delta TableThis function is designed to rename a Delta table. It can operate either within a Databricks environment or with a standalone Spark session.Parameters:delta_table(DeltaTable): An object representing the Delta table to be renamed.new_table_name(str): The new name for the table.table_location(str, optional): The file path where the table is stored. If not provided, the function attempts to deduce the location from theDeltaTableobject. Defaults toNone.databricks(bool, optional): A flag indicating the function's operational environment. Set toTrueif running within Databricks, otherwise,False. Defaults toFalse.spark_session(pyspark.sql.SparkSession, optional): The Spark session. This is required whendatabricksis set toTrue. Defaults toNone.Returns:NoneRaises:TypeError: If the provideddelta_tableis not a DeltaTable object, or ifdatabricksis set toTrueandspark_sessionisNone.Example Usage:rename_delta_table(existing_delta_table,"new_table_name")DictionaryWe're leveraging the following terminology definedhere.Natural key:an attribute that can uniquely identify a row, and exists in the real world.Surrogate key:an attribute that can uniquely identify a row, and does not exist in the real world.Composite key:more than one attribute that when combined can uniquely identify a row.Primary key:the single unique identifier for the row.Candidate key:an attribute that could be the primary key.Alternate key:a candidate key that is not the primary key.Unique key:an attribute that can be unique on the table. Can also be called an alternate key.Foreign key:an attribute that is used to refer to another record in another table.Project maintainersMatthew Powers akaMrPowersRobert Kossendey akarobertkossendeySouvik Pratiher akasouvik-databricksProject philosophyThe mack library is designed to make common Delta Lake data tasks easier.You don't need to use mack of course.  You can write the logic yourself.If you don't want to add a dependency to your project, you can also easily copy / paste the functions from mack.  The functions in this library are intentionally designed to be easy to copy and paste.Let's look at some of the reasons you may want to add mack as a dependency.Exposing nice public interfacesThe public interface (and only the public interface) is available via themacknamespace.When you runimport mack, you can access the entirety of the public interface.  No private implementation details are exposed in themacknamespace.Minimal dependenciesMack only depends on Spark & Delta Lake.  No other dependencies will be added to Mack.Spark users leverage a variety of runtimes and it's not always easy to add a dependency.  You can runpip install mackand won't have to worry about resolving a lot of dependency conflicts.  You can also Just attach a mack wheel file to a cluster to leverage the project.Provide best practices examples for the communityMack strives to be a good example codebase for the PySpark / Delta Lake community.There aren't a lot of open source Delta Lake projects.  There are even fewer that use good software engineering practices like CI and unit testing.  You can use mack to help guide your design decisions in proprietary code repos.Stable public interfaces and long term support after 1.0 releaseMack reserves the right to make breaking public interface changes before the 1.0 release.  We'll always minimize breaking changes whenever possible.After the 1.0 release, Mack will stricly follow Semantic Versioning 2.0 and will only make breaking public interface changes in major releases.  Hopefully 1.0 will be the only major release and there won't have to be any breaking changes.Code designHere are some of the code design principles used in Mack:We avoid classes whenever possible.  Classes make it harder to copy / paste little chunks of code into notebooks.  It's good toStop Writing Classes.We try to make functions that are easy to copy.  We do this by limiting functions that depend on other functions or classes.  We'd rather nest a single use function in a public interface method than make it separate.Develop and then abstract.  All code goes in a single file till the right abstractions become apparent.  We'd rather have a large file than the wrong abstractions.Docker EnvironmentTheDockerfileanddocker-composefiles provide a containerized way to run and develop
withmack.The first time rundocker build --tag=mack .to build the image.To execute the unit tests inside theDockercontainer, rundocker-compose up testTo drop into the runningDockercontainer to develop, rundocker run -it mack /bin/bashCommunityBlogsDaniel Beach (Confessions of a Data Guy): Simplify Delta Lake Complexity with mack.Bartosz Konieczny (waitingforcode): Simplified Delta Lake operations with MackVideosGeekCoders on YouTube: How I use MACK Library in Delta Lake using Databricks/PySpark