Releases: databrickslabs/remorph
Releases · databrickslabs/remorph
v0.9.0
- Added support for format_datetime function in presto to Databricks (#1250). A new
format_datetime
function has been added to theParser
class in thepresto.py
file to provide support for formatting datetime values in Presto on Databricks. This function utilizes theDateFormat.from_arg_list
method from thelocal_expression
module to format datetime values according to a specified format string. To ensure compatibility and consistency between Presto and Databricks, a new test filetest_format_datetime_1.sql
has been added, containing SQL queries that demonstrate the usage of theformat_datetime
function in Presto and its equivalent in Databricks,DATE_FORMAT
. This standalone change adds new functionality without modifying any existing code. - Added support for SnowFlake
SUBSTR
(#1238). This commit enhances the library's SnowFlake support by adding theSUBSTR
function, which was previously unsupported and existed only as an alternative toSUBSTRING
. The project now fully supports both functions, and theSUBSTRING
function can be used interchangeably withSUBSTR
via the newwithConversionStrategy(SynonymOf("SUBSTR"))
method. Additionally, this commit supersedes a previous pull request that lacked a GPG signature and includes a test for theSUBSTR
function. TheARRAY_SLICE
function has also been updated to match SnowFlake's behavior, and the project now supports a more comprehensive list of SQL functions with their corresponding arity. - Added support for json_size function in presto (#1236). A new
json_size
function for Presto has been added, which determines the size of a JSON object or array and returns an integer. Two new methods,_build_json_size
andget_json_object
, have been implemented to handle JSON objects and arrays differently, and the Parser and Tokenizer classes of the Presto class have been updated to include the new json_size function. An alternative implementation for Databricks using SQL functions is provided, and a test case is added to cover a fixedis not null
error for json_extract in the Databricks generator. Additionally, a new test file for Presto has been added to test the functionality of thejson_extract
function in Presto, and a new methodGetJsonObject
is introduced to extract a JSON object from a given path. Thejson_extract
function has also been updated to extract the value associated with a specified key from JSON data in both Presto and Databricks. - Enclosed subqueries in parenthesis (#1232). This PR introduces changes to the ExpressionGenerator and LogicalPlanGenerator classes to ensure that subqueries are correctly enclosed in parentheses during code generation. Previously, subqueries were not always enclosed in parentheses, leading to incorrect code. This issue has been addressed by enclosing subqueries in parentheses in the
in
andscalarSubquery
methods, and by adding new match cases forir.Filter
in theLogicalPlanGenerator
class. The changes also take care to avoid doubling enclosing parentheses in the.. IN(SELECT...)
pattern. New methods have not been added, and existing functionality has been modified to ensure that subqueries are correctly enclosed in parentheses, leading to the generation of correct SQL code. Test cases have been included in a separate PR. These changes improve the correctness of the generated code, avoiding issues such asSELECT * FROM SELECT * FROM t WHERE a >
aWHERE a > 'b'
and ensuring that the generated code includes parentheses around subqueries. - Fixed serialization of MultipleErrors (#1177). In the latest release, the encoding of errors in the
com.databricks.labs.remorph.coverage
package has been improved with an update to theencoders.scala
file. The change involves a fix for serializingMultipleErrors
instances using theasJson
method on each error instead of just the message. This modification ensures that all relevant information about each error is included in the encoded output, improving the accuracy of serialization forMultipleErrors
class. Users who handle multiple errors and require precise serialization representation will benefit from this enhancement, as it guarantees comprehensive information encoding for each error instance. - Fixed presto strpos and array_average functions (#1196). This PR introduces new classes
Locate
andNamedStruct
in thelocal_expression.py
file to handle theSTRPOS
andARRAY_AVERAGE
functions in a Databricks environment, ensuring compatibility with Presto SQL. TheSTRPOS
function, used to locate the position of a substring within a string, now uses theLocate
class and emits a warning regarding differences in implementation between Presto and Databricks SQL. A new method_build_array_average
has been added to handle theARRAY_AVERAGE
function in Databricks, which calculates the average of an array, accommodating nulls, integers, and doubles. Two SQL test cases have been added to demonstrate the use of theARRAY_AVERAGE
function with arrays containing integers and doubles. These changes promote compatibility and consistent behavior between Presto and Databricks when dealing withSTRPOS
andARRAY_AVERAGE
functions, enhancing the ability to migrate between the systems smoothly. - Handled presto Unnest cross join to Databricks lateral view (#1209). This release introduces new features and updates for handling Presto UNNEST cross joins in Databricks, utilizing the lateral view feature. New methods have been added to improve efficiency and robustness when handling UNNEST cross joins. Additionally, new test cases have been implemented for Presto and Databricks to ensure compatibility and consistency between the two systems in handling UNNEST cross joins, array construction and flattening, and parsing JSON data. Some limitations and issues remain, which will be addressed in future work. The acceptance tests have also been updated, with certain tests now expected to pass, while others may still fail. This release aims to improve the functionality and compatibility of Presto and Databricks when handling UNNEST cross joins and JSON data.
- Implemented remaining TSQL set operations (#1227). This pull request enhances the TSql parser by adding support for parsing and converting the set operations
UNION [ALL]
,EXCEPT
, andINTERSECT
to the Intermediate Representation (IR). Initially, the grammar recognized these operations, but they were not being converted to the IR. This change resolves issues #1126 and #1102 and includes new unit, transpiler, and functional tests, ensuring the correct behavior of these set operations, including precedence rules. The commit also introduces a new test file,union-all.sql
, demonstrating the correct handling of simpleUNION ALL
operations, ensuring consistent output across TSQL and Databricks SQL platforms. - Supported multiple columns in order by clause in for ARRAYAGG (#1228). This commit enhances the ARRAYAGG and LISTAGG functions by adding support for multiple columns in the order by clause and sorting in both ascending and descending order. A new method, sortArray, has been introduced to handle multiple sort orders. The changes also improve the functionality of the ARRAYAGG function in the Snowflake dialect by supporting multiple columns in the ORDER BY clause, with an optional DESC keyword for each column. The
WithinGroupParams
dataclass has been updated in the local expression module to include a list of tuples for the order columns and their sorting direction. These changes provide increased flexibility and control over the output of the ARRAYAGG and LISTAGG functions - Added TSQL parser support for
(LHS) UNION RHS
queries (#1211). In this release, we have implemented support for a new form of UNION in the TSQL parser, specifically for queries formatted as(SELECT a from b) UNION [ALL] SELECT x from y
. This allows the union of two SELECT queries with an optional ALL keyword to include duplicate rows. The implementation includes a new case statement in theTSqlRelationBuilder
class that handles this form of UNION, creating aSetOperation
object with the left-hand side and right-hand side of the union, and anis_all
flag based on the presence of the ALL keyword. Additionally, we have added support for parsing right-associative UNION clauses in TSQL queries, enhancing the flexibility and expressiveness of the TSQL parser for more complex and nuanced queries. The commit also includes new test cases to verify the correct translation of TSQL set operations to Databricks SQL, resolving issue #1127. This enhancement allows for more accurate parsing of TSQL queries that use the UNION operator in various formats. - Added support for inline columns in CTEs (#1184). In this release, we have added support for inline columns in Common Table Expressions (CTEs) in Snowflake across various components of our open-source library. This includes updates to the AST (Abstract Syntax Tree) for better TSQL translation and the introduction of the new case class
KnownInterval
for handling intervals. We have also implemented a new method,DealiasInlineColumnExpressions
, in theSnowflakePlanParser
...
v0.8.0
- Added IR for stored procedures (#1161). In this release, we have made significant enhancements to the project by adding support for stored procedures. We have introduced a new
CreateVariable
case class to manage variable creation within the intermediate representation (IR), and removed theSetVariable
case class as it is now redundant. A newCaseStatement
class has been added to represent SQL case statements with value match, and aCompoundStatement
class has been implemented to enable encapsulation of a sequence of logical plans within a single compound statement. TheDeclareCondition
,DeclareContinueHandler
, andDeclareExitHandler
case classes have been introduced to handle conditional logic and exit handlers in stored procedures. New classesDeclareVariable
,ElseIf
,ForStatement
,If
,Iterate
,Leave
,Loop
,RepeatUntil
,Return
,SetVariable
, andSignal
have been added to the project to provide more comprehensive support for procedural language features and control flow management in stored procedures. We have also included SnowflakeCommandBuilder support for stored procedures and updated thevisitExecuteTask
method to handle stored procedure calls using theSetVariable
method. - Added Variant Support (#998). In this commit, support for the Variant datatype has been added to the create table functionality, enhancing the system's compatibility with Snowflake's datatypes. A new VariantType has been introduced, which allows for more comprehensive handling of data during create table operations. Additionally, a
remarks VARIANT
line is added in the CREATE TABLE statement and the corresponding spec test has been updated. The Variant datatype is a flexible datatype that can store different types of data, such as arrays, objects, and strings, offering increased functionality for users working with variant data. Furthermore, this change will enable the use of the Variant datatype in Snowflake tables and improves the data modeling capabilities of the system. - Added
PySpark
generator (#1026). The engineering team has developed a newPySpark
generator for thecom.databricks.labs.remorph.generators
package. This addition introduces a new parameter,logical
, of typeGenerator[ir.LogicalPlan, String]
, in theSQLGenerator
for SQL queries. A new abstract classBasePythonGenerator
has been added, which extends theGenerator
class and generates Python code. AExpressionGenerator
class has also been added, which extendsBasePythonGenerator
and is responsible for generating Python code forir.Expression
objects. A newLogicalPlanGenerator
class has been added, which extendsBasePythonGenerator
and is responsible for generating Python code for a givenir.LogicalPlan
. A newStatementGenerator
class has been implemented, which convertsStatement
objects into Python code. A new Python-generating class,PythonGenerator
, has been added, which includes the implementation of an abstract syntax tree (AST) for Python in Scala. This AST includes classes for various Python language constructs. Additionally, new implicit classes forPythonInterpolator
,PythonOps
, andPythonSeqOps
have been added to allow for the creation of PySpark code using the Remorph framework. TheAndOrToBitwise
rule has been implemented to convertAnd
andOr
expressions to their bitwise equivalents. TheDotToFCol
rule has been implemented to transform code that references columns using dot notation in a DataFrame to use thecol
function with a string literal of the column name instead. A newPySparkStatements
object andPySparkExpressions
class have been added, which provide functionality for transforming expressions in a data processing pipeline to PySpark equivalents. TheSnowflakeToPySparkTranspiler
class has been added to transpile Snowflake queries to PySpark code. A newPySpark
generator has been added to theTranspiler
class, which is implemented as an instance of theSqlGenerator
class. This change enhances theTranspiler
class with a newPySpark
generator and improves serialization efficiency. - Added
debug-bundle
command for folder-to-folder translation (#1045). In this release, we have introduced adebug-bundle
command to the remorph project's CLI, specifically added to theproxy_command
function, which already includesdebug-script
,debug-me
, anddebug-coverage
commands. This new command enhances the tool's debugging capabilities, allowing developers to generate a bundle of translated queries for folder-to-folder translation tasks. Thedebug-bundle
command accepts three flags:dialect
,src
, anddst
, specifying the SQL dialect, source directory, and destination directory, respectively. Furthermore, the update includes refactoring theFileSetGenerator
class in theorchestration
package of thecom.databricks.labs.remorph.generators
package, adding adebug-bundle
command to theMain
object, and updating theFileQueryHistoryProvider
method in theApplicationContext
trait. These improvements focus on providing a convenient way to convert folder-based SQL scripts to other formats like SQL and PySpark, enhancing the translation capabilities of the project. - Added
ruff
Python formatter proxy (#1038). In this release, we have added support for theruff
Python formatter in our project's continuous integration and development workflow. We have also introduced a newFORMAT
stage in theWorkflowStage
object in theResult
Scala object to include formatting as a separate step in the workflow. A newRuffFormatter
class has been added to format Python code using theruff
tool, and aStandardInputPythonSubprocess
class has been included to run a Python subprocess and capture its output and errors. Additionally, we have added a proxy for theruff
formatter to the SnowflakeToPySparkTranspilerTest for Scala to improve the readability of the transpiled Python code generated by the SnowflakeToPySparkTranspiler. Lastly, we have introduced a newruff
formatter proxy in the test code for the transpiler library to enforce format and style conventions in Python code. These changes aim to improve the development and testing experience for the project and ensure that the code follows the desired formatting and style standards. - Added baseline for translating workflows (#1042). In this release, several new features have been added to the open-source library to improve the translation of workflows. A new dependency for the Jackson YAML data format library, version 2.14.0, has been added to the pom.xml file to enable processing YAML files and converting them to Java objects. A new
FileSet
class has been introduced, which provides an in-memory data structure to manage a set of files, allowing users to add, retrieve, and remove files by name and persist the contents of the files to the file system. A newFileSetGenerator
class has been added that generates aFileSet
object from aJobNode
object, enabling the translation of workflows by generating all necessary files for a workspace. A newDefineJob
class has been developed to define a new rule for processingJobNode
objects in the Remorph system, converting instances ofSuccessPy
andSuccessSQL
intoPythonNotebookTask
andSqlNotebookTask
objects, respectively. Additionally, various new classes, such asGenerateBundleFile
,QueryHistoryToQueryNodes
,ReformatCode
,TryGeneratePythonNotebook
,TryGenerateSQL
,TrySummarizeFailures
,InformationFile
,SuccessPy
,SuccessSQL
,FailedQuery
,Migration
,PartialQuery
,QueryPlan
,RawMigration
,Comment
, andPlanComment
, have been introduced to provide a more comprehensive and nuanced job orchestration framework. TheLibrary
case class has been updated to better separate concerns between library configuration and code assets. These changes address issue #1042 and provide a more robust and flexible workflow translation solution. - Added correct generation of
databricks.yml
forQueryHistory
(#1044). The FileSet class in the FileSet.scala file has been updated to include a new method that correctly generates thedatabricks.yml
file for theQueryHistory
feature. This file is used for orchestrating cross-compiled queries, creating three files in total - two SQL notebooks with translated and formatted queries and adatabricks.yml
file to define an asset bundle for the queries. The new method in the FileSet class writes the content to the file using theFiles.write
method from thejava.nio.file
package instead of the previously usedPrintWriter
. The FileSetGenerator class has been updated to include the newdatabricks.yml
file generation, and new rules and methods have been added to improve the accuracy and consistency of schema definitions in the generated orchestration files. Additionally, the DefineJob and DefineSchemas classes have been introduced to simplify the orchestration generation process. - Added documentation around Transformation (#1043). In this release, the Transformation class in our open-source library has been enhanced with detailed documentation, type parameters, and new methods. The class represents a stateful computation that produces an output of type Out while managing a state of type State. The new methods include map and flatMap for modifying the output and chaining transformations, as well as run and runAndDiscardState for executing the computation with ...
v0.1.6
- Added serverless validation using lsql library (#176). Workspaceclient object is used with
product
name andproduct_version
along with correspondingcluster_id
orwarehouse_id
assdk_config
inMorphConfig
object. - Enhanced install script to enforce usage of a warehouse or cluster when
skip-validation
is set toFalse
(#213). In this release, the installation process has been enhanced to mandate the use of a warehouse or cluster when theskip-validation
parameter is set toFalse
. This change has been implemented across various components, including the install script,transpile
function, andget_sql_backend
function. Additionally, new pytest fixtures and methods have been added to improve test configuration and resource management during testing. Unit tests have been updated to enforce usage of a warehouse or cluster when theskip-validation
flag is set toFalse
, ensuring proper resource allocation and validation process improvement. This development focuses on promoting a proper setup and usage of the system, guiding new users towards a correct configuration and improving the overall reliability of the tool. - Patch subquery with json column access (#190). The open-source library has been updated with new functionality to modify how subqueries with JSON column access are handled in the
snowflake.py
file. This change includes the addition of a check for an opening parenthesis after theFROM
keyword to detect and break loops when a subquery is found, as opposed to a table name. This improvement enhances the handling of complex subqueries and JSON column access, making the code more robust and adaptable to different query structures. Additionally, a new test method,test_nested_query_with_json
, has been introduced to thetests/unit/snow/test_databricks.py
file to test the behavior of nested queries involving JSON column access when using a Snowflake dialect. This new method validates the expected output of a specific nested query when it is transpiled to Snowflake's SQL dialect, allowing for more comprehensive testing of JSON column access and type casting in Snowflake dialects. The existingtest_delete_from_keyword
method remains unchanged. - Snowflake
UPDATE FROM
to DatabricksMERGE INTO
implementation (#198). - Use Runtime SQL backend in Notebooks (#211). In this update, the
db_sql.py
file in thedatabricks/labs/remorph/helpers
directory has been modified to support the use of the Runtime SQL backend in Notebooks. This change includes the addition of a newRuntimeBackend
class in thebackends
module and an import statement foros
. Theget_sql_backend
function now returns aRuntimeBackend
instance when theDATABRICKS_RUNTIME_VERSION
environment variable is present, allowing for more efficient and secure SQL statement execution in Databricks notebooks. Additionally, a new test case for theget_sql_backend
function has been added to ensure the correct behavior of the function in various runtime environments. These enhancements improve SQL execution performance and security in Databricks notebooks and increase the project's versatility for different use cases. - Added Issue Templates for bugs, feature and config (#194). Two new issue templates have been added to the project's GitHub repository to improve issue creation and management. The first template, located in
.github/ISSUE_TEMPLATE/bug.yml
, is for reporting bugs and prompts users to provide detailed information about the issue, including the current and expected behavior, steps to reproduce, relevant log output, and sample query. The second template, added under the path.github/ISSUE_TEMPLATE/config.yml
, is for configuration-related issues and includes support contact links for general Databricks questions and Remorph documentation, as well as fields for specifying the operating system and software version. A new issue template for feature requests, named "Feature Request", has also been added, providing a structured format for users to submit requests for new functionality for the Remorph project. These templates will help streamline the issue creation process, improve the quality of information provided, and make it easier for the development team to quickly identify and address bugs and feature requests. - Added Databricks Source Adapter (#185). In this release, the project has been enhanced with several new features for the Databricks Source Adapter. A new
engine
parameter has been added to theDataSource
class, replacing the originalsource
parameter. The_get_secrets
and_get_table_or_query
methods have been updated to use theengine
parameter for key naming and handling queries with aselect
statement differently, respectively. A Databricks Source Adapter for Oracle databases has been introduced, which includes a newOracleDataSource
class that provides functionality to connect to an Oracle database using JDBC. A Databricks Source Adapter for Snowflake has also been added, featuring theSnowflakeDataSource
class that handles data reading and schema retrieval from Snowflake. TheDatabricksDataSource
class has been updated to handle data reading and schema retrieval from Databricks, including a newget_schema_query
method that generates the query to fetch the schema based on the provided catalog and table name. Exception handling for reading data and fetching schema has been implemented for all new classes. These changes provide increased flexibility for working with various data sources, improved code maintainability, and better support for different use cases. - Added Threshold Query Builder (#188). In this release, the open-source library has added a Threshold Query Builder feature, which includes several changes to the existing functionality in the data source connector. A new import statement adds the
re
module for regular expressions, and new parameters have been added to theread_data
andget_schema
abstract methods. The_get_jdbc_reader_options
method has been updated to accept aoptions
parameter of type "JdbcReaderOptions", and a new static method, "_get_table_or_query", has been added to construct the table or query string based on provided parameters. Additionally, a new class, "QueryConfig", has been introduced in the "databricks.labs.remorph.reconcile" package to configure queries for data reconciliation tasks. A new abstract base class QueryBuilder has been added to the query_builder.py file, along with HashQueryBuilder and ThresholdQueryBuilder classes to construct SQL queries for generating hash values and selecting columns based on threshold values, transformation rules, and filtering conditions. These changes aim to enhance the functionality of the data source connector, add modularity, customizability, and reusability to the query builder, and improve data reconciliation tasks. - Added snowflake connector code (#177). In this release, the open-source library has been updated to add a Snowflake connector for data extraction and schema manipulation. The changes include the addition of the SnowflakeDataSource class, which is used to read data from Snowflake using PySpark, and has methods for getting the JDBC URL, reading data with and without JDBC reader options, getting the schema, and handling exceptions. These changes were completed by Ravikumar Thangaraj and SundarShankar89.
remorph reconcile
baseline for Query Builder and Source Adapter for oracle as source (#150).
Dependency updates:
- Bump sqlglot from 22.4.0 to 22.5.0 (#175).
- Updated databricks-sdk requirement from <0.22,>=0.18 to >=0.18,<0.23 (#178).
- Updated databricks-sdk requirement from <0.23,>=0.18 to >=0.18,<0.24 (#189).
- Bump actions/checkout from 3 to 4 (#203).
- Bump actions/setup-python from 4 to 5 (#201).
- Bump codecov/codecov-action from 1 to 4 (#202).
- Bump softprops/action-gh-release from 1 to 2 (#204).
Contributors: @dependabot[bot], @sundarshankar89, @ganeshdogiparthi-db, @vijaypavann-db, @bishwajit-db, @ravit-db, @nfx
v0.1.5
- Added Pylint Checker (#149). This diff adds a Pylint checker to the project, which is used to enforce a consistent code style, identify potential bugs, and check for errors in the Python code. The configuration for Pylint includes various settings, such as a line length limit, the maximum number of arguments for a function, and the maximum number of lines in a module. Additionally, several plugins have been specified to load, which add additional checks and features to Pylint. The configuration also includes settings that customize the behavior of Pylint's naming conventions checks and handle various types of code constructs, such as exceptions, logging statements, and import statements. By using Pylint, the project can help ensure that its code is of high quality, easy to understand, and free of bugs. This diff includes changes to various files, such as cli.py, morph_status.py, validate.py, and several SQL-related files, to ensure that they adhere to the desired Pylint configuration and best practices for code quality and organization.
- Fixed edge case where column name is same as alias name (#164). A recent commit has introduced fixes for edge cases related to conflicts between column names and alias names in SQL queries, addressing issues #164 and #130. The
check_for_unsupported_lca
function has been updated with two helper functions_find_aliases_in_select
and_find_invalid_lca_in_window
to detect aliases with the same name as a column in a SELECT expression and identify invalid Least Common Ancestors (LCAs) in window functions, respectively. Thefind_windows_in_select
function has been refactored and renamed to_find_windows_in_select
for improved code readability. Thetranspile
andparse
functions in thesql_transpiler.py
file have been updated with try-except blocks to handle cases where a column name matches the alias name, preventing errors or exceptions such asParseError
,TokenError
, andUnsupportedError
. A new unit test, "test_query_with_same_alias_and_column_name", has been added to verify the fix, passing a SQL query with a subquery having a column aliasca_zip
which is also used as a column name in the same query, confirming that the function correctly handles the scenario where a column name conflicts with an alias name. TO_NUMBER
withoutformat
edge case (#172). TheTO_NUMBER without format edge case
commit introduces changes to address an unsupported usage of theTO_NUMBER
function in Databicks SQL dialect when theformat
parameter is not provided. The new implementation introduces constantsPRECISION_CONST
andSCALE_CONST
(set to 38 and 0 respectively) as default values forprecision
andscale
parameters. These changes ensure Databricks SQL dialect requirements are met by modifying the_to_number
method to incorporate these constants. AnUnsupportedError
will now be raised whenTO_NUMBER
is called without aformat
parameter, improving error handling and ensuring users are aware of the requiredformat
parameter. Test cases have been added forTO_DECIMAL
,TO_NUMERIC
, andTO_NUMBER
functions with format strings, covering cases where the format is taken from table columns. The commit also ensures that an error is raised whenTO_DECIMAL
is called without a format parameter.
Dependency updates:
- Bump sqlglot from 21.2.1 to 22.0.1 (#152).
- Bump sqlglot from 22.0.1 to 22.1.1 (#159).
- Updated databricks-labs-blueprint[yaml] requirement from ~=0.2.3 to >=0.2.3,<0.4.0 (#162).
- Bump sqlglot from 22.1.1 to 22.2.0 (#161).
- Bump sqlglot from 22.2.0 to 22.2.1 (#163).
- Updated databricks-sdk requirement from <0.21,>=0.18 to >=0.18,<0.22 (#168).
- Bump sqlglot from 22.2.1 to 22.3.1 (#170).
- Updated databricks-labs-blueprint[yaml] requirement from <0.4.0,>=0.2.3 to >=0.2.3,<0.5.0 (#171).
- Bump sqlglot from 22.3.1 to 22.4.0 (#173).
Contributors: @dependabot[bot], @sundarshankar89, @bishwajit-db
v0.1.4
- Added conversion logic for Try_to_Decimal without format (#142).
- Identify Root Table for folder containing SQLs (#124).
- Install Script (#106).
- Integration Test Suite (#145).
Dependency updates:
- Updated databricks-sdk requirement from <0.20,>=0.18 to >=0.18,<0.21 (#143).
- Bump sqlglot from 21.0.0 to 21.1.2 (#137).
- Bump sqlglot from 21.1.2 to 21.2.0 (#147).
- Bump sqlglot from 21.2.0 to 21.2.1 (#148).
Contributors: @dependabot[bot], @nfx, @sundarshankar89, @vijaypavann-db, @derekyidd, @bishwajit-db
v0.1.3
- Added support for WITHIN GROUP for ARRAY_AGG and LISTAGG functions (#133).
- Fixed Merge "INTO" for delete from syntax (#129).
- Fixed
DATE TRUNC
parse errors (#131). - Patched Logger function call during wheel file (#135).
- Patched extra call to root path (#126).
Dependency updates:
- Updated databricks-sdk requirement from ~=0.18.0 to >=0.18,<0.20 (#134).
Contributors: @sundarshankar89, @dependabot[bot], @bishwajit-db, @nsenno-dbr
v0.1.2
Dependency updates:
Contributors: @dependabot[bot], @bishwajit-db
v0.1.1
- Added test_approx_percentile and test_trunc Testcases (#98).
- Updated contributing/developer guide (#97).
Contributors: @sundarshankar89, @vijaypavann-db
v0.1.0
- Added baseline for Databricks CLI frontend (#60).
- Added custom Databricks dialect test cases and lateral struct parsing (#77).
- Extended Snowflake to Databricks functions coverage (#72, #69).
- Added
databricks labs remorph transpile
documentation for installation and usage (#73).
Dependency updates:
- Bump sqlglot from 20.8.0 to 20.9.0 (#83).
- Updated databricks-sdk requirement from ~=0.17.0 to ~=0.18.0 (#90).
- Bump sqlglot from 20.9.0 to 20.10.0 (#91).
Contributors: @bishwajit-db, @sundarshankar89, @nfx, @vijaypavann-db, @ganeshdogiparthi-db, @dependabot[bot], @nsenno-dbr