Amundsen Databuilder

Amundsen Databuilder is a ETL framework designed to build data from Amundsen. You could use the library either with an adhoc python script(example) or inside an Apache Airflow DAG(example).

Requirements

Python = 2.7.x (And Python >= 3.x if you don't use column usage transformer as it depends on antlr python 2 runtime)

Concept

ETL job consists of extraction of records from the source, transform records, if necessary, and load records into the sink. Amundsen Databuilder is a ETL framework for Amundsen and there are corresponding components for ETL called Extractor, Transformer, and Loader that deals with record level operation. A component called task controls all these three components. Job is the highest level component in Databuilder that controls task and publisher and is the one that client use to launch the ETL job.

In Databuilder, each components are highly modularized and each components are using namespace based config, HOCON config, which makes it highly reusable and pluggable. (e.g: transformer can be reused within extractor, or extractor can be reused within extractor) (Note that concept on components are highly motivated by Apache Gobblin)

Extractor

Extractor extracts record from the source. This does not neccessarily mean that it only supports pull pattern in ETL. For example, extracting record from messaging bus make it a push pattern in ETL.

Transformer

Transfomer takes record from either extractor or from transformer itself (via ChainedTransformer) to transform record.

Loader

A loader takes record from transformer or from extractor directly and load it to sink, or staging area. As loader is operated in record level, it's not capable of supporting atomicity.

Task

A task orchestrates extractor, transformer, and loader to perform record level operation.

Record

A record is represented by one of models.

Publisher

A publisher is an optional component. It's common usage is to support atomicity in job level and/or to easily support bulk load into the sink.

Job

Job is the highest level component in Databuilder, and it orchestrates task, and publisher.

List of extractors

DBAPIExtractor

An extractor that uses Python Database API interface. DBAPI requires three information, connection object that conforms DBAPI spec, a SELECT SQL statement, and a model class that correspond to the output of each row of SQL statement.

job_config = ConfigFactory.from_dict({
		'extractor.dbapi{}'.format(DBAPIExtractor.CONNECTION_CONFIG_KEY): db_api_conn,
		'extractor.dbapi.{}'.format(DBAPIExtractor.SQL_CONFIG_KEY ): select_sql_stmt,
		'extractor.dbapi.model_class': 'package.module_name.class_name'
		})

job = DefaultJob(
	conf=job_config,
	task=DefaultTask(
		extractor=DBAPIExtractor(),
		loader=AnyLoader()))
job.launch()

GenericExtractor

An extractor that takes list of dict from user through config.

HiveTableLastUpdatedExtractor

An extractor that extracts last updated time from Hive metastore and underlying file system. Although, hive metastore as a parameter called "last_modified_time", but it cannot be used as it provides DDL timestamp not DML timestamp. For this reason, HiveTableLastUpdatedExtractor is utilizing underlying file of Hive to fetch latest updated date. However, it is not efficient to poke all files in Hive, and it only pokes underlying storage for non-partitioned table. For partitioned table, it will fetch partition created timestamp, and it's close enough for last updated timestamp.

As getting metadata from files could be time consuming there're several features to increase performance.

Support of multithreading to parallelize metadata fetching. Although, cpython's multithreading is not true multithreading as it's bounded by single core, getting metadata of file is mostly IO bound operation. Note that number of threads should be less or equal to number of connections.
User can pass where clause to only include certain schema and also remove certain tables. For example, by adding something like TBL_NAME NOT REGEXP '(tmp|temp) would eliminate unncecessary computation.

job_config = ConfigFactory.from_dict({
	'extractor.hive_table_last_updated.partitioned_table_where_clause_suffix': partitioned_table_where_clause,
	'extractor.hive_table_last_updated.non_partitioned_table_where_clause_suffix'): non_partitioned_table_where_clause,
	'extractor.hive_table_last_updated.extractor.sqlalchemy.{}'.format(
            SQLAlchemyExtractor.CONN_STRING): connection_string,
	'extractor.hive_table_last_updated.extractor.fs_worker_pool_size': pool_size,
	'extractor.hive_table_last_updated.filesystem.{}'.format(FileSystem.DASK_FILE_SYSTEM): s3fs.S3FileSystem(
		anon=False,
		config_kwargs={'max_pool_connections': pool_size})})
job = DefaultJob(
	conf=job_config,
	task=DefaultTask(
		extractor=HiveTableLastUpdatedExtractor(),
		loader=AnyLoader()))
job.launch()

HiveTableMetadataExtractor

An extractor that extracts table and column metadata including database, schema, table name, table description, column name and column description from Hive metastore database.

job_config = ConfigFactory.from_dict({
	'extractor.hive_table_metadata.{}'.format(HiveTableMetadataExtractor.WHERE_CLAUSE_SUFFIX_KEY): where_clause_suffix,
	'extractor.hive_table_metadata.extractor.sqlalchemy.{}'.format(SQLAlchemyExtractor.CONN_STRING): connection_string()})
job = DefaultJob(
	conf=job_config,
	task=DefaultTask(
		extractor=HiveTableMetadataExtractor(),
		loader=AnyLoader()))
job.launch()

Neo4jEsLastUpdatedExtractor

An extractor that basically get current timestamp and passes it GenericExtractor. This extractor is basically being used to create timestamp for "Amundsen was last indexed on ..." in Amundsen web page's footer.

Neo4jExtractor

An extractor that extracts records from Neo4j based on provided Cypher query. One example is to extract data from Neo4j so that it can transform and publish to Elasticsearch.

job_config = ConfigFactory.from_dict({
	'extractor.neo4j.{}'.format(Neo4jExtractor.CYPHER_QUERY_CONFIG_KEY)': cypher_query,
	'extractor.neo4j.{}'.format(Neo4jExtractor.GRAPH_URL_CONFIG_KEY): neo4j_endpoint,
	'extractor.neo4j.{}'.format(Neo4jExtractor.MODEL_CLASS_CONFIG_KEY): 'package.module.class_name',
	'extractor.neo4j.{}'.format(Neo4jExtractor.NEO4J_AUTH_USER): neo4j_user,
	'extractor.neo4j.{}'.format(Neo4jExtractor.NEO4J_AUTH_PW): neo4j_password})
job = DefaultJob(
	conf=job_config,
	task=DefaultTask(
		extractor=Neo4jExtractor(),
		loader=AnyLoader()))
job.launch()

Neo4jSearchDataExtractor

An extractor that is extracting Neo4j utilizing Neo4jExtractor where CYPHER query is already embedded in it.

job_config = ConfigFactory.from_dict({
	'extractor.search_data.extractor.neo4j.{}'.format(Neo4jExtractor.GRAPH_URL_CONFIG_KEY): neo4j_endpoint,
	'extractor.search_data.extractor.neo4j.{}'.format(Neo4jExtractor.MODEL_CLASS_CONFIG_KEY): 'databuilder.models.neo4j_data.Neo4jDataResult',
	'extractor.search_data.extractor.neo4j.{}'.format(Neo4jExtractor.NEO4J_AUTH_USER): neo4j_user,
	'extractor.search_data.extractor.neo4j.{}'.format(Neo4jExtractor.NEO4J_AUTH_PW): neo4j_password})
job = DefaultJob(
	conf=job_config,
	task=DefaultTask(
		extractor=Neo4jSearchDataExtractor(),
		loader=AnyLoader()))
job.launch()

SQLAlchemyExtractor

An extractor utilizes SQLAlchemy to extract record from any database that support SQL Alchemy.

job_config = ConfigFactory.from_dict({
	'extractor.sqlalchemy.{}'.format(SQLAlchemyExtractor.CONN_STRING): connection_string(),
	'extractor.sqlalchemy.{}'.format(SQLAlchemyExtractor.EXTRACT_SQL): sql,
	'extractor.sqlalchemy.model_class': 'package.module.class_name'})
job = DefaultJob(
	conf=job_config,
	task=DefaultTask(
		extractor=SQLAlchemyExtractor(),
		loader=AnyLoader()))
job.launch()

TblColUsgAggExtractor

An extractor that extracts table usage from SQL statements. It accept any extractor that extracts row from source that has SQL audit log. Once SQL statement is extracted, it uses ANTLR to parse and get tables and columns that it reads from. Also, it aggregates usage based on table and user. (Column level aggregation is not there yet.)

List of transformers

ChainedTransformer

A chanined transformer that can take multiple transformer.

ElasticsearchDocumentTransformer

A transformer that transform Neo4j record to Elasticserach document.

RegexStrReplaceTransformer

Generic string replacement transformer using REGEX. User can pass list of tuples where tuple contains regex and replacement pair.

job_config = ConfigFactory.from_dict({
	'transformer.regex_str_replace.{}'.format(REGEX_REPLACE_TUPLE_LIST): [(',', ' '), ('"', '')],
	'transformer.regex_str_replace.{}'.format(ATTRIBUTE_NAME): 'instance_field_name',})

job = DefaultJob(
	conf=job_config,
	task=DefaultTask(
		extractor=AnyExtractor(),
		transformer=RegexStrReplaceTransformer(),
		loader=AnyLoader()))
job.launch()

SqlToTblColUsageTransformer

A SQL to usage transformer where it transforms to ColumnReader that has column, user, count. Currently it's collects on table level that column on same table will be de-duped. In many cases, "from" clause does not contain schema and this will be fetched via table name -> schema name mapping which it gets from metadata extractor.

List of loader

FsNeo4jCSVLoader

Write node and relationship CSV file(s) that can be consumed by Neo4jCsvPublisher. It assumes that the record it consumes is instance of Neo4jCsvSerializable.

job_config = ConfigFactory.from_dict({
	'loader.filesystem_csv_neo4j.{}'.format(FsNeo4jCSVLoader.NODE_DIR_PATH): node_files_folder,
	'loader.filesystem_csv_neo4j.{}'.format(FsNeo4jCSVLoader.RELATION_DIR_PATH): relationship_files_folder},)

job = DefaultJob(
	conf=job_config,
	task=DefaultTask(
		extractor=AnyExtractor(),
		loader=FsNeo4jCSVLoader()),
	publisher=Neo4jCsvPublisher())
job.launch()

FSElasticsearchJSONLoader

Write Elasticsearch document in JSON format which can be consumed by ElasticsearchPublisher. It assumes that the record it consumes is instance of ElasticsearchDocument.

tmp_folder = '/var/tmp/amundsen/dummy_metadata'
node_files_folder = '{tmp_folder}/nodes/'.format(tmp_folder=tmp_folder)
relationship_files_folder = '{tmp_folder}/relationships/'.format(tmp_folder=tmp_folder)

job_config = ConfigFactory.from_dict({
	'loader.filesystem.elasticsearch.{}'.format(FSElasticsearchJSONLoader.FILE_PATH_CONFIG_KEY): data_file_path,
	'loader.filesystem.elasticsearch.{}'.format(FSElasticsearchJSONLoader.FILE_MODE_CONFIG_KEY): 'w',})

job = DefaultJob(
	conf=job_config,
	task=DefaultTask(
		extractor=AnyExtractor(),
		loader=FSElasticsearchJSONLoader()),
	publisher=ElasticsearchPublisher())
job.launch()

List of publisher

Neo4jCsvPublisher

A Publisher takes two folders for input and publishes to Neo4j. One folder will contain CSV file(s) for Node where the other folder will contain CSV file(s) for Relationship. Neo4j follows Label Node properties Graph and refer to here for more information

job_config = ConfigFactory.from_dict({
	'loader.filesystem_csv_neo4j.{}'.format(FsNeo4jCSVLoader.NODE_DIR_PATH): node_files_folder,
	'loader.filesystem_csv_neo4j.{}'.format(FsNeo4jCSVLoader.RELATION_DIR_PATH): relationship_files_folder,
	'publisher.neo4j.{}'.format(neo4j_csv_publisher.NODE_FILES_DIR): node_files_folder,
	'publisher.neo4j.{}'.format(neo4j_csv_publisher.RELATION_FILES_DIR): relationship_files_folder,
	'publisher.neo4j.{}'.format(neo4j_csv_publisher.NEO4J_END_POINT_KEY): neo4j_endpoint,
	'publisher.neo4j.{}'.format(neo4j_csv_publisher.NEO4J_USER): neo4j_user,
	'publisher.neo4j.{}'.format(neo4j_csv_publisher.NEO4J_PASSWORD): neo4j_password,})

job = DefaultJob(
	conf=job_config,
	task=DefaultTask(
		extractor=AnyExtractor(),
		loader=FsNeo4jCSVLoader()),
	publisher=Neo4jCsvPublisher())
job.launch()

ElasticsearchPublisher

Elasticsearch Publisher uses Bulk API to load data from JSON file. Elasticsearch publisher supports atomic operation by utilizing alias in Elasticsearch. A new index is created and data is uploaded into it. After the upload is complete, index alias is swapped to point to new index from old index and traffic is routed to new index.

tmp_folder = '/var/tmp/amundsen/dummy_metadata'
node_files_folder = '{tmp_folder}/nodes/'.format(tmp_folder=tmp_folder)
relationship_files_folder = '{tmp_folder}/relationships/'.format(tmp_folder=tmp_folder)

job_config = ConfigFactory.from_dict({
	'loader.filesystem.elasticsearch.{}'.format(FSElasticsearchJSONLoader.FILE_PATH_CONFIG_KEY): data_file_path,
	'loader.filesystem.elasticsearch.{}'.format(FSElasticsearchJSONLoader.FILE_MODE_CONFIG_KEY): 'w',
	'publisher.elasticsearch.{}'.format(ElasticsearchPublisher.FILE_PATH_CONFIG_KEY): data_file_path,
	'publisher.elasticsearch.{}'.format(ElasticsearchPublisher.FILE_MODE_CONFIG_KEY): 'r',
	'publisher.elasticsearch{}'.format(ElasticsearchPublisher.ELASTICSEARCH_CLIENT_CONFIG_KEY): elasticsearch_client,
	'publisher.elasticsearch.{}'.format(ElasticsearchPublisher.ELASTICSEARCH_NEW_INDEX_CONFIG_KEY): elasticsearch_new_index,
	'publisher.elasticsearch.{}'.format(ElasticsearchPublisher.ELASTICSEARCH_ALIAS_CONFIG_KEY): elasticsearch_index_alias,)

job = DefaultJob(
	conf=job_config,
	task=DefaultTask(
		extractor=AnyExtractor(),
		loader=FSElasticsearchJSONLoader()),
	publisher=ElasticsearchPublisher())
job.launch()

jtneill / amundsendatabuilder