| commit | 44a1e2e0ac37da096724d6bfae6c1ed87d7e1a16 | [log] [tgz] |
|---|---|---|
| author | Sagar Lakshmipathy <18vidhyasagar@gmail.com> | Sat Nov 25 11:47:04 2023 -0800 |
| committer | Sagar Lakshmipathy <18vidhyasagar@gmail.com> | Sat Nov 25 11:47:04 2023 -0800 |
| tree | 618ab4173d5dc95d7a74c0ffb40a279cbed7736e | |
| parent | 71131f33ebb382e541200502ba489dd994676d05 [diff] |
added and clarified docs
OneTable is an omni-directional converter for table formats that facilitates interoperability across data processing systems and query engines. Currently, OneTable supports widely adopted open-source table formats such as Apache Hudi, Apache Iceberg, and Delta Lake.
OneTable simplifies data lake operations by leveraging a common model for table representation. This allows users to write data in one format while still benefiting from integrations and features available in other formats. For instance, OneTable enables existing Hudi users to seamlessly work with Databricks's Photon Engine or query Iceberg tables with Snowflake. Creating transformations from one format to another is straightforward and only requires the implementation of a few interfaces, which we believe will facilitate the expansion of supported source and target formats in the future.
mvn clean package. Use mvn clean package -DskipTests to skip tests while building.mvn clean test or mvn test to run all unit tests. If you need to run only a specific test you can do this by something like mvn test -Dtest=TestDeltaSync -pl core.mvn clean verify or mvn verify to run integration tests.mvn spotless:check to find out code style violations and mvn spotless:apply to fix them. Code style check is tied to compile phase by default, so code style violations will lead to build failures.mvn install -DskipTestssourceFormat: HUDI targetFormats: - DELTA - ICEBERG datasets: - tableBasePath: s3://tpc-ds-datasets/1GB/hudi/call_center tableDataPath: s3://tpc-ds-datasets/1GB/hudi/call_center/data tableName: call_center namespace: my.db - tableBasePath: s3://tpc-ds-datasets/1GB/hudi/catalog_sales tableName: catalog_sales partitionSpec: cs_sold_date_sk:VALUE - tableBasePath: s3://hudi/multi-partition-dataset tableName: multi_partition_dataset partitionSpec: time_millis:DAY:yyyy-MM-dd,type:VALUE - tableBasePath: abfs://container@storage.dfs.core.windows.net/multi-partition-dataset tableName: multi_partition_dataset
tableFormats is a list of formats you want to create from your source tablestableBasePath is the basePath of the tabletableDataPath is an optional field specifying the path to the data files. If not specified, the tableBasePath will be used. For Iceberg source tables, you will need to specify the /data path.namespace is an optional field specifying the namespace of the table and will be used when syncing to a catalog.partitionSpec is a spec that allows us to infer partition values. This is only required for Hudi source tables. If the table is not partitioned, leave it blank. If it is partitioned, you can specify a spec with a comma separated list with format path:type:formatpath is a dot separated path to the partition fieldtype describes how the partition value was generated from the column valueVALUE: an identity transform of field value to partition valueYEAR: data is partitioned by a field representing a date and year granularity is usedMONTH: same as YEAR but with month granularityDAY: same as YEAR but with day granularityHOUR: same as YEAR but with hour granularityformat: if your partition type is YEAR, MONTH, DAY, or HOUR specify the format for the date string as it appears in your file paths# sourceClientProviderClass: The class name of a table format's client factory, where the client is # used for reading from a table of this format. All user configurations, including hadoop config # and client specific configuration, will be available to the factory for instantiation of the # client. # targetClientProviderClass: The class name of a table format's client factory, where the client is # used for writing to a table of this format. # configuration: A map of configuration values specific to this client. tableFormatsClients: HUDI: sourceClientProviderClass: io.onetable.hudi.HudiSourceClientProvider DELTA: targetClientProviderClass: io.onetable.delta.DeltaClient configuration: spark.master: local[2] spark.app.name: onetableclient
--icebergCatalogConfig option. The format of the catalog config file is:catalogImpl: io.my.CatalogImpl catalogName: name catalogOptions: # all other options are passed through in a map key1: value1 key2: value2
java -jar utilities/target/utilities-0.1.0-SNAPSHOT-bundled.jar --datasetConfig my_config.yaml [--hadoopConfig hdfs-site.xml] [--clientsConfig clients.yaml] [--icebergCatalogConfig catalog.yaml] The bundled jar includes hadoop dependencies for AWS, Azure, and GCP. Authentication for AWS is done with com.amazonaws.auth.DefaultAWSCredentialsProviderChain. To override this setting, specify a different implementation with the --awsCredentialsProvider option.For setting up the repo on IntelliJ, open the project and change the java version to Java11 in File->ProjectStructure 
You have found a bug, or have a cool idea you that want to contribute to the project ? Please file a GitHub issue here
Adding a new target format requires a developer implement TargetClient. Once you have implemented that interface, you can integrate it into the OneTableClient. If you think others may find that target useful, please raise a Pull Request to add it to the project.
