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commit 29918115b08d4878284c6ab6bf1f96646cdbec83 Author: Kyle Weller <[email protected]> AuthorDate: Thu Mar 7 23:40:32 2024 -0800 removing unnecessary new lines --- README.md | 46 ++++++++++++++++++++-------------------------- 1 file changed, 20 insertions(+), 26 deletions(-) diff --git a/README.md b/README.md index cc81f618..ee57016d 100644 --- a/README.md +++ b/README.md @@ -14,18 +14,17 @@ of a few interfaces, which we believe will facilitate the expansion of supported future. # Building the project and running tests. -1. Use Java11 for building the project. If you are using some other java version, you can - use [jenv](https://github.com/jenv/jenv) to use multiple java versions locally. +1. Use Java11 for building the project. If you are using some other java version, you can use [jenv](https://github.com/jenv/jenv) to use multiple java versions locally. 2. Build the project using `mvn clean package`. Use `mvn clean package -DskipTests` to skip tests while building. -3. Use `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`. +3. Use `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`. 4. Similarly, use `mvn clean verify` or `mvn verify` to run integration tests. # Style guide -1. We use [Maven Spotless plugin](https://github.com/diffplug/spotless/tree/main/plugin-maven) and +1. We use [Maven Spotless plugin](https://github.com/diffplug/spotless/tree/main/plugin-maven) and [Google java format](https://github.com/google/google-java-format) for code style. -2. Use `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. +2. Use `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. # Running the bundled jar 1. Get a pre-built bundled jar or create the jar with `mvn install -DskipTests` @@ -36,17 +35,21 @@ targetFormats: - DELTA - ICEBERG datasets: - - tableBasePath: s3://tpc-ds-datasets/1GB/hudi/call_center + - + 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 + - + tableBasePath: s3://tpc-ds-datasets/1GB/hudi/catalog_sales tableName: catalog_sales partitionSpec: cs_sold_date_sk:VALUE - - tableBasePath: s3://hudi/multi-partition-dataset + - + tableBasePath: s3://hudi/multi-partition-dataset tableName: multi_partition_dataset partitionSpec: time_millis:DAY:yyyy-MM-dd,type:VALUE - - tableBasePath: abfs://[email protected]/multi-partition-dataset + - + tableBasePath: abfs://[email protected]/multi-partition-dataset tableName: multi_partition_dataset ``` - `sourceFormat` is the format of the source table that you want to convert @@ -54,9 +57,7 @@ datasets: - `tableBasePath` is the basePath of the table - `tableDataPath` 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:format` +- `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:format` - `path` is a dot separated path to the partition field - `type` describes how the partition value was generated from the column value - `VALUE`: an identity transform of field value to partition value @@ -64,10 +65,8 @@ datasets: - `MONTH`: same as `YEAR` but with month granularity - `DAY`: same as `YEAR` but with day granularity - `HOUR`: same as `YEAR` but with hour granularity - - `format`: if your partition type is `YEAR`, `MONTH`, `DAY`, or `HOUR` specify the format for the date string as it - appears in your file paths -3. The default implementations of table format clients can be replaced with custom implementations by specifying a - client configs yaml file in the format below: + - `format`: if your partition type is `YEAR`, `MONTH`, `DAY`, or `HOUR` specify the format for the date string as it appears in your file paths +3. The default implementations of table format clients can be replaced with custom implementations by specifying a client configs yaml file in the format below: ```yaml # 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 @@ -85,8 +84,7 @@ tableFormatsClients: spark.master: local[2] spark.app.name: onetableclient ``` -4. A catalog can be used when reading and updating Iceberg tables. The catalog can be specified in a yaml file and - passed in with the `--icebergCatalogConfig` option. The format of the catalog config file is: +4. A catalog can be used when reading and updating Iceberg tables. The catalog can be specified in a yaml file and passed in with the `--icebergCatalogConfig` option. The format of the catalog config file is: ```yaml catalogImpl: io.my.CatalogImpl catalogName: name @@ -94,8 +92,7 @@ catalogOptions: # all other options are passed through in a map key1: value1 key2: value2 ``` -5. run - with `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]` +5. run with `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. @@ -108,10 +105,7 @@ For setting up the repo on IntelliJ, open the project and change the java versio You have found a bug, or have a cool idea you that want to contribute to the project ? Please file a GitHub issue [here](https://github.com/onetable-io/onetable/issues) ## Adding a new target format -Adding a new target format requires a developer -implement [TargetClient](./api/src/main/java/io/onetable/spi/sync/TargetClient.java). Once you have implemented that -interface, you can integrate it into the [OneTableClient](./core/src/main/java/io/onetable/client/OneTableClient.java). -If you think others may find that target useful, please raise a Pull Request to add it to the project. +Adding a new target format requires a developer implement [TargetClient](./api/src/main/java/io/onetable/spi/sync/TargetClient.java). Once you have implemented that interface, you can integrate it into the [OneTableClient](./core/src/main/java/io/onetable/client/OneTableClient.java). If you think others may find that target useful, please raise a Pull Request to add it to the project. ## Overview of the sync process 
