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Posted to issues@iceberg.apache.org by GitBox <gi...@apache.org> on 2021/05/07 00:41:24 UTC
[GitHub] [iceberg] openinx commented on a change in pull request #2544: Doc: refactor Hive documentation with catalog loading examples
openinx commented on a change in pull request #2544:
URL: https://github.com/apache/iceberg/pull/2544#discussion_r627850521
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File path: site/docs/hive.md
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@@ -17,117 +17,324 @@
# Hive
-## Hive read support
-Iceberg supports the reading of Iceberg tables from [Hive](https://hive.apache.org) by using a [StorageHandler](https://cwiki.apache.org/confluence/display/Hive/StorageHandlers). Please note that only Hive 2.x versions are currently supported.
+Iceberg supports reading and writing Iceberg tables through [Hive](https://hive.apache.org) by using a [StorageHandler](https://cwiki.apache.org/confluence/display/Hive/StorageHandlers).
+Here is the current compatibility matrix for Iceberg Hive support:
-### Table creation
-This section explains the various steps needed in order to overlay a Hive table "on top of" an existing Iceberg table. Iceberg tables are created using either a [`Catalog`](./javadoc/master/index.html?org/apache/iceberg/catalog/Catalog.html) or an implementation of the [`Tables`](./javadoc/master/index.html?org/apache/iceberg/Tables.html) interface and Hive needs to be configured accordingly to read data from these different types of table.
+| Feature | Hive 2.x | Hive 3.1.2 |
+| ------------------------ | ---------------------- | ---------------------- |
+| CREATE EXTERNAL TABLE | ✔️ | ✔️ |
+| CREATE TABLE | ✔️ | ✔️ |
+| DROP TABLE | ✔️ | ✔️ |
+| SELECT | ✔️ (MapReduce and Tez) | ✔️ (MapReduce and Tez) |
+| INSERT INTO | ✔️ (MapReduce only)️ | ✔️ (MapReduce only) |
-#### Add the Iceberg Hive Runtime jar file to the Hive classpath
-Regardless of the table type, the `HiveIcebergStorageHandler` and supporting classes need to be made available on Hive's classpath. These are provided by the `iceberg-hive-runtime` jar file. For example, if using the Hive shell, this can be achieved by issuing a statement like so:
-```sql
+## Enabling Iceberg support in Hive
+
+### Loading runtime jar
+
+To enable Iceberg support in Hive, the `HiveIcebergStorageHandler` and supporting classes need to be made available on Hive's classpath.
+These are provided by the `iceberg-hive-runtime` jar file.
+For example, if using the Hive shell, this can be achieved by issuing a statement like so:
+
+```
add jar /path/to/iceberg-hive-runtime.jar;
```
-There are many others ways to achieve this including adding the jar file to Hive's auxiliary classpath (so it is available by default) - please refer to Hive's documentation for more information.
-#### Using Hadoop Tables
-Iceberg tables created using `HadoopTables` are stored entirely in a directory in a filesystem like HDFS.
+There are many others ways to achieve this including adding the jar file to Hive's auxiliary classpath so it is available by default.
+Please refer to Hive's documentation for more information.
-##### Create an Iceberg table
-The first step is to create an Iceberg table using the Spark/Java/Python API and `HadoopTables`. For the purposes of this documentation we will assume that the table is called `table_a` and that the table location is `hdfs://some_path/table_a`.
+### Enabling support
-##### Create a Hive table
-Now overlay a Hive table on top of this Iceberg table by issuing Hive DDL like so:
-```sql
-CREATE EXTERNAL TABLE table_a
-STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
-LOCATION 'hdfs://some_bucket/some_path/table_a';
+If the Iceberg storage handler is not in Hive's classpath, then Hive cannot load or update the metadata for an Iceberg table when the storage handler is set.
+To avoid the appearance of broken tables in Hive, Iceberg will not add the storage handler to a table unless Hive support is enabled.
+The storage handler is kept in sync (added or removed) every time a table is updated.
+There are two ways to enable Hive support: globally in Hadoop Configuration and per-table using a table property.
+
+#### Hadoop configuration
+
+To enable Hive support globally for an application, set `iceberg.engine.hive.enabled=true` in its Hadoop configuration.
+For example, setting this in the `hive-site.xml` loaded by Spark will enable the storage handler for all tables created by Spark.
+
+!!! Warning
+ When using Tez, you also have to disable vectorization for now (`hive.vectorized.execution.enabled=false`)
+
+#### Table property configuration
+
+Alternatively, the property `engine.hive.enabled` can be set to `true` and added to the table properties when creating the Iceberg table.
+Here is an example of doing it programmatically:
+
+```java
+Catalog catalog = ...;
+Map<String, String> tableProperties = Maps.newHashMap();
+tableProperties.put(TableProperties.ENGINE_HIVE_ENABLED, "true"); // engine.hive.enabled=true
+catalog.createTable(tableId, schema, spec, tableProperties);
```
-#### Query the Iceberg table via Hive
-You should now be able to issue Hive SQL `SELECT` queries using the above table and see the results returned from the underlying Iceberg table.
-```sql
-SELECT * from table_a;
+The table level configuration overrides the global Hadoop configuration.
+
+## Catalog Management
+
+### Global Hive catalog
+
+From the Hive engine's perspective, there is only one global data catalog that is defined in the Hadoop configuration in the runtime environment.
+On contrast, Iceberg supports multiple different data catalog types such as Hive, Hadoop, AWS Glue, or custom catalog implementations.
+Iceberg also allows loading a table directly based on its path in the file system. Those tables do not belong to any catalog.
+Users might want to read these cross-catalog and path-based tables through the Hive engine for use cases like join.
+
+To support this, a table in the Hive metastore can represent three different ways of loading an Iceberg table,
+depending on the table's `iceberg.catalog` property:
+
+1. The table will be loaded using a `HiveCatalog` that corresponds to the metastore configured in the Hive environment if no `iceberg.catalog` is set
+2. The table will be loaded using a custom catalog if `iceberg.catalog` is set to a catalog name (see below)
+3. The table can be loaded directly using the table's root location if `iceberg.catalog` is set to `location_based_table`
+
+For cases 2 and 3 above, users can create an overlay of an Iceberg table in the Hive metastore,
+so that different table types can work together in the same Hive environment.
+See [CREATE EXTERNAL TABLE](#create-external-table) for more details.
+
+### Custom Iceberg catalogs
+
+To globally register different catalogs, set the following Hadoop configurations:
+
+| Config Key | Description |
+| --------------------------------------------- | ------------------------------------------------------ |
+| iceberg.catalog.<catalog_name\>.type | type of catalog: `hive`,`hadoop` or `custom` |
+| iceberg.catalog.<catalog_name\>.catalog-impl | catalog implementation, must not be null if type is `custom` |
+| iceberg.catalog.<catalog_name\>.<key\> | any config key and value pairs for the catalog |
+
+Here are some examples using Hive CLI:
+
+Register a `HiveCatalog` called `another_hive`:
+
```
+SET iceberg.catalog.another_hive.type=hive;
+SET iceberg.catalog.another_hive.uri=thrift://example.com:9083;
+SET iceberg.catalog.another_hive.clients=10;
+SET iceberg.catalog.another_hive.warehouse=hdfs://example.com:8020/warehouse;
+```
+
+Register a `HadoopCatalog` called `hadoop`:
-#### Using Hive Catalog
-Iceberg tables created using `HiveCatalog` are automatically registered with Hive.
+```
+SET iceberg.catalog.hadoop.type=hadoop;
+SET iceberg.catalog.hadoop.warehouse=hdfs://example.com:8020/warehouse;
+```
-##### Create an Iceberg table
-The first step is to create an Iceberg table using the Spark/Java/Python API and `HiveCatalog`. For the purposes of this documentation we will assume that the table is called `table_b` and that the table location is `s3://some_path/table_b`. In order for Iceberg to correctly set up the Hive table for querying some configuration values need to be set, the two options for this are described below - you can use either or the other depending on your use case.
+Register an AWS `GlueCatalog` called `glue`:
-##### Hive Configuration
-The value `iceberg.engine.hive.enabled` needs to be set to `true` and added to the Hive configuration file on the classpath of the application creating or modifying (altering, inserting etc.) the table. This can be done by modifying the relevant `hive-site.xml`. Alternatively this can be done programmatically like so:
-```java
-Configuration hadoopConfiguration = spark.sparkContext().hadoopConfiguration();
-hadoopConfiguration.set(ConfigProperties.ENGINE_HIVE_ENABLED, "true"); //iceberg.engine.hive.enabled=true
-HiveCatalog catalog = new HiveCatalog(hadoopConfiguration);
-...
-catalog.createTable(tableId, schema, spec);
+```
+SET iceberg.catalog.glue.type=custom;
Review comment:
How about adding the line `SET iceberg.catalog=glue;` in this custom catalog example ? Because I think people would prefer to looking for a full example to make the demo run, according to this [part](https://github.com/apache/iceberg/pull/2544/files#diff-0270f04c6c1a4be5da895415fff2797103da7ded6ec97c303f2f7e218e99ac26R88).
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