### Build and Start Hive with Ozone-backed Storage

Source: https://github.com/apache/hive/blob/master/packaging/src/docker/README.md

Cleans up previous containers, sets the path to the PostgreSQL driver, builds Hive and Hadoop, and starts Hive with Ozone integration.

```shell
docker compose down --rmi local # cleanup previous containers and images

export POSTGRES_LOCAL_PATH=... # set the path to the postgres driver jar
./build.sh -hive 4.2.0 -hadoop 3.4.1 -tez 0.10.5 
./start-hive.sh --ozone
```

--------------------------------

### Install Hive Metastore Benchmarks

Source: https://github.com/apache/hive/blob/master/standalone-metastore/metastore-tools/metastore-benchmarks/README.md

Use this Maven command to clean, install, and run the performance profile for the metastore benchmarks.

```bash
mvn clean install -Pperf
```

--------------------------------

### IMetaStoreClient Usage Example

Source: https://context7.com/apache/hive/llms.txt

This example demonstrates common operations using IMetaStoreClient, including creating databases and tables, adding partitions, listing metadata, and fetching table details.

```APIDOC
## IMetaStoreClient: Programmatic Metastore Access

`IMetaStoreClient` provides a Java API to create, query, and manage Hive metadata (databases, tables, partitions, statistics) directly against the Hive Metastore over Thrift without going through HiveServer2.

### Example Usage

```java
import org.apache.hadoop.hive.conf.HiveConf;
import org.apache.hadoop.hive.metastore.*;
import org.apache.hadoop.hive.metastore.api.*;
import java.util.*;

public class MetastoreClientExample {
    public static void main(String[] args) throws Exception {
        HiveConf conf = new HiveConf();
        conf.set("hive.metastore.uris", "thrift://metastore-host:9083");

        try (IMetaStoreClient client = new HiveMetaStoreClient(conf)) {

            // Create a database
            Database db = new Database("analytics", "Analytics database",
                "/user/hive/warehouse/analytics.db", null);
            if (!client.getAllDatabases().contains("analytics")) {
                client.createDatabase(db);
            }

            // Create a table programmatically
            Table tbl = new Table();
            tbl.setDbName("analytics");
            tbl.setTableName("page_views");
            tbl.setTableType(TableType.MANAGED_TABLE.name());

            StorageDescriptor sd = new StorageDescriptor();
            sd.setCols(Arrays.asList(
                new FieldSchema("page_url",   "string", "Page URL"),
                new FieldSchema("user_id",    "bigint", "User identifier"),
                new FieldSchema("duration_ms","int",    "Time on page in ms")
            ));
            sd.setInputFormat("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat");
            sd.setOutputFormat("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat");
            SerDeInfo serde = new SerDeInfo();
            serde.setSerializationLib("org.apache.hadoop.hive.ql.io.orc.OrcSerde");
            sd.setSerdeInfo(serde);
            sd.setLocation("/user/hive/warehouse/analytics.db/page_views");
            tbl.setSd(sd);
            tbl.setPartitionKeys(List.of(new FieldSchema("event_date", "string", "Partition date")));

            client.createTable(tbl);

            // Add a partition
            Partition part = new Partition();
            part.setDbName("analytics");
            part.setTableName("page_views");
            part.setValues(List.of("2024-01-15"));
            StorageDescriptor partSd = sd.deepCopy();
            partSd.setLocation("/user/hive/warehouse/analytics.db/page_views/event_date=2024-01-15");
            part.setSd(partSd);
            client.add_partition(part);

            // List tables and partitions
            List<String> tables = client.getTables("analytics", ".*");
            System.out.println("Tables: " + tables);  // [page_views]

            List<Partition> partitions = client.listPartitions("analytics", "page_views", (short) 100);
            partitions.forEach(p -> System.out.println("Partition: " + p.getValues()));

            // Check table existence and fetch metadata
            boolean exists = client.tableExists("analytics", "page_views");
            System.out.println("Table exists: " + exists);  // true

            Table fetchedTbl = client.getTable("analytics", "page_views");
            System.out.println("Table location: " + fetchedTbl.getSd().getLocation());

            // Drop the table
            // client.dropTable("analytics", "page_views", true, true);
        }
    }
}
```
```

--------------------------------

### Start Hive Services with Docker Compose

Source: https://github.com/apache/hive/blob/master/packaging/src/docker/README.md

Starts HiveServer2, Metastore, and PostgreSQL services using a docker-compose.yml file. This provides a quick overview and setup for both services.

```shell
docker compose up -d
```

--------------------------------

### Start Hive Metastore

Source: https://github.com/apache/hive/blob/master/hcatalog/src/test/e2e/templeton/README.txt

Starts the Hive metastore service on the specified port. This is a prerequisite for Templeton tests.

```bash
./bin/hive --service metastore -p 9933
```

--------------------------------

### Start Templeton Server

Source: https://github.com/apache/hive/blob/master/hcatalog/src/test/e2e/templeton/README.txt

Launches the Templeton server. Ensure this is running before executing tests.

```bash
./hcatalog/sbin/webhcat_server.sh start
```

--------------------------------

### Run Example Hive Queries

Source: https://github.com/apache/hive/blob/master/packaging/src/docker/README.md

Execute a set of sample SQL queries to demonstrate basic Hive functionality.

```sql
show tables;
create table hive_example(a string, b int) partitioned by(c int);
alter table hive_example add partition(c=1);
insert into hive_example partition(c=1) values('a', 1), ('a', 2),('b',3);
select count(distinct a) from hive_example;
select sum(b) from hive_example;
```

--------------------------------

### Configure and Start Hive with S3-backed Warehouse

Source: https://github.com/apache/hive/blob/master/packaging/src/docker/README.md

Sets environment variables for S3 access and Hive warehouse path, then starts the Docker Compose services.

```shell
DEFAULT_FS="s3a://dw-team-bucket" \
HIVE_WAREHOUSE_PATH="/data/warehouse/tablespace/managed/hive" \
S3_ENDPOINT_URL="s3.us-west-2.amazonaws.com" \
docker-compose up
```

--------------------------------

### Build and Start Hive LLAP Cluster with Docker Compose

Source: https://github.com/apache/hive/blob/master/packaging/src/docker/README.md

Use this workflow to clean up previous containers, build a new Hive image, and start the cluster with LLAP daemons enabled.

```shell
docker-compose down --rmi local # cleanup previous containers and images

export POSTGRES_LOCAL_PATH=... # set the path to the postgres driver jar on the host machine
./build.sh -hive 4.2.0 -hadoop 3.4.1 -tez 0.10.5 # build image from the common Dockerfile
./start-hive.sh --llap
```

--------------------------------

### Start Services with Docker Compose

Source: https://github.com/apache/hive/blob/master/packaging/src/docker/thirdparties/polaris/README.md

Launches the Hive and Polaris services defined in the docker-compose.yml file.

```shell
docker-compose up -d
```

--------------------------------

### Launch HiveServer2 with Embedded Metastore

Source: https://github.com/apache/hive/blob/master/packaging/src/docker/README.md

Starts a Docker container running HiveServer2 with an embedded Derby Metastore. This is suitable for quick setups and testing.

```shell
docker run -d -p 10000:10000 -p 10002:10002 --env SERVICE_NAME=hiveserver2 --name hive4 apache/hive:${HIVE_VERSION}
```

--------------------------------

### Run Hive Metastore Benchmarks

Source: https://github.com/apache/hive/blob/master/standalone-metastore/metastore-tools/metastore-benchmarks/README.md

After setting the HMS_HOST environment variable, use this Maven command to install and run the performance profile.

```bash
mvn install -Pperf
```

--------------------------------

### Initialize Hive System Schemas with Schematool

Source: https://github.com/apache/hive/blob/master/packaging/src/docker/README.md

Commands to download the PostgreSQL JDBC driver, start the Docker Compose services, and then use schematool to initialize system schemas for HiveServer2.

```shell
mvn dependency:get -Dartifact=org.postgresql:postgresql:42.7.5
docker compose up -d
docker compose exec hiveserver2-standalone /bin/bash
/opt/hive/bin/schematool -initSchema -dbType hive -metaDbType postgres -url jdbc:hive2://localhost:10000/default
exit
```

--------------------------------

### Run HiveServer2 with Remote Metastore

Source: https://github.com/apache/hive/blob/master/packaging/src/docker/README.md

Starts a HiveServer2 instance configured to connect to a remote Metastore service. Options for resuming and verbose logging are included.

```shell
docker run -d -p 10000:10000 -p 10002:10002 --env SERVICE_NAME=hiveserver2 \
         --env SERVICE_OPTS="-Dhive.metastore.uris=thrift://metastore:9083" \
         --env IS_RESUME="true" \
         --env VERBOSE="true" \
         --name hiveserver2-standalone apache/hive:${HIVE_VERSION}
```

--------------------------------

### Launch Standalone Metastore

Source: https://github.com/apache/hive/blob/master/packaging/src/docker/README.md

Starts a Docker container for a standalone Metastore using Derby as the database. This is useful when you need a separate Metastore service.

```shell
docker run -d -p 9083:9083 --env SERVICE_NAME=metastore --name metastore-standalone apache/hive:${HIVE_VERSION}
```

--------------------------------

### Beeline CLI: Connect to HiveServer2

Source: https://context7.com/apache/hive/llms.txt

Examples of connecting to HiveServer2 using the Beeline CLI with different authentication methods and transport protocols, including basic, Kerberos, and HTTP.

```bash
# Connect to HiveServer2 (basic)
beeline -u "jdbc:hive2://hs2-host:10000/default" -n hiveuser -p secret

# Connect with Kerberos (no password needed)
beeline -u "jdbc:hive2://hs2-host:10000/default;principal=hive/hs2-host@REALM.COM"

# Connect via HTTP transport
beeline -u "jdbc:hive2://hs2-host:10001/default;transportMode=http;httpPath=cliservice" \
        -n hiveuser -p secret
```

--------------------------------

### Date Calculations in DB2

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/offline/select_db2.out.txt

This example shows how to perform date calculations in DB2, including adding and subtracting days from a given date. It utilizes a subquery with `sysibm.sysdummy1` for demonstration purposes.

```sql
select cd, cd + inc days, cd - inc days + coalesce(inc, 0) days
from (select date '2015-09-02' as cd, 3 as inc from sysibm.sysdummy1)
```

--------------------------------

### HPL/SQL Function Definition

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/local/create_function4.out.txt

Defines a function named GET in HPL/SQL. This is the starting point for creating reusable code blocks.

```hplsql
CREATE FUNCTION GET
```

--------------------------------

### Run All Metastore Benchmarks

Source: https://github.com/apache/hive/blob/master/standalone-metastore/metastore-tools/metastore-benchmarks/README.md

Execute all available benchmark tests with default settings. Replace `metastore_db_name` and `hostname` with your specific values.

```java
java -jar hmsbench.jar -d `metastore_db_name` -H `hostname`
```

--------------------------------

### Show All Partitions

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/db/part_count.out.txt

Use this command to display all partitions associated with a given table.

```sql
SHOW PARTITIONS partition_date_1
```

--------------------------------

### Show Partitions for Another Table

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/db/part_count.out.txt

Demonstrates showing partitions for a different table, illustrating the general syntax for partition inspection.

```sql
SHOW PARTITIONS partition_date_1a
```

--------------------------------

### Get Next ID with Exclusive Locks

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/offline/select_db2.out.txt

This snippet demonstrates how to get the next available ID from a sequence table, ensuring exclusive locks to prevent concurrent modifications. It handles the case where the table might be empty by defaulting to 0.

```sql
select coalesce(max(info_id)+1,0) into NextID from sproc_info with rr use and keep exclusive locks
```

--------------------------------

### Declare and Set Variables in HPL/SQL

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/local/mult_div.out.txt

Demonstrates how to declare integer variables and assign them initial values. Use this for setting up variables before performing operations.

```HPLSQL
DECLARE a int = 8
DECLARE b int = 4
DECLARE c int = 2
```

--------------------------------

### Export Hive Version

Source: https://github.com/apache/hive/blob/master/packaging/src/docker/thirdparties/polaris/README.md

Set the HIVE_VERSION environment variable before starting the services.

```shell
export HIVE_VERSION=4.2.0-SNAPSHOT
```

--------------------------------

### Get New Aggregation Buffer

Source: https://github.com/apache/hive/blob/master/ql/src/gen/vectorization/UDAFTemplates/VectorUDAFMinMax.txt

Instantiates a new aggregation buffer for the UDAF. This is called internally by Hive.

```Java
@Override
    public AggregationBuffer getNewAggregationBuffer() throws HiveException {
      return new Aggregation();
    }
```

--------------------------------

### Get Right Dynamic Value

Source: https://github.com/apache/hive/blob/master/ql/src/gen/vectorization/ExpressionTemplates/FilterColumnBetweenDynamicValue.txt

Accessor method to retrieve the right dynamic value used for filtering.

```Java
public DynamicValue getRightDynamicValue() {
    return rightDynamicValue;
}
```

--------------------------------

### Configure Hadoop Options for Kerberos

Source: https://github.com/apache/hive/blob/master/hcatalog/src/test/e2e/templeton/README.txt

Add Hadoop options to conf/hadoop-env.sh for Kerberos realm and KDC configuration.

```bash
export HADOOP_OPTS="-Djava.security.krb5.realm=OX.AC.UK -Djava.security.krb5.kdc=kdc0.ox.ac.uk:kdc1.ox.ac.uk"
```

--------------------------------

### Get Left Dynamic Value

Source: https://github.com/apache/hive/blob/master/ql/src/gen/vectorization/ExpressionTemplates/FilterColumnBetweenDynamicValue.txt

Accessor method to retrieve the left dynamic value used for filtering.

```Java
public DynamicValue getLeftDynamicValue() {
    return leftDynamicValue;
}
```

--------------------------------

### Reset Aggregation Buffer

Source: https://github.com/apache/hive/blob/master/ql/src/gen/vectorization/UDAFTemplates/VectorUDAFMinMax.txt

Resets the aggregation buffer to its initial state. This is called when starting a new aggregation or partition.

```Java
@Override
    public void reset(AggregationBuffer agg) throws HiveException {
      Aggregation myAgg = (Aggregation) agg;
      myAgg.reset();
    }
```

--------------------------------

### Create Database with IF NOT EXISTS and Comment

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/db/create_drop_database.out.txt

Create a database only if it does not already exist, and optionally add a comment and specify a location. This prevents errors if the database already exists and allows for metadata.

```sql
create database if not exists test1 comment 'abc' location '/users'
```

--------------------------------

### Set Current Schema to Default

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/db/set_current_schema.out.txt

Use the `USE` statement to set the current schema. This example sets it to 'default'.

```SQL
USE default
```

--------------------------------

### Configure Metastore Benchmark Test Parameters

Source: https://github.com/apache/hive/blob/master/standalone-metastore/metastore-tools/metastore-benchmarks/README.md

Run benchmark tests with custom parameters, such as the number of objects created (`-N`), warm-up iterations (`-W`), and exclusions for specific operations (`-E`).

```java
java -jar hmsbench.jar -d `metastore_db_name` -H `hostname` -N 500 -W 10 -E 'drop.*' -E 'concurrent.*'
```

--------------------------------

### Configure Metastore Database

Source: https://github.com/apache/hive/blob/master/hcatalog/src/test/e2e/templeton/README.txt

Configures the database connection for the Hive metastore persistence. This example shows Derby configuration.

```xml
<property>
  <name>javax.jdo.option.ConnectionURL</name>
  <value>jdbc:derby:;databaseName=/Users/ekoifman/dev/data/tmp/metastore_db_e2e;create=true</value>
  <description>Controls which DB engine metastore will use for persistence. In particular,
  where Derby will create it's data files.</description>
</property>
```

--------------------------------

### Run a Specific Metastore Benchmark Test

Source: https://github.com/apache/hive/blob/master/standalone-metastore/metastore-tools/metastore-benchmarks/README.md

Execute a single, named benchmark test. Use the `-M` flag followed by the test name. Replace `metastore_db_name` and `hostname` with your specific values.

```java
java -jar hmsbench.jar -d `metastore_db_name` -H `hostname` -M "TestGetValidWriteIds"
```

--------------------------------

### Run Specific Test Group

Source: https://github.com/apache/hive/blob/master/hcatalog/src/test/e2e/templeton/README.txt

Filters the Ant test execution to run a specific test group, for example, 'TestHive'.

```bash
-Dtests.to.run='-t TestHive'
```

--------------------------------

### Initialize Offset Table for ETL

Source: https://github.com/apache/hive/blob/master/kafka-handler/README.md

Prepare an offset tracking table by initializing it with the minimum offset for each partition. This is a prerequisite for the Kafka to Hive ETL pipeline.

```sql
DROP TABLE 
  kafka_table_offsets;
  
CREATE TABLE 
  kafka_table_offsets (
  `partition_id` INT,
  `max_offset` BIGINT,
  `insert_time` TIMESTAMP);

INSERT OVERWRITE TABLE 
  kafka_table_offsets 
SELECT
 `__partition`, 
 MIN(`__offset`) - 1, 
 CURRENT_TIMESTAMP 
FROM 
  wiki_kafka_hive 
GROUP BY 
  `__partition`, 
  CURRENT_TIMESTAMP;
```

--------------------------------

### Get New Aggregation Buffer

Source: https://github.com/apache/hive/blob/master/ql/src/gen/vectorization/UDAFTemplates/VectorUDAFAvgDecimalMerge.txt

Creates and returns a new instance of the aggregation buffer. This is used to initialize aggregation state.

```Java
@Override
public AggregationBuffer getNewAggregationBuffer() throws HiveException {
  return new Aggregation();
}
```

--------------------------------

### Get Aggregation Buffer Fixed Size

Source: https://github.com/apache/hive/blob/master/ql/src/gen/vectorization/UDAFTemplates/VectorUDAFAvgMerge.txt

Calculates and returns the fixed size of the aggregation buffer, aligned to memory boundaries.

```Java
@Override
  public long getAggregationBufferFixedSize() {
    JavaDataModel model = JavaDataModel.get();
    return JavaDataModel.alignUp(
      model.object() +
      model.primitive2() * 2, model.memoryAlign());
  }
```

--------------------------------

### Configure ORC Storage and Optimization Settings

Source: https://context7.com/apache/hive/llms.txt

Tune ORC file format behavior at the session level using SET commands. Options include default compression, split handling, predicate pushdown, and caching.

```sql
-- Session-level ORC tuning
SET hive.exec.orc.default.compress=ZSTD;
SET hive.orc.splits.include.file.footer=true;
SET hive.optimize.index.filter=true;   -- enable ORC predicate pushdown
SET hive.orc.cache.use.soft.references=true;
```

```sql
-- Inspect ORC file metadata
SET hive.exec.post.hooks=org.apache.hadoop.hive.ql.hooks.PostExecOrcFileDump;
SELECT * FROM events_orc LIMIT 1;
```

--------------------------------

### HPL/SQL Function Execution

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/local/create_function4.out.txt

Executes a previously defined function named GET in HPL/SQL. This snippet shows how to invoke a function.

```hplsql
EXEC FUNCTION GET
```

--------------------------------

### Launch Metastore with Custom HDFS Configuration

Source: https://github.com/apache/hive/blob/master/standalone-metastore/packaging/src/docker/README.md

Runs the Hive Metastore, allowing the use of a custom hdfs-site.xml. The custom configuration directory is mounted into the container, and the HIVE_CUSTOM_CONF_DIR environment variable points to its location within the container.

```shell
docker run -d -p 9083:9083 --env DB_DRIVER=postgres \
    -v /opt/hive/conf:/hive_custom_conf --env HIVE_CUSTOM_CONF_DIR=/hive_custom_conf \
    --mount type=bind,source=`mvn help:evaluate -Dexpression=settings.localRepository -q -DforceStdout`/org/postgresql/postgresql/42.7.3/postgresql-42.7.3.jar,target=/opt/hive/lib/postgres.jar \
    --name metastore apache/hive:standalone-metastore-${HIVE_VERSION}
```

--------------------------------

### HPLSQL IF with Complex Condition

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/local/if.out.txt

An example of an IF statement with a more complex condition involving logical operators like AND and BETWEEN. This allows for more nuanced conditional checks.

```HPLSQL
PRINT
IS NOT NULL AND BETWEEN
IF TRUE executed
PRINT
True block - Correct
```

--------------------------------

### HmsBench Command-Line Usage

Source: https://github.com/apache/hive/blob/master/standalone-metastore/metastore-tools/metastore-benchmarks/README.md

Displays the help message for the BenchmarkTool, outlining all available options and their default values.

```bash
Usage: BenchmarkTool [-ChlV] [--sanitize] [--confdir=<confDir>]
                         [--params=<nParameters>] [--savedata=<dataSaveDir>]
                         [--separator=<csvSeparator>] [-d=<dbName>] [-H=URI]
                         [-L=<spinCount>] [-N=<instances>] [-o=<outputFile>]
                         [-P=<port>] [-t=<tableName>] [-T=<nThreads>] [-W=<warmup>]
                         [-E=<exclude>]... [-M=<matches>]...
       --confdir=<confDir>    configuration directory
       --params=<nParameters> number of table/partition parameters
                                Default: 0
       --sanitize             sanitize results (remove outliers)
       --savedata=<dataSaveDir>
                                 save raw data in specified dir
       --separator=<csvSeparator>
                                 CSV field separator
                                   Default: 
  -C, --csv                  produce CSV output
  -d, --db=<dbName>          database name
  -E, --exclude=<exclude>    test name patterns to exclude
  -h, --help                 Show this help message and exit.
  -H, --host=URI             HMS Host
  -l, --list                 list matching benchmarks
  -L, --spin=<spinCount>     spin count
                                   Default: 100
  -M, --pattern=<matches>    test name patterns
  -N, --number=<instances>   umber of object instances
                                   Default: 100
  -o, --output=<outputFile>  output file
  -P, --port=<port>          HMS Server port
                                   Default: 9083
  -t, --table=<tableName>    table name
  -T, --threads=<nThreads>   number of concurrent threads
                                   Default: 2
  -V, --version              Print version information and exit.
  -W, --warmup=<warmup>      warmup count
                                   Default: 15
```

--------------------------------

### Advanced Metastore Benchmark Configuration

Source: https://github.com/apache/hive/blob/master/standalone-metastore/metastore-tools/metastore-benchmarks/README.md

Run benchmark tests with advanced options including saving raw data, sanitizing results, using a specific table name, and running with multiple partition counts. The `-N` flag can be used multiple times to specify different partition counts for tests.

```java
java -jar hmsbench.jar -H `hostname` \
            --savedata /tmp/benchdata \
            --sanitize \
            -N 100 -N 1000 \
            -o bench_results.csv -C \
            -d testbench \
            --params=100
```

--------------------------------

### Get New Aggregation Buffer

Source: https://github.com/apache/hive/blob/master/ql/src/gen/vectorization/UDAFTemplates/VectorUDAFAvgDecimal64ToDecimal.txt

Provides a new instance of the AggregationBuffer for the average calculation. Requires input scale and temporary storage.

```Java
@Override
    public AggregationBuffer getNewAggregationBuffer() throws HiveException {
      return new Aggregation(inputScale, temp);
    }
```

--------------------------------

### Metastore Benchmark with Tab-Separated Output and Data Saving

Source: https://github.com/apache/hive/blob/master/standalone-metastore/metastore-tools/metastore-benchmarks/README.md

Run benchmark tests and configure the output to be tab-separated (`--csv`). Individual data points can be saved to a specified directory (`--savedata`).

```java
java -jar hmsbench.jar -d `metastore_db_name` -H `hostname` -o result.csv --csv --savedata data
```

--------------------------------

### Get Aggregation Buffer Fixed Size

Source: https://github.com/apache/hive/blob/master/ql/src/gen/vectorization/UDAFTemplates/VectorUDAFVar.txt

Calculates and returns the fixed size of the aggregation buffer in bytes, considering memory alignment.

```Java
@Override
  public long getAggregationBufferFixedSize() {
      JavaDataModel model = JavaDataModel.get();
      return JavaDataModel.alignUp(
        model.object() +
        model.primitive2()*3+
        model.primitive1(),
        model.memoryAlign());
  }
```

--------------------------------

### Execute Pig Script with Load/Store

Source: https://github.com/apache/hive/blob/master/hcatalog/src/test/e2e/templeton/newtests/pigtest.txt

Run a Pig script named 'loadstore.pig' that includes load and store operations. Ensure that input and output directories are correctly passed as parameters.

```bash
pig -p INPDIR=:INPDIR: -p OUTDIR=:OUTDIR: loadstore.pig
```

--------------------------------

### Query Kafka Table

Source: https://github.com/apache/hive/blob/master/kafka-handler/README.md

Perform standard SQL queries on the Kafka table to retrieve data. This example shows a simple select with a limit.

```sql
SELECT * FROM kafka_ssl LIMIT 10;
```

--------------------------------

### Create HPL/SQL Package

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/local/drop_package.out.txt

Use CREATE PACKAGE to define the interface for a package.

```HPLSQL
CREATE PACKAGE
```

--------------------------------

### Get Aggregation Buffer Fixed Size

Source: https://github.com/apache/hive/blob/master/ql/src/gen/vectorization/UDAFTemplates/VectorUDAFMinMaxString.txt

Calculates and returns the fixed size of the aggregation buffer in bytes. This is used for memory management and optimization.

```Java
@Override
    public long getAggregationBufferFixedSize() {
      JavaDataModel model = JavaDataModel.get();
      return JavaDataModel.alignUp(
        model.object() +
        model.ref()+
        model.primitive1()*2,
        model.memoryAlign());
    }
```

--------------------------------

### Set and Print Variable in HPL/SQL

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/local/decode.out.txt

Shows how to set a variable to a specific value and print it.

```hplsql
SET var1 = 1
PRINT
A
```

--------------------------------

### Get Aggregation Buffer Fixed Size

Source: https://github.com/apache/hive/blob/master/ql/src/gen/vectorization/UDAFTemplates/VectorUDAFAvgTimestamp.txt

Calculates and returns the fixed size of the aggregation buffer in bytes, considering object and primitive types.

```Java
@Override
  public long getAggregationBufferFixedSize() {
    JavaDataModel model = JavaDataModel.get();
    return JavaDataModel.alignUp(
      model.object() +
      model.primitive2() * 2, 
      model.memoryAlign());
  }
```

--------------------------------

### Run Benchmarks with Single Jar

Source: https://github.com/apache/hive/blob/master/standalone-metastore/metastore-tools/metastore-benchmarks/README.md

Execute the HmsBench tool using its standalone jar file, providing necessary options and specifying tests.

```bash
java -jar hmsbench.jar <optins> [test]...
```

--------------------------------

### Get Current Aggregation Buffer

Source: https://github.com/apache/hive/blob/master/ql/src/gen/vectorization/UDAFTemplates/VectorUDAFAvgTimestamp.txt

Retrieves the specific Aggregation buffer instance for a given row and buffer index from the aggregation buffer sets.

```java
private Aggregation getCurrentAggregationBuffer(
        VectorAggregationBufferRow[] aggregationBufferSets,
        int bufferIndex,
        int row) {
      VectorAggregationBufferRow mySet = aggregationBufferSets[row];
      Aggregation myagg = (Aggregation) mySet.getAggregationBuffer(bufferIndex);
      return myagg;
    }
```

--------------------------------

### Declare and Print Variable in HPL/SQL

Source: https://github.com/apache/hive/blob/master/hplsql/src/test/results/local/decode.out.txt

Demonstrates declaring an integer variable and printing its initial value.

```hplsql
DECLARE var1 INT = 3
PRINT
C
```

--------------------------------

### Create Partitioned and Bucketed ORC Table with ACID

Source: https://context7.com/apache/hive/llms.txt

Use this for creating transactional tables with specific storage formats, partitioning, and bucketing for optimized performance and ACID compliance. Ensure 'transactional' property is set to 'true'.

```sql
CREATE TABLE orders (
    order_id    BIGINT,
    customer_id INT,
    product     STRING,
    amount      DECIMAL(10,2),
    order_ts    TIMESTAMP
)
PARTITIONED BY (order_date STRING, region STRING)
CLUSTERED BY (customer_id) INTO 32 BUCKETS
STORED AS ORC
TBLPROPERTIES (
    'transactional'         = 'true',
    'orc.compress'          = 'SNAPPY',
    'orc.bloom.filter.columns' = 'customer_id,product',
    'orc.bloom.filter.fpp'  = '0.01'
);
```

--------------------------------

### Get Vectorized Column Parameters

Source: https://github.com/apache/hive/blob/master/ql/src/gen/vectorization/ExpressionTemplates/ColumnUnaryMinus.txt

Retrieves the string representation of column parameters for vectorized expression evaluation. This is used internally by the VectorExpression framework.

```Java
  @Override
  public String vectorExpressionParameters() {
    return getColumnParamString(0, inputColumnNum[0]);
  }
```