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How to use the playground
/how-to-use-the-playground
playground
Copyright 2023 Datastrato Pvt Ltd. This software is licensed under the Apache License version 2.

Playground introduction

The playground is a complete Gravitino Docker runtime environment with Hive, HDFS, Trino, MySQL, PostgreSQL, and a Gravitino server.

Depending on your network and computer, startup time may take 3-5 minutes. Once the playground environment has started, you can open http://localhost:8090 in a browser to access the Gravitino Web UI.

Prerequisites

Install Git and Docker Compose.

TCP ports used

The playground runs a number of services. The TCP ports used may clash with existing services you run, such as MySQL or Postgres.

Docker container Ports used
playground-gravitino 8090 9001
playground-hive 3307 9000 9083
playground-mysql 3306
playground-postgresql 5342
playground-trino 8080

Start playground

git clone [email protected]:datastrato/gravitino-playground.git
cd gravitino-playground
./launch-playground.sh

Experiencing Gravitino with Trino SQL

  1. Log in to the Gravitino playground Trino Docker container using the following command:
docker exec -it playground-trino bash
  1. Open the Trino CLI in the container.
trino@d2bbfccc7432:/$ trino

Example

Simple queries

You can use simple queries to test in the Trino CLI.

SHOW CATALOGS;

CREATE SCHEMA "metalake_demo.catalog_hive".company
  WITH (location = 'hdfs://hive:9000/user/hive/warehouse/company.db');

SHOW CREATE SCHEMA "metalake_demo.catalog_hive".company;

CREATE TABLE "metalake_demo.catalog_hive".company.employees
(
  name varchar,
  salary decimal(10,2)
)
WITH (
  format = 'TEXTFILE'
);

INSERT INTO "metalake_demo.catalog_hive".company.employees (name, salary) VALUES ('Sam Evans', 55000);

SELECT * FROM "metalake_demo.catalog_hive".company.employees;

SHOW SCHEMAS from "metalake_demo.catalog_hive";

DESCRIBE "metalake_demo.catalog_hive".company.employees;

SHOW TABLES from "metalake_demo.catalog_hive".company;

Cross-catalog queries

In a company, there may be different departments using different data stacks. In this example, the HR department uses Apache Hive to store its data and the sales department uses PostgreSQL. You can run some interesting queries by joining the two departments' data together with Gravitino.

To know which employee has the largest sales amount, run this SQL:

SELECT given_name, family_name, job_title, sum(total_amount) AS total_sales
FROM "metalake_demo.catalog_hive".sales.sales as s,
  "metalake_demo.catalog_postgres".hr.employees AS e
where s.employee_id = e.employee_id
GROUP BY given_name, family_name, job_title
ORDER BY total_sales DESC
LIMIT 1;

To know the top customers who bought the most by state, run this SQL:

SELECT customer_name, location, SUM(total_amount) AS total_spent
FROM "metalake_demo.catalog_hive".sales.sales AS s,
  "metalake_demo.catalog_hive".sales.stores AS l,
  "metalake_demo.catalog_hive".sales.customers AS c
WHERE s.store_id = l.store_id AND s.customer_id = c.customer_id
GROUP BY location, customer_name
ORDER BY location, SUM(total_amount) DESC;

To know the employee's average performance rating and total sales, run this SQL:

SELECT e.employee_id, given_name, family_name, AVG(rating) AS average_rating,  SUM(total_amount) AS total_sales
FROM "metalake_demo.catalog_postgres".hr.employees AS e,
  "metalake_demo.catalog_postgres".hr.employee_performance AS p,
  "metalake_demo.catalog_hive".sales.sales AS s
WHERE e.employee_id = p.employee_id AND p.employee_id = s.employee_id
GROUP BY e.employee_id,  given_name, family_name;

Using Iceberg REST service

If you want to migrate your business from Hive to Iceberg. Some tables will use Hive, and the other tables will use Iceberg. Gravitino provides an Iceberg REST catalog service, too. You can use Spark to access REST catalog to write the table data. Then, you can use Trino to read the data from the Hive table joining the Iceberg table.

spark-defaults.conf is as follows (It's already configured in the playground):

spark.sql.extensions org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions
spark.sql.catalog.catalog_iceberg org.apache.iceberg.spark.SparkCatalog
spark.sql.catalog.catalog_iceberg.type rest
spark.sql.catalog.catalog_iceberg.uri http://gravitino:9001/iceberg/
spark.locality.wait.node 0
  1. Login Spark container and execute the steps.
docker exec -it playground-spark bash
spark@7a495f27b92e:/$ cd /opt/spark && /bin/bash bin/spark-sql 
use catalog_iceberg;
create database sales;
use sales;
create table customers (customer_id int, customer_name varchar(100), customer_email varchar(100));
describe extended customers;    
insert into customers (customer_id, customer_name, customer_email) values (11,'Rory Brown','[email protected]');
insert into customers (customer_id, customer_name, customer_email) values (12,'Jerry Washington','[email protected]');
  1. Login Trino container and execute the steps. You can get all the customers from both the Hive and Iceberg table.
docker exec -it playground-trino bash
trino@d2bbfccc7432:/$ trino  
select * from "metalake_demo.catalog_hive".sales.customers
union
select * from "metalake_demo.catalog_iceberg".sales.customers;