Quick start
This guide aims to provide a quick and easy way to get started with RisingWave.
Step 1: Start RisingWave
The following options start RisingWave in the standalone mode. In this mode, data is stored in the file system and the metadata is stored in the embedded SQLite database.
For extensive testing or single-machine deployment, consider starting RisingWave via Docker Compose. For production environments, consider RisingWave Cloud, our fully managed service, or deployment on Kubernetes using the Operator or Helm Chart.
Script installation
Open a terminal and run the following curl command.
curl https://risingwave.com/sh | sh
To start a RisingWave instance, run the following command.
risingwave
Docker
Ensure Docker Desktop is installed and running in your environment.
docker run -it --pull=always -p 4566:4566 -p 5691:5691 risingwavelabs/risingwave:latest single_node
Homebrew
Ensure Homebrew is installed, and run the following commands:
brew tap risingwavelabs/risingwave
brew install risingwave
risingwave
From a Web browser
https://playground.risingwave.dev/
Step 2: Connect to RisingWave
Ensure you have psql installed in your environment. To learn about how to install it, see Install psql without PostgreSQL.
Open a new terminal window and run:
psql -h localhost -p 4566 -d dev -U root
Step 3: Insert some data
RisingWave supports both direct data insertion and streaming data ingestion from sources like message queues and database change streams.
To keep things simple, we'll demonstrate the approach of direct data insertion. Let's create a table and insert some data.
For instance, we can create a table named exam_scores to store information about examination scores.
CREATE TABLE exam_scores (
score_id int,
exam_id int,
student_id int,
score real,
exam_date date
);
INSERT INTO exam_scores (score_id, exam_id, student_id, score, exam_date)
VALUES
(1, 101, 1001, 85.5, '2022-01-10'),
(2, 101, 1002, 92.0, '2022-01-10'),
(3, 101, 1003, 78.5, '2022-01-10'),
(4, 102, 1001, 91.2, '2022-02-15'),
(5, 102, 1003, 88.9, '2022-02-15');
Step 4: Analyze and query data
As an example, let's create a materialized view to calculate the average score for each examination, and then view the current result of the materialized view.
CREATE MATERIALIZED VIEW average_exam_scores AS
SELECT
exam_id,
AVG(score) AS average_score,
COUNT(score) AS total_scores
FROM
exam_scores
GROUP BY
exam_id;
SELECT * FROM average_exam_scores;
------
exam_id | average_score | total_scores
---------+-------------------+--------------
102 | 90.05000000000001 | 2
101 | 85.33333333333333 | 3
(2 rows)
As new data is received, the average_exam_scores materialized view will be automatically updated. RisingWave performs incremental computations in the background to keep the results up to date.
Now, let's insert five additional rows of data into the exam_scores table and query the latest result from the average_exam_scores materialized view. This will provide us with the updated average score for each examination.
INSERT INTO exam_scores (score_id, exam_id, student_id, score, exam_date)
VALUES
(11, 101, 1004, 89.5, '2022-05-05'),
(12, 101, 1005, 93.2, '2022-05-05'),
(13, 102, 1004, 87.1, '2022-06-10'),
(14, 102, 1005, 91.7, '2022-06-10'),
(15, 102, 1006, 84.3, '2022-06-10');
SELECT * FROM average_exam_scores;
------
exam_id | average_score | total_scores
---------+-------------------+--------------
101 | 87.74 | 5
102 | 88.64000000000001 | 5
(2 rows)
What's next?
Congratulations! You've successfully started RisingWave and conducted some initial data analysis. To explore further, you may want to:
- Check out the ready-to-run examples:
- See this GitHub directory for ready-to-run demos and integration examples.
- Read our documentation to learn about how to ingest data from data streaming sources, transform data, and deliver data to downstream systems.
