starrocks

Pros of Doris

• Mature Apache project with a larger community and longer history
• Better documentation and more comprehensive user guides
• Stronger support for SQL compliance and compatibility

Cons of Doris

• Generally slower query performance, especially for complex analytical queries
• Less flexible storage architecture, limiting scalability for very large datasets
• Fewer advanced features for real-time analytics and streaming data ingestion

Code Comparison

Doris query example:

SELECT user_id, SUM(order_amount) AS total_amount
FROM orders
WHERE order_date BETWEEN '2023-01-01' AND '2023-12-31'
GROUP BY user_id
HAVING total_amount > 1000
ORDER BY total_amount DESC
LIMIT 10;

StarRocks query example:

SELECT user_id, SUM(order_amount) AS total_amount
FROM orders
WHERE order_date BETWEEN '2023-01-01' AND '2023-12-31'
GROUP BY user_id
HAVING total_amount > 1000
ORDER BY total_amount DESC
LIMIT 10;

The SQL syntax for both systems is very similar, as they both aim for MySQL compatibility. However, StarRocks may offer better performance for this type of analytical query due to its optimized storage and query engine.

Pros of ClickHouse

• Mature project with a larger community and more extensive documentation
• Wider range of data types and functions supported
• Better performance for certain types of analytical queries

Cons of ClickHouse

• Steeper learning curve and more complex configuration
• Less optimized for real-time analytics and updates
• Limited support for distributed transactions

Code Comparison

ClickHouse query example:

SELECT
    toYear(date) AS year,
    sum(amount) AS total_amount
FROM sales
GROUP BY year
ORDER BY year

StarRocks query example:

SELECT
    year(date) AS year,
    sum(amount) AS total_amount
FROM sales
GROUP BY year
ORDER BY year

Both systems use SQL-like syntax, but ClickHouse often requires more specific function names (e.g., toYear instead of year). StarRocks generally aims for a more familiar SQL experience, which can be easier for users transitioning from traditional databases.

While both systems excel in analytical processing, StarRocks focuses more on real-time analytics and easier integration with big data ecosystems. ClickHouse, on the other hand, offers more flexibility and power for complex analytical queries, albeit with a steeper learning curve.

Pros of Druid

• Mature project with a large community and extensive documentation
• Excellent for real-time analytics and time-series data
• Highly scalable and fault-tolerant architecture

Cons of Druid

• Steeper learning curve and more complex setup compared to StarRocks
• Less efficient for ad-hoc queries on large datasets
• Limited support for complex joins and subqueries

Code Comparison

Druid query example:

SELECT COUNT(*) AS count
FROM my_datasource
WHERE timestamp >= CURRENT_TIMESTAMP - INTERVAL '1' DAY
GROUP BY time_floor(__time, 'PT1H')

StarRocks query example:

SELECT COUNT(*) AS count
FROM my_table
WHERE timestamp >= DATE_SUB(NOW(), INTERVAL 1 DAY)
GROUP BY DATE_TRUNC('HOUR', timestamp)

Both systems use SQL-like syntax, but Druid has some specific functions like time_floor, while StarRocks uses more standard SQL functions like DATE_TRUNC. StarRocks generally offers a more familiar SQL experience for users coming from traditional database backgrounds.

Pros of Trino

• More mature project with a larger community and ecosystem
• Supports a wider range of data sources and connectors
• Better suited for complex, federated queries across multiple data sources

Cons of Trino

• Generally slower query performance for analytical workloads
• Higher resource consumption, especially for memory-intensive operations
• More complex setup and configuration process

Code Comparison

Trino SQL query:

SELECT
  customer.name,
  SUM(orders.total_price) AS total_spent
FROM
  hive.sales.customer
  JOIN mysql.ecommerce.orders ON customer.id = orders.customer_id
GROUP BY
  customer.name

StarRocks SQL query:

SELECT
  customer.name,
  SUM(orders.total_price) AS total_spent
FROM
  customer
  JOIN orders ON customer.id = orders.customer_id
GROUP BY
  customer.name

The main difference in these examples is that Trino explicitly specifies the data source for each table (hive.sales and mysql.ecommerce), while StarRocks assumes all tables are within the same database. This highlights Trino's strength in federated queries across multiple data sources, while StarRocks focuses on optimized performance within a single analytical database.

Pros of Pinot

• More mature project with a larger community and ecosystem
• Supports real-time ingestion and near real-time query processing
• Offers flexible schema design and automatic schema inference

Cons of Pinot

• Higher complexity in setup and configuration
• Steeper learning curve for beginners
• May require more resources for optimal performance

Code Comparison

Pinot query example:

SELECT COUNT(*) FROM myTable
WHERE timeColumn BETWEEN 1000 AND 2000
GROUP BY dimension1, dimension2
TOP 50

StarRocks query example:

SELECT COUNT(*) FROM myTable
WHERE timeColumn BETWEEN 1000 AND 2000
GROUP BY dimension1, dimension2
ORDER BY COUNT(*) DESC
LIMIT 50

Both systems use SQL-like syntax for querying, with minor differences in syntax for certain operations. Pinot uses TOP for limiting results, while StarRocks uses the more standard LIMIT clause.

StarRocks generally offers a simpler setup process and easier management, making it more suitable for users who prioritize ease of use. Pinot, on the other hand, provides more advanced features and flexibility, which can be beneficial for complex use cases and larger-scale deployments.