kylin

Pros of Pinot

• Better real-time analytics capabilities, especially for streaming data
• More flexible schema design and support for nested data structures
• Higher query performance for large-scale datasets

Cons of Pinot

• Steeper learning curve and more complex setup compared to Kylin
• Less mature OLAP cube functionality
• Requires more manual tuning for optimal performance

Code Comparison

Pinot query example:

SELECT COUNT(*) FROM myTable
WHERE timeColumn BETWEEN ? AND ?
GROUP BY dimension1, dimension2
LIMIT 100

Kylin query example:

SELECT dimension1, dimension2, SUM(metric1)
FROM my_cube
WHERE time_column BETWEEN '2023-01-01' AND '2023-12-31'
GROUP BY dimension1, dimension2

Key Differences

• Pinot uses a columnar storage format optimized for real-time ingestion and querying
• Kylin pre-builds OLAP cubes for faster query performance on predefined dimensions
• Pinot supports a wider range of data types and more flexible schema evolution
• Kylin integrates more tightly with the Hadoop ecosystem

Both projects are Apache Software Foundation top-level projects and offer robust solutions for big data analytics, but they cater to slightly different use cases and architectural preferences.

Pros of Druid

• Designed for real-time analytics and sub-second query performance
• Highly scalable and can handle massive datasets efficiently
• Supports streaming ingestion and real-time data updates

Cons of Druid

• Steeper learning curve and more complex setup compared to Kylin
• Less optimized for OLAP cube-based analytics
• May require more hardware resources for optimal performance

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')

Kylin query example:

SELECT COUNT(*) AS count
FROM my_cube
WHERE part_dt >= TRUNC(SYSDATE) - 1
GROUP BY TRUNC(part_dt, 'HH24')

Both systems use SQL-like syntax, but Druid focuses on time-series data and real-time aggregations, while Kylin is optimized for pre-calculated OLAP cubes.

Summary

Druid excels in real-time analytics and scalability, making it suitable for large-scale streaming data scenarios. Kylin, on the other hand, is better suited for traditional OLAP workloads with pre-calculated cubes. The choice between the two depends on specific use cases, data volumes, and query patterns.

Pros of Doris

• Better real-time analytics performance, especially for large-scale datasets
• More flexible and scalable architecture, supporting both MPP and vectorized execution
• Easier to deploy and maintain, with a simpler system architecture

Cons of Doris

• Less mature ecosystem compared to Kylin
• Limited support for complex pre-aggregation scenarios
• Steeper learning curve for users familiar with traditional OLAP systems

Code Comparison

Doris query example:

SELECT user_id, SUM(order_amount)
FROM orders
WHERE order_date >= '2023-01-01'
GROUP BY user_id;

Kylin query example:

SELECT user_id, SUM(order_amount)
FROM orders_cube
WHERE order_date >= '2023-01-01'
GROUP BY user_id;

The main difference is that Kylin typically uses pre-built cubes (e.g., orders_cube) for faster query performance, while Doris can efficiently query the base table directly. Doris's MPP architecture allows for fast ad-hoc queries without the need for extensive pre-aggregation, making it more flexible for real-time analytics scenarios.

Pros of Presto

• Faster query execution for large-scale data processing
• More flexible architecture supporting various data sources
• Wider adoption and larger community support

Cons of Presto

• Higher memory consumption
• Steeper learning curve for configuration and optimization
• Less optimized for OLAP-specific workloads

Code Comparison

Kylin query example:

SELECT SUM(price) AS total_price
FROM sales
WHERE country = 'USA'
GROUP BY product_category

Presto query example:

SELECT product_category, SUM(price) AS total_price
FROM hive.sales
WHERE country = 'USA'
GROUP BY product_category

Both examples show similar SQL syntax, but Presto's query includes the data source (hive) in the table reference. Kylin typically uses pre-built cubes for faster OLAP queries, while Presto can query various data sources directly.

Key Differences

• Kylin focuses on OLAP workloads with pre-built cubes, while Presto is a more general-purpose SQL query engine
• Presto supports a wider range of data sources out-of-the-box
• Kylin offers better performance for specific OLAP scenarios, while Presto provides more flexibility for diverse query types

Pros of Calcite

• More versatile and adaptable to various data processing systems
• Stronger focus on SQL optimization and query planning
• Wider adoption and integration with other Apache projects

Cons of Calcite

• Steeper learning curve due to its complexity
• May require more configuration and setup for specific use cases
• Less out-of-the-box functionality for OLAP-specific operations

Code Comparison

Calcite (SQL parsing):

SqlParser.Config parserConfig = SqlParser.config()
    .withCaseSensitive(false)
    .withQuotedCasing(Casing.UNCHANGED)
    .withUnquotedCasing(Casing.TO_UPPER);
SqlParser parser = SqlParser.create(sql, parserConfig);
SqlNode sqlNode = parser.parseQuery();

Kylin (Cube building):

CubeInstance cube = cubeManager.getCube(cubeName);
CubeSegment newSeg = cube.getNextSegment();
CubeBuilder cubeBuilder = new CubeBuilder(cube, newSeg);
cubeBuilder.buildCube(jobId, buildType);

While Calcite focuses on SQL parsing and optimization, Kylin specializes in OLAP cube operations. Calcite provides a more flexible foundation for various data processing tasks, whereas Kylin offers more specific functionality for multidimensional analysis and cube management out of the box.

Pros of Hive

• Mature and widely adopted data warehousing solution with extensive ecosystem support
• Supports a wide range of data formats and storage systems
• Provides SQL-like query language (HiveQL) for easy data manipulation

Cons of Hive

• Can be slower for real-time queries compared to Kylin's OLAP cube approach
• Requires more manual optimization for complex queries
• Less efficient for high-concurrency scenarios

Code Comparison

Hive query example:

SELECT customer_id, SUM(order_total)
FROM orders
GROUP BY customer_id
HAVING SUM(order_total) > 1000;

Kylin query example:

SELECT customer_id, SUM(order_total)
FROM orders_cube
GROUP BY customer_id
HAVING SUM(order_total) > 1000;

The main difference is that Kylin queries are executed on pre-built OLAP cubes, which can provide faster query performance for complex aggregations and high-concurrency scenarios. Hive queries are executed directly on the raw data, which may require more processing time but offers more flexibility for ad-hoc queries.

Both systems use SQL-like syntax, making it easier for users familiar with traditional databases to work with big data. However, Kylin's approach is more suited for scenarios where query patterns are known in advance and can be optimized through cube design, while Hive is more flexible for exploratory data analysis and ad-hoc querying.