Pros of Druid

• Highly scalable and designed for real-time analytics on large datasets
• Supports sub-second query latency for complex aggregations
• Offers a rich set of built-in aggregators and post-aggregators

Cons of Druid

• Steeper learning curve and more complex setup compared to Doris
• Limited support for SQL-like queries, focusing more on specialized query language
• Higher resource requirements for optimal performance

Code Comparison

Druid query example:

{
  "queryType": "timeseries",
  "dataSource": "sample_data",
  "granularity": "day",
  "aggregations": [
    { "type": "longSum", "name": "total_sales", "fieldName": "sales" }
  ],
  "intervals": ["2023-01-01/2023-12-31"]
}

Doris query example:

SELECT DATE_TRUNC('day', event_time) AS day,
       SUM(sales) AS total_sales
FROM sample_data
WHERE event_time BETWEEN '2023-01-01' AND '2023-12-31'
GROUP BY 1
ORDER BY 1;

Both Druid and Doris are powerful analytics databases, but they cater to different use cases. Druid excels in real-time analytics and complex aggregations, while Doris offers a more familiar SQL interface and easier setup. The choice between them depends on specific project requirements and team expertise.

Pros of Pinot

• Better suited for real-time analytics with low-latency queries
• Supports a wider range of data ingestion methods, including streaming
• More flexible schema design and data modeling capabilities

Cons of Pinot

• Higher complexity in setup and maintenance
• Less mature ecosystem compared to Doris
• Steeper learning curve for new users

Code Comparison

Pinot query example:

SELECT COUNT(*) FROM myTable
WHERE timeColumn BETWEEN 1589980800000 AND 1589984400000
GROUP BY dimension1, dimension2
LIMIT 10

Doris query example:

SELECT COUNT(*) FROM myTable
WHERE timeColumn BETWEEN '2020-05-20 10:00:00' AND '2020-05-20 11:00:00'
GROUP BY dimension1, dimension2
LIMIT 10

Both systems use SQL-like syntax for querying, but Pinot often uses millisecond timestamps, while Doris typically uses datetime strings. Pinot's query language also includes some additional features for real-time analytics that may not be present in Doris.

Overall, Pinot excels in real-time analytics scenarios with its focus on low-latency queries and streaming data ingestion. Doris, on the other hand, offers a more straightforward setup and may be easier for teams already familiar with traditional data warehousing concepts. The choice between the two depends on specific use cases and team expertise.

Pros of ClickHouse

• Extremely fast query performance, especially for analytical workloads
• Highly scalable and can handle petabytes of data efficiently
• Rich set of built-in functions and features for data analysis

Cons of ClickHouse

• Steeper learning curve due to its unique query language and architecture
• Limited support for real-time updates and deletes
• Less mature ecosystem compared to some other database systems

Code Comparison

ClickHouse query example:

SELECT
    toYear(date) AS year,
    sum(revenue) AS total_revenue
FROM sales
GROUP BY year
ORDER BY year

Doris query example:

SELECT
    year(date) AS year,
    sum(revenue) AS total_revenue
FROM sales
GROUP BY year
ORDER BY year

Both ClickHouse and Doris are columnar databases designed for analytical workloads, but they have different query syntaxes and optimizations. ClickHouse often uses custom functions like toYear(), while Doris tends to use more standard SQL functions. Both systems aim to provide fast query performance and scalability for large datasets, but ClickHouse is generally considered to have an edge in raw performance for certain types of analytical queries.

Pros of StarRocks

• Higher query performance, especially for complex analytical queries
• More advanced vectorized execution engine
• Better support for real-time analytics and streaming data ingestion

Cons of StarRocks

• Smaller community and ecosystem compared to Doris
• Less mature documentation and learning resources
• Fewer integrations with other data tools and platforms

Code Comparison

StarRocks query example:

SELECT user_id, COUNT(*) as order_count
FROM orders
WHERE order_date >= '2023-01-01'
GROUP BY user_id
HAVING order_count > 5
ORDER BY order_count DESC
LIMIT 10;

Doris query example:

SELECT user_id, COUNT(*) as order_count
FROM orders
WHERE order_date >= '2023-01-01'
GROUP BY user_id
HAVING order_count > 5
ORDER BY order_count DESC
LIMIT 10;

Both StarRocks and Doris use similar SQL syntax for querying data. The main differences lie in their internal processing and optimization techniques rather than the query language itself. StarRocks may execute this query faster due to its advanced vectorized execution engine, especially for large datasets or complex joins.

Pros of Kylin

• Mature OLAP solution with a longer history and wider adoption
• Strong support for multidimensional analysis and cube-based queries
• Integration with various Hadoop ecosystem components

Cons of Kylin

• Higher complexity in setup and maintenance
• Longer query latency for non-cube queries
• Limited support for real-time data ingestion

Code Comparison

Kylin query example:

SELECT year, SUM(price) AS total_price
FROM sales_fact
JOIN date_dim ON sales_fact.date_key = date_dim.date_key
GROUP BY year

Doris query example:

SELECT year, SUM(price) AS total_price
FROM sales_fact
GROUP BY year

Both systems support SQL queries, but Kylin often requires pre-built cubes for optimal performance, while Doris can handle ad-hoc queries more efficiently.

Kylin excels in scenarios with predefined, complex OLAP queries on large datasets, particularly when integrated with Hadoop ecosystems. Doris, on the other hand, offers more flexibility for real-time analytics and ad-hoc queries, with a simpler architecture that's easier to deploy and maintain.

While Kylin provides powerful multidimensional analysis capabilities, Doris focuses on delivering low-latency queries and real-time data ingestion, making it more suitable for scenarios requiring frequent updates and fast query responses.

Pros of Trino

• Broader ecosystem support with connectors for various data sources
• More mature project with a larger community and extensive documentation
• Better performance for complex queries across multiple data sources

Cons of Trino

• Higher resource requirements, especially for memory
• Steeper learning curve due to more complex architecture
• Less optimized for OLAP workloads compared to Doris

Code Comparison

Trino SQL query:

SELECT customer_name, SUM(order_total)
FROM orders
JOIN customers ON orders.customer_id = customers.id
GROUP BY customer_name
HAVING SUM(order_total) > 1000

Doris SQL query:

SELECT customer_name, SUM(order_total)
FROM orders
JOIN customers ON orders.customer_id = customers.id
GROUP BY customer_name
HAVING SUM(order_total) > 1000

The SQL syntax for both Trino and Doris is similar in this example, as they both support standard SQL. However, the underlying execution and optimization strategies differ, with Doris being more optimized for OLAP workloads and Trino excelling in federated queries across multiple data sources.