ClickHouse

Pros of Druid

• Better support for real-time data ingestion and streaming
• More flexible query language (SQL-like DruidSQL)
• Built-in support for rollup and data summarization

Cons of Druid

• Higher system requirements and more complex setup
• Less efficient for batch processing of large datasets
• Steeper learning curve for configuration and optimization

Code Comparison

Druid query example:

SELECT COUNT(*) AS count,
       SUM(price) AS total_price
FROM transactions
WHERE timestamp >= CURRENT_TIMESTAMP - INTERVAL '1' DAY
GROUP BY product_id
HAVING count > 100
ORDER BY total_price DESC
LIMIT 10

ClickHouse query example:

SELECT product_id,
       COUNT(*) AS count,
       SUM(price) AS total_price
FROM transactions
WHERE timestamp >= now() - INTERVAL 1 DAY
GROUP BY product_id
HAVING count > 100
ORDER BY total_price DESC
LIMIT 10

Both systems support SQL-like queries, but Druid's DruidSQL offers more flexibility in terms of time-based operations and built-in functions. ClickHouse's SQL syntax is more straightforward and closely resembles standard SQL. ClickHouse generally provides better performance for analytical queries on large datasets, while Druid excels in real-time data ingestion and querying scenarios.

Pros of Pinot

• Better support for real-time streaming ingestion and analysis
• More flexible schema design with support for nested fields
• Stronger support for multi-tenancy and resource isolation

Cons of Pinot

• Steeper learning curve and more complex setup compared to ClickHouse
• Less mature ecosystem and fewer integrations with other tools
• Generally slower query performance for large-scale batch processing

Code Comparison

ClickHouse query example:

SELECT date, count() AS count
FROM events
WHERE type = 'click'
GROUP BY date
ORDER BY date DESC
LIMIT 10

Pinot query example:

SELECT dateTime, COUNT(*) AS count
FROM events
WHERE type = 'click'
GROUP BY dateTime
ORDER BY dateTime DESC
LIMIT 10

Both systems use SQL-like syntax for querying, but Pinot's query language (PQL) has some unique features and limitations compared to ClickHouse's more standard SQL dialect. ClickHouse generally offers more advanced SQL functionality, while Pinot focuses on optimizing for specific real-time analytics use cases.

Pros of TimescaleDB

• Built on PostgreSQL, leveraging its robust ecosystem and SQL compatibility
• Optimized for time-series data with automatic partitioning and indexing
• Supports both relational and time-series data in a single database

Cons of TimescaleDB

• Generally slower for large-scale analytics compared to ClickHouse
• Limited support for distributed deployments and horizontal scaling
• Higher storage overhead due to its row-based storage model

Code Comparison

TimescaleDB (SQL-based):

CREATE TABLE sensors (
  time        TIMESTAMPTZ NOT NULL,
  sensor_id   INTEGER,
  temperature DOUBLE PRECISION,
  humidity    DOUBLE PRECISION
);

SELECT time_bucket('1 hour', time) AS hour,
       AVG(temperature) AS avg_temp
FROM sensors
WHERE time > NOW() - INTERVAL '24 hours'
GROUP BY hour
ORDER BY hour;

ClickHouse (SQL-like):

CREATE TABLE sensors (
  time        DateTime,
  sensor_id   UInt32,
  temperature Float64,
  humidity    Float64
) ENGINE = MergeTree()
ORDER BY (time, sensor_id);

SELECT toStartOfHour(time) AS hour,
       avg(temperature) AS avg_temp
FROM sensors
WHERE time > now() - INTERVAL 1 DAY
GROUP BY hour
ORDER BY hour;

Both systems use SQL-like syntax, but ClickHouse has some unique functions and a different table creation process with explicit engine specification.

Pros of Citus

• Built on PostgreSQL, leveraging its robust ecosystem and features
• Supports both distributed and local tables, offering flexibility
• Seamless horizontal scaling for PostgreSQL workloads

Cons of Citus

• Limited to PostgreSQL-compatible queries and syntax
• May require more complex setup and configuration compared to ClickHouse
• Performance can be less optimal for certain analytical workloads

Code Comparison

ClickHouse query example:

SELECT date, count() AS events
FROM events
WHERE type = 'click'
GROUP BY date
ORDER BY date

Citus (PostgreSQL) query example:

SELECT date_trunc('day', event_time) AS date, COUNT(*) AS events
FROM events
WHERE type = 'click'
GROUP BY date
ORDER BY date

Both ClickHouse and Citus are designed for distributed data processing, but they take different approaches. ClickHouse is a column-oriented DBMS optimized for analytical queries, while Citus extends PostgreSQL for distributed workloads. ClickHouse often provides better performance for large-scale analytics, whereas Citus offers more flexibility and compatibility with existing PostgreSQL applications.

Pros of Trino

• Supports a wide variety of data sources and formats
• Designed for distributed query processing across large datasets
• Highly scalable and can handle petabyte-scale data warehouses

Cons of Trino

• Generally slower for OLAP workloads compared to ClickHouse
• More complex setup and configuration
• Higher resource requirements for optimal performance

Code Comparison

ClickHouse SQL example:

SELECT sum(number) FROM numbers(1000000000)

Trino SQL example:

SELECT sum(cast(n AS bigint))
FROM UNNEST(sequence(1, 1000000000)) AS t(n)

Both examples perform a sum operation on a large sequence of numbers, but ClickHouse's syntax is more concise and optimized for analytical queries. Trino's query uses a more standard SQL approach with the UNNEST function to generate the sequence.

ClickHouse is known for its exceptional performance in OLAP scenarios, while Trino excels in federated queries across diverse data sources. ClickHouse is often faster for large-scale aggregations, while Trino offers more flexibility in connecting to various data systems.