trino

Pros of Spark

• More mature ecosystem with extensive libraries and integrations
• Better support for machine learning and graph processing
• Stronger community and wider adoption in industry

Cons of Spark

• Higher memory consumption and resource requirements
• Steeper learning curve, especially for complex use cases
• Less efficient for ad-hoc SQL queries on large datasets

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

Spark DataFrame API (Scala):

val result = spark.sql("""
  SELECT customer_name, SUM(order_total)
  FROM orders
  JOIN customers ON orders.customer_id = customers.id
  GROUP BY customer_name
""")

Both Trino and Spark support SQL queries, but Spark offers additional APIs for data processing. Trino focuses on SQL and is optimized for distributed query execution, while Spark provides a more comprehensive data processing framework with support for various programming languages and paradigms.

Trino excels at fast, interactive SQL queries across multiple data sources, making it ideal for data lake analytics. Spark, on the other hand, offers a more versatile platform for large-scale data processing, including batch and stream processing, machine learning, and graph computations.

Pros of Hive

• Mature ecosystem with extensive documentation and community support
• Tightly integrated with Hadoop ecosystem for big data processing
• Supports a wide range of file formats and storage systems

Cons of Hive

• Slower query performance compared to Trino, especially for interactive queries
• Limited support for real-time data processing and streaming
• Less flexible when working with diverse data sources outside the Hadoop ecosystem

Code Comparison

Hive query example:

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

Trino query example:

SELECT customer_id, SUM(order_total)
FROM hive.sales.orders
WHERE order_date >= DATE '2023-01-01'
GROUP BY customer_id;

The syntax is similar, but Trino uses a more standardized SQL dialect and supports querying across different data sources using a catalog.schema.table notation.

Key Differences

• Performance: Trino is designed for fast, interactive queries, while Hive is better suited for batch processing
• Data sources: Trino can query multiple data sources simultaneously, Hive is primarily focused on Hadoop data
• Query engine: Trino uses a distributed query engine, while Hive translates queries into MapReduce jobs
• Real-time capabilities: Trino offers better support for real-time data analysis compared to Hive

Pros of Flink

• Robust stream processing capabilities with low latency and high throughput
• Extensive ecosystem with support for various data sources and sinks
• Built-in support for event time processing and windowing operations

Cons of Flink

• Steeper learning curve due to complex API and concepts
• Limited support for ad-hoc querying compared to Trino's SQL-based approach
• Higher resource requirements for small-scale deployments

Code Comparison

Flink (Java):

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStream<String> text = env.readTextFile("input.txt");
DataStream<Tuple2<String, Integer>> counts = text
    .flatMap(new Tokenizer())
    .keyBy(value -> value.f0)
    .sum(1);
counts.print();

Trino (SQL):

SELECT word, COUNT(*) as count
FROM (
  SELECT UNNEST(SPLIT(LOWER(line), ' ')) AS word
  FROM input_table
)
GROUP BY word
ORDER BY count DESC

Summary

Flink excels in stream processing and complex event processing scenarios, offering low-latency and high-throughput capabilities. It provides a rich ecosystem for various data sources and sinks. However, Flink has a steeper learning curve and may be overkill for simpler data processing tasks.

Trino, on the other hand, focuses on distributed SQL query execution across multiple data sources. It offers a more familiar SQL-based approach, making it easier for users with SQL backgrounds to get started. Trino is better suited for ad-hoc querying and data exploration tasks, while Flink shines in real-time stream processing applications.

Pros of Drill

• Supports schema-free JSON data querying without predefined schemas
• Offers a distributed cache for improved query performance
• Provides built-in support for Hive metastore integration

Cons of Drill

• Less extensive connector ecosystem compared to Trino
• Slower development pace and community growth
• Limited support for complex data types and advanced SQL features

Code Comparison

Drill query example:

SELECT * FROM dfs.`/path/to/data/file.json` WHERE age > 30

Trino query example:

SELECT * FROM hive.default.users WHERE age > 30

Both systems use SQL-like syntax, but Drill can query JSON files directly without a predefined schema, while Trino typically requires a catalog and schema definition.

Key Differences

• Query Engine: Drill uses a vectorized execution engine, while Trino uses a distributed query engine
• Data Format Support: Drill excels at querying schema-less data, whereas Trino is better suited for structured data sources
• Ecosystem: Trino has a more extensive connector ecosystem and broader adoption in the industry
• Performance: Trino generally offers better performance for large-scale, complex queries across multiple data sources

Pros of Presto

• Longer history and established ecosystem
• Wider adoption in enterprise environments
• More extensive documentation and community resources

Cons of Presto

• Slower release cycle and feature development
• Less active community engagement
• Limited support for newer technologies and cloud-native environments

Code Comparison

Presto:

SELECT
  orderkey,
  orderstatus,
  totalprice,
  orderdate
FROM
  orders
WHERE
  orderdate >= DATE '1994-01-01'
  AND totalprice > 100000;

Trino:

SELECT
  orderkey,
  orderstatus,
  totalprice,
  orderdate
FROM
  orders
WHERE
  orderdate >= DATE '1994-01-01'
  AND totalprice > 100000;

The SQL syntax for both Presto and Trino is nearly identical in this example, as Trino was forked from Presto. However, Trino has since added more features and optimizations that may not be reflected in basic SQL queries.

While the core functionality remains similar, Trino has evolved to offer improved performance, enhanced security features, and better integration with modern data architectures. Presto, on the other hand, maintains its position as a stable and widely-used solution in enterprise environments, with a focus on compatibility and established workflows.

Pros of Dremio

• Built-in data catalog and data lineage features
• Supports data virtualization and data lake architecture
• User-friendly interface for data exploration and visualization

Cons of Dremio

• Less extensive ecosystem and community support
• More limited query optimization capabilities
• Steeper learning curve for advanced features

Code Comparison

Trino query example:

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

Dremio query example:

SELECT customer_name, SUM(order_total)
FROM "Sales"."Orders"
JOIN "Sales"."Customers" ON Orders.customer_id = Customers.id
GROUP BY customer_name

Both Trino and Dremio use SQL-like syntax for querying data. The main difference lies in how data sources are referenced. Trino typically uses catalog and schema names, while Dremio uses virtual datasets and spaces.

Trino offers more flexibility in connecting to various data sources, making it easier to query across different systems. Dremio, on the other hand, provides a more integrated approach to data virtualization and management within its platform.

Overall, Trino is better suited for large-scale distributed queries across diverse data sources, while Dremio excels in data lake management and self-service analytics scenarios.