Apache Paimon

Giriş

Açıklaması şöyle

Apache Paimon is a new data lakehouse format that focuses on solving the challenges of streaming scenarios, but also supports batch processing.

Merge files

Açıklaması şöyle

Paimon is designed with a built-in merge mechanism, and many other optimizations for mass writes, making it more adaptable to streaming scenarios.

Apache Iceberg

Giriş

Açıklaması şöyle

Iceberg is the most widely supported by various open-source engines, including pure query engines (e.g., Trino), New SQL databases (e.g., StarRocks, Doris), and streaming frameworks (e.g., Flink, Spark), all of which support Iceberg.

Iceberg Problems 

Stream write işleminde dosyaları birleştirmek gerekiyor. Açıklaması şöyle

Iceberg faces several problems in streaming scenarios, the most serious one is the fragmentation of small files. Queries in data lakehouses rely heavily on file reads, and if a query has to scan many files at once, it will of course perform poorly.

To address this issue, an external orchestrator is required to regularly merge files. 

What’s Coming for Iceberg in 2025?

Açıklama burada

1. RBAC Catalog: Fixing Permissions at Scale

2. Change Data Capture (CDC): Iceberg’s Streaming Evolution

3. Materialized Views: Simplifying Derived Data

Apache Arrow - Columnar Memory Format

Giriş

Açıklaması şöyle

Apache Arrow is a cross-language development framework for in-memory data. It provides a standardized columnar memory format for efficient data sharing and fast analytics. Arrow employs a language-agnostic approach, designed to eliminate the need for data serialization and deserialization, improving the performance and interoperability between complex data processes and systems.

Apache Arrow vs Apache Parquet 

Açıklaması şöyle

The Apache Arrow format project began in February 2016, focusing on columnar in-memory analytics workload. Unlike file formats like Parquet or CSV, which specify how data is organized on disk, Arrow focuses on how data is organized in memory.

Maven

Şu satırı dahil ederiz

<dependency>
  <groupId>org.apache.arrow</groupId>
  <artifactId>arrow-memory</artifactId>
  <version>6.0.1</version>
</dependency>

<dependency>
  <groupId>org.apache.arrow</groupId>
  <artifactId>arrow-vector</artifactId>
  <version>6.0.1</version>
</dependency>

Örnek

Yazma için şöyle yaparız

import org.apache.arrow.memory.RootAllocator;
import org.apache.arrow.vector.*;
import org.apache.arrow.vector.ipc.*;
import org.apache.arrow.vector.util.*;

// Set up the allocator and the schema for the vector
try (RootAllocator allocator = new RootAllocator(Integer.MAX_VALUE);
  VarCharVector vector = new VarCharVector("vector", allocator);
  ArrowWriter writer = new ArrowWriter(vector, new Schema(Collections.
    singletonList(vector.getField())))) {

  // Write data to the vector
  vector.setSafe(0, "Apache".getBytes());
  vector.setSafe(1, "Arrow".getBytes());
  vector.setSafe(2, "Java".getBytes());
  vector.setValueCount(3);

  // Write vector to a file
  try (FileOutputStream out = new FileOutputStream("arrow-data.arrow")) {
    writer.writeArrow(out.getChannel());
  }
}

Okuma için şöyle yaparız

// Now, let's read the data we just wrote
try (RootAllocator allocator = new RootAllocator(Integer.MAX_VALUE);
  ArrowReader reader = new ArrowReader(new FileInputStream("arrow-data.arrow")
    .getChannel(), allocator)) {

  // Read schema and load the data
  reader.loadNextBatch();

  // Get the vector
  try (VarCharVector vector = (VarCharVector) reader.getVectorSchemaRoot()
    .getVector("vector")) {
    // Iterate over the values in the vector
    for (int i = 0; i < vector.getValueCount(); i++) {
      System.out.println(new String(vector.get(i)));
    }
  }
}