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Apache Parquet: Good for Analytics, Not So Good for Search

· 5 min read
Wes Richardet
Wes Richardet
Lucenia Team

Apache Parquet has become a popular format for modern data lake architectures, and it is widely used in Apache Iceberg, Delta Lake, and other analytics-driven storage solutions. Its columnar storage format is designed for high-throughput analytical queries, making it a reasonable choice for batch processing, aggregation-heavy workloads, and structured data exploration. However, when it comes to powering search applications - where low-latency retrieval, relevance ranking, and hybrid search (numeric, text, vector, and geospatial) are critical - Parquet is far from efficient. The fundamental assumptions that make it designed for analytics - compressed column storage, sequential reads, and late materialization - become bottlenecks in search-heavy environments. Let's break down why Parquet, despite its strengths, is ill-suited for search use cases.

The Strength of Apache Parquet in Analytics​

Purposefully designed and built for analytical work loads Apache Parquet's primary advantages include:

  • Columnar Storage: Parquet is designed for read-heavy, analytical workloads where queries scan a subset of columns across a large dataset. This reduces I/O costs by allowing queries to skip irrelevant columns.
  • Compression and Encoding: Techniques like dictionary encoding, run-length encoding, and bit-packing is designed to reduce storage costs and improve retrieval efficiency for data aggregation.
  • Predicate Pushdown: Data is filtered at the storage layer, enabling analytics processing to skip irrelevant data, reducing I/O and improving efficiency in analytics workloads.
  • Batch Processing: Parquet is optimized for parallel processing, making it well-suited for Spark, Trino, and Presto workloads that process large datasets in bulk.

These benefits make Parquet a reasonable choice for batch processing, business intelligence, and data lake storage, where analytics tools like Apache Spark and Presto thrive.

Despite its advantages for analytics, Apache Parquet is ill-suited for search-intensive use cases. Here's why:

1. Inefficient for Low-Latency Retrieval​

Search engines are built to retrieve and rank individual documents efficiently. Apache Parquet's columnar format requires reassembling rows from separate column chunks, adding overhead that drastically slows search lookups. Row groups statistics help but they are also written at write time. The statistics may not be helpful if your access patterns change or your queries target different aspects of the data than you anticipated when designing the row groups.

2. Lack of Native Indexing​

Search engines rely on inverted indexes, KD-trees, HNSW graphs (for vector search), and spatial indexes. Apache Parquet lacks native support for built-in indexing structures optimized for fast retrieval beyond simple row group statistics. This means that keyword searches in Apache Parquet require full-table scans to retrieve relevant documents rather than optimal indexed lookups.

3. Scaling Challenges​

While Apache Parquet might be efficient for analytical workloads, it can struggle with scaling, particularly when dealing with many small files. This leads to performance bottlenecks and increased dataset management complexity. To compensate, engineers often resort to hacks like bucketing, directory partitioning, and file/object naming conventions to impose structure and improve lookup performance. However, these methods introduce additional overhead and fail to provide the low-latency retrieval required for search applications.

4. Update and Schema Evolution Bottlenecks​

Apache Parquet is designed for immutable datasets, making updates costly and inefficient. Additionally, schema evolution can be cumbersome, especially in fast-changing environments where data structures frequently evolve. Search engines, on the other hand, support dynamic indexing and real-time updates, which are essential for applications that require frequently changing data.

5. Whole Record Reading Limitations​

Apache Parquet's columnar format, while great for analytics, makes it difficult to read whole records efficiently for processing. Search engines, built around document-based storage and indexing, can retrieve complete records with minimal latency, making them far superior for search-intensive tasks.

6. Higher Complexity and Overhead​

Downsides of Apache Parquet technology include:

  • Not human-readable: Apache Parquet uses a binary format, making direct inspection or debugging difficult.
  • Slower write speeds: The columnar structure that makes Apache Parquet efficient for reading can make writing more resource-intensive. Writing requires buffering records in memory to organize them into columns before writing to disk, leading to higher memory consumption during write operations.
  • Overhead with small files: Parquet is optimized for large-scale analytics, and dealing with many small files can introduce inefficiencies.
  • Schema evolution complexity: Managing schema changes in Apache Parquet can be challenging, especially in fast-moving environments.
  • Higher CPU usage: Compression and decompression operations can demand significant CPU resources, impacting performance in real-time applications.
  • Less suitable for transactional datasets: Frequent updates and fast write times are better handled by row-based databases or search engines. This is why it is often fronted with Apache Iceberg

7. Limited Support for Relevance Ranking​

Search engines rank results based on sophisticated scoring mechanisms like BM25 and vector search. Apache Parquet, designed for structured analytics, lacks these capabilities, making it unsuitable for applications where relevance matters.

If your workload involves analytical queries, aggregations, and structured reporting, Apache Parquet could be a good choice. However, if your use case demands fast, scalable, and relevance-driven search, Lucenia's search solutions are the better fit.

At Lucenia, we specialize in cutting-edge search solutions that empower organizations to go beyond traditional analytics and unlock the full potential of their data. Our search technology is designed for speed, scalability, and precision, ensuring that businesses can find the right information instantly.

If you're struggling with search performance on Apache Parquet, reach out to us to explore how Lucenia can help optimize your search and analytics strategies with tailored solutions.