Benchmarking Columnar Storage Formats for DNS Logs

As the volume of DNS logs continues to surge in enterprise and service provider environments, the need for efficient storage and fast analytical access becomes increasingly critical. DNS logs are generated continuously, with billions of records accumulating daily in large networks. Each log entry includes multiple fields such as timestamps, query types, domain names, source IP addresses, response codes, and various metadata. Managing and querying such datasets at scale requires careful selection of storage formats, and in the big data ecosystem, columnar storage formats have emerged as the preferred solution. Benchmarking formats like Apache Parquet, Apache ORC, and Apache Arrow for storing DNS logs reveals important trade-offs in terms of compression, query performance, schema evolution, and compatibility with processing engines like Apache Spark, Presto, and Hive.

Columnar storage formats are fundamentally different from row-based formats such as JSON, CSV, or traditional RDBMS layouts. In a columnar format, data is stored field by field rather than row by row. This design significantly improves the performance of analytical queries that typically access a subset of fields over large numbers of rows. For DNS logs, which often include dozens of fields, columnar formats allow queries to scan only the relevant columns—such as querying the distribution of query_type or counting unique query_name values—without touching unrelated data. This results in reduced I/O and faster execution, especially when combined with predicate pushdown and projection optimizations available in modern query engines.

Parquet is one of the most widely adopted columnar formats for DNS log storage, primarily due to its balance of performance, compression efficiency, and ecosystem support. It organizes data into row groups and column chunks, each of which can be independently read and processed. Parquet supports advanced compression codecs like Snappy, Zstandard, and GZIP, and also includes built-in statistics such as min, max, and null counts for each column chunk. These statistics allow query engines to skip irrelevant data during execution, which is particularly valuable for DNS workloads that often include large amounts of repetitive or sparsely queried information. For example, a dataset consisting of billions of DNS queries where only a small fraction relate to a suspicious domain can be filtered quickly by exploiting these min/max bounds.

In benchmarking Parquet for DNS logs, several performance dimensions are evaluated. Compression ratio is critical, as it directly impacts storage costs and disk I/O. DNS logs stored in Parquet using Zstandard often achieve compression ratios of 10:1 or better, especially when fields like query_name are dictionary-encoded. Read performance is another key metric; when queries are limited to a few high-cardinality fields, Parquet’s column pruning capabilities shine. In practice, a query scanning only timestamp and query_type over a month’s worth of logs can execute several times faster than equivalent scans over row-based formats. Spark and Trino both demonstrate strong performance on Parquet files, particularly when data is partitioned by time or other high-level attributes like resolver ID or client region.

Apache ORC offers similar benefits with some distinct differences that make it attractive for DNS analytics, especially in environments leveraging Hive or Hadoop-based systems. ORC files are highly optimized for read-heavy workloads and provide superior compression for datasets with nested or highly repetitive structures. In DNS logs, where many queries are directed to a relatively small set of authoritative servers or domain suffixes, ORC’s ability to use lightweight compression like lightweight dictionaries can lead to even higher compression ratios than Parquet. ORC also provides rich metadata and supports type-specific encodings that reduce CPU overhead during query execution. However, its ecosystem is slightly more biased toward the Hadoop and Hive ecosystem, and while it is well-supported in Spark and Presto, certain edge-case behaviors in schema evolution or complex data types may require tuning.

Arrow, although not traditionally used as a storage format on disk, plays a different role in the DNS analytics pipeline. Apache Arrow is designed as an in-memory columnar representation optimized for fast data interchange between systems. While Arrow is not suitable for long-term storage of DNS logs, it is instrumental when DNS data is being transformed or moved between stages in a pipeline. For example, a Spark job that reads Parquet files containing DNS logs may convert them into Arrow format for high-speed processing in memory, enabling low-latency transformations such as domain enrichment, entropy scoring, or timestamp normalization. Arrow’s tight integration with analytical engines and its support for zero-copy reads across languages like Python and Java make it an excellent choice for ephemeral data stages in high-performance DNS analysis workflows.

To properly benchmark these formats, synthetic and real-world DNS datasets are used, typically ranging from several hundred gigabytes to multiple terabytes. Benchmarks assess ingestion time, disk footprint, query latency, and CPU utilization under realistic workloads. For instance, queries such as counting NXDOMAIN responses over time, detecting clients generating high volumes of queries for random-looking subdomains, or extracting daily top-level domain frequencies are tested across all formats. Parquet generally offers the best mix of speed and storage efficiency for Spark and Trino environments, while ORC performs slightly better for complex aggregations in Hive. Arrow, meanwhile, serves best when performance between stages or across APIs is paramount.

Schema evolution is another consideration in long-term DNS analytics pipelines. As logging formats change—perhaps adding fields like EDNS Client Subnet or query latency—the underlying storage format must accommodate these changes without disrupting historical data access. Parquet and ORC both support schema evolution to varying degrees. Parquet’s support is more robust in scenarios involving new nullable fields or field reordering, while ORC tends to be stricter, sometimes requiring additional tooling or metadata management to avoid read errors. Maintaining consistent schema definitions via Avro or Protobuf and using schema registries can alleviate many of these issues, ensuring compatibility across ingestion, transformation, and query stages.

The selection of a columnar format for DNS logs also influences downstream operations such as machine learning and threat detection. Models that predict DNS tunneling, classify domain generation algorithms, or detect beaconing behavior rely on efficient access to massive datasets for training and inference. Parquet and ORC both allow feature extraction jobs to run faster and at scale, with GPU-accelerated platforms like RAPIDS gaining ground for real-time DNS model scoring when combined with Arrow for memory efficiency.

In summary, benchmarking columnar storage formats for DNS logs is essential for designing a cost-effective, high-performance analytics infrastructure. Parquet provides a versatile and broadly supported solution ideal for Spark-based pipelines. ORC offers deeper optimization for specific query patterns and tight Hive integration. Arrow enables ultra-fast in-memory processing and cross-language communication for real-time transformations. By carefully evaluating these formats against actual DNS workloads, organizations can unlock the full analytical potential of their DNS data, enabling faster incident detection, richer intelligence, and a more responsive data-driven security posture.

As the volume of DNS logs continues to surge in enterprise and service provider environments, the need for efficient storage and fast analytical access becomes increasingly critical. DNS logs are generated continuously, with billions of records accumulating daily in large networks. Each log entry includes multiple fields such as timestamps, query types, domain names, source…