Great question—dealing with massive streaming Parquet files while needing both ad-hoc debugging and real-time monitoring is a super common pain point in big data. Let’s break down your two options, plus better fits for your 10TB+ daily scale.

Spark SQL #

Pros #

• Native batch-stream integration: You can use Spark Structured Streaming to directly consume streaming Parquet (or process upstream data into Parquet) while running Spark SQL for ad-hoc queries—no need to switch engines for real-time monitoring and debugging. Its stream processing can output key metrics to dashboards or merge streaming data into optimized Parquet files for later analysis.
• Mature Parquet optimizations: Spark has robust support for Parquet-specific optimizations like column pruning, predicate pushdown, and dynamic partition pruning. It also offers tools to handle small file bloat (e.g., spark.sql.files.maxPartitionBytes to control partition size, or running OPTIMIZE jobs to merge small files).
• Ecosystem flexibility: If you later need complex ETL, machine learning, or data exports, Spark’s ecosystem (MLlib, connectors for various data sources) integrates seamlessly, so you won’t have to rebuild your toolchain.

Cons #

• High interactive query latency: By default, every Spark SQL submission spins up a new job, which can take tens of seconds to minutes even for small queries. While you can use Spark Thriftserver or Livy to maintain long-running sessions, this adds configuration and maintenance overhead, and resource competition remains an issue for multi-user workloads.
• Heavy resource footprint: Spark is a JVM-based distributed framework, so even simple queries allocate executor resources. Its resource usage is far higher than Presto, making it easy to saturate a cluster when multiple users run ad-hoc queries simultaneously.
• Steep learning curve: If you’re new to Spark, tuning stream jobs, optimizing query performance (e.g., adjusting shuffle parameters, memory allocation), and debugging can be time-consuming and error-prone.

Presto #

Pros #

• Instant ad-hoc query performance: Presto is purpose-built for interactive SQL queries—once a session is active, responses are nearly instant (assuming metadata is up-to-date). Its stateless architecture supports elastic scaling, and it handles multi-user concurrency with strong resource isolation, making it ideal for team-wide debugging and analysis.
• Automatic discovery of new Parquet files: If your Parquet files are partitioned (e.g., dt=2024-05-20), Presto uses metadata services like Hive Metastore (HMS) to automatically detect new partitions. For dynamically generated partitions, you can either enable HMS auto-sync or run the SYNC PARTITION command manually—no extra ingestion jobs required.
• Lightweight query experience: Presto’s SQL syntax is close to standard SQL, so it’s easy to pick up. Unlike Spark, you don’t need to write code or manage complex configurations for basic ad-hoc queries.

Cons #

• Limited streaming query support: Presto is fundamentally a batch engine. While there are plugins for real-time data sources, it doesn’t natively support streaming monitoring for Parquet. To achieve quasi-real-time access, you’ll need external tools to sync metadata periodically (e.g., every 5-15 minutes), which caps latency at the minute level—far slower than Spark’s sub-second streaming.
• Poor small file handling: Presto schedules tasks at the file level. If your streaming pipeline generates thousands of small Parquet files (e.g., a few MB each), it will spin up thousands of tiny tasks, drastically slowing down queries. You must pre-process files to merge them into larger chunks (128MB-256MB) to maintain performance.
• Ecosystem limitations: Presto excels at querying data but lacks robust support for complex ETL, streaming processing, or machine learning. You’ll need to pair it with other tools like Spark or Flink if your needs expand beyond ad-hoc analysis.

Your choice depends on which priority comes first: real-time monitoring or ad-hoc query performance. Here are the best paths:

1. If real-time monitoring is non-negotiable:
   Use Spark Structured Streaming for real-time ingestion/processing (merge small Parquet files, compute monitoring metrics) + Presto for ad-hoc queries. To simplify this workflow, write Spark’s output to a lakehouse format like Apache Iceberg or Apache Hudi instead of raw Parquet. These formats automatically manage file sizes and metadata, making Presto queries faster and more reliable.

2. If ad-hoc query speed and concurrency are top priority:
   Stick with Presto paired with Hive Metastore. Schedule hourly Spark jobs to merge small Parquet files and refresh HMS partitions. This gives you quasi-real-time access (5-15 minute latency) to new data while keeping ad-hoc queries fast. Again, using Iceberg/Hudi here will eliminate most small-file-related headaches.

3. Best long-term fit for 10TB+ scale:
   Adopt a lakehouse architecture with Apache Iceberg or Apache Hudi. These formats solve all the pain points of raw Parquet:

   • Automatic file compaction to prevent small file explosions
   • ACID compliance and snapshotting, enabling historical and incremental queries
   • Native support for both Spark (streaming ingestion) and Presto (ad-hoc queries)
   • Efficient metadata management, so Presto can locate new files without scanning the entire storage system

• Partition strategically: Always partition Parquet files by time (hour/day) and high-cardinality business fields—this drastically reduces the data scanned during queries.
• Enforce file size standards: Keep Parquet files between 128MB-256MB. Use Spark’s OPTIMIZE or Iceberg’s Rewrite Data Files to merge small files regularly.
• Maintain metadata hygiene: Ensure your metadata service (HMS/Iceberg) stays up-to-date. For raw Parquet, enable HMS auto-partition discovery; for Iceberg/Hudi, metadata is updated automatically as you write data.

内容的提问来源于stack exchange，提问作者user179156