A data scientist is performing exploratory data analysis (EDA) on a large dataset stored in Amazon S3. The dataset is partitioned by 'year' and 'month'. During EDA, the data scientist frequently needs to query specific columns for a range of months across multiple years. However, some queries are taking a very long time and incurring high costs due to full-file scans. What data partitioning and file format strategy would optimize query performance and reduce costs for this access pattern?
- ARemove all partitioning and store data in a single large JSON file.
- BImplement multi-column partitioning by 'year/month/day' and convert to Apache Avro.
- CConsolidate small files into larger ones, maintain 'year/month' partitioning, and convert to Apache Parquet.
- DRe-partition the data by 'day' and store it in CSV format.
Show answer & explanationAnswer & explanation
Correct answer: C. Consolidate small files into larger ones, maintain 'year/month' partitioning, and convert to Apache Parquet.
Consolidating small files into larger ones reduces overhead from too many S3 calls and metadata processing. Apache Parquet is a columnar storage format that allows predicates to push down and column pruning, meaning only necessary columns are read, significantly reducing query time and cost for queries that select specific columns. Maintaining 'year/month' partitioning is appropriate for queries over time ranges.
Why the other options are wrong
- A. Removing partitioning and using a single large JSON file would force full table scans for every query, leading to the worst performance and highest costs.
- B. While multi-column partitioning can be useful, adding 'day' might create too many partitions for this access pattern. Apache Avro is a row-oriented format, not ideal for column-pruning performance.
- D. Partitioning by 'day' would lead to an excessive number of small files and partitions, increasing overhead. CSV is a row-oriented format and does not offer the same performance benefits as columnar formats for analytical queries.
S3 Data Optimization for Analytics
Strategies involving file format, file size, and partitioning to improve query performance and reduce costs when analyzing data stored in Amazon S3.
- Columnar formats (e.g., Parquet, ORC) are ideal for analytical queries as they allow column pruning.
- Larger file sizes (e.g., 128MB-1GB) reduce S3 overhead and improve read performance.
- Appropriate partitioning schemes (e.g., by date) limit the amount of data scanned.
Memory trick: Parquet files, large and partitioned, make S3 queries smart!