# Parquet Bloom Filter ZoneMap Dictionary and Column Statistics Pruner Evaluator Decode a Parquet thrift footer and quantify predicate-pushdown pruning: zone-map min/max, split-block bloom filters (XXH64), dictionary ratios and encodings, with per-row-group prune-savings estimates for example WHERE clauses. > Canonical page: https://elysiatools.com/en/tools/parquet-bloom-filter-zonemap-dictionary-and-column-statistics-pruner-evaluator - **Category:** Data Analysis - **Keywords:** parquet pruning, bloom filter parquet, zone map, predicate pushdown, row group skip, dictionary encoding ratio, parquet statistics, thrift footer parser ## Overview A hand-rolled thrift compact protocol reader decodes the footer and walks every page header; statistics decode into real zone maps; bloom membership runs the actual XXH64 + SBBF arithmetic; predicates (=, !=, <, <=, >, >=, BETWEEN, IN) are scored per row group as pruned-by-zone-map, pruned-by-bloom or scan with byte costs from total_compressed_size. ## Inputs - **Parquet file** (file): Upload a .parquet file - **WHERE clauses (one per line)** (textarea): id = 75 id > 60 name = 'zeta' Supports =, !=, <, <=, >, >=, BETWEEN a AND b, IN (x, y) ## When to use - When evaluating whether split-block Bloom filters or column min/max statistics are effectively pruning row groups on targeted queries. - When debugging query performance and scan bottlenecks across Parquet files written by engines like Arrow, DuckDB, or Spark. - When verifying dictionary encoding ratios and page-level column statistics before deploying analytical datasets to object storage. ## How it works - Upload a Parquet file to decode the Thrift compact protocol footer and parse column metadata across all row groups and page headers. - Enter optional WHERE clauses with operators like =, !=, <, <=, >, >=, BETWEEN, or IN to test predicate pushdown rules. - The engine performs XXH64 hashing and Split-Block Bloom Filter arithmetic against decoded zone maps and dictionary tables. - Inspect the generated HTML report displaying per-row-group scan decisions, pruning causes, dictionary ratios, and byte savings based on total compressed size. ## Use cases - Optimizing data lake layouts by measuring the pruning efficacy of sorting keys against column statistics and zone maps. - Auditing newly generated Parquet datasets to ensure Split-Block Bloom filters are properly serialized and non-empty. - Estimating network and I/O byte reduction when applying selective WHERE clauses against partition files in cloud storage. ## Frequently asked questions ### Which SQL predicate operators are supported? The evaluator supports =, !=, <, <=, >, >=, BETWEEN a AND b, and IN (x, y) predicates formatted as one clause per line. ### How does the tool evaluate Split-Block Bloom Filters? It extracts raw SBBF byte blocks from the footer/pages, hashes predicate literals using XXH64, and tests bit set membership directly. ### What determines whether a row group is pruned or scanned? A row group is pruned if the predicate falls outside min/max zone maps or fails the Bloom filter test; otherwise, it is marked for scan. ### What is the maximum supported file upload size? The tool accepts Parquet files up to 100 MB (104,857,600 bytes). ### How are byte savings calculated for skipped row groups? 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