Support reading and writing Parquet bloom filters

Bloom filter is a performance optimization feature now available in Impala. This filter tells you, rapidly and memory-efficiently, whether the data you are looking for is present in a file.

Impala determines the appropriate conditions while the query is running. Impala can now read and write Parquet bloom filters. However, bloom filters can also provide false positives. If a bloom filter evaluates to:

  • true: data might be present in the data file or might not be present.
  • false: data is not present.

Currently, bloom filters are per column chunk entries which implies that you can skip entire row groups based on the filter. Writing a bloom filter is not useful for dictionary encoded columns, as all distinct values are included in the dictionary and the dictionary can give exact results in filtering with the predicate. If no value passes the predicate the whole row group can be skipped.

For example, if there is a predicate 'WHERE col = some_value' and some_value is not in the bloom filter the row group will be discarded.

Bloom filters support reading and writing columns with the following data types: integers, float, double, and Impala strings. Reading does not need any intervention from Impala, however, writing can be controlled by a new query option parquet_bloom_filter_write and the table property parquet.bloom.filter.columns.

The following table contains the list of the supported data types.
Parquet type Impala type

Query option

The query option for writing Parquet bloom filters (parquet_bloom_filter_write) accepts any of the following values:

  • NEVER - never write Parquet bloom filters.

  • IF_NO_DICT - write Parquet bloom filters if specified in the table properties AND if the row group is not fully dictionary encoded (the number of distinct values exceeds the maximum dictionary size); the row group may still be partially dictionary encoded, in which case the bloom filter contains all values from the whole row group, including those that are present in the dictionary.

  • ALWAYS - always write Parquet bloom filters if specified in the table properties, even if the row group is fully dictionary encoded.

Table setting

The parquet.bloom.filter.columns table property is a comma separated list of 'col_name:bytes' pairs.


col_name is the name of the column for which a bloom filter should be written;

bytes represents the size (in bytes) of the bitset of the bloom filter, and is optional. If you do not provide the size, it will default to the maximal bloom filter size (ParquetBloomFilter::MAX_BYTES).

Example: “col1:1024,col2,col4:100"


The following table contains the data types that are not supported currently. Support for these data types may be added in a future release.

Impala type Reason for not supporting
VARCHAR(N) truncation can change hash
CHAR(N) padding / truncation can change hash
DECIMAL multiple encodings supported
TIMESTAMP multiple encodings supported, timezone conversion
DATE not considered yet