Bigtable Row#

It is possible to use a RowFilter when adding mutations to a Row and when reading row data with read_row() read_rows().

As laid out in the RowFilter definition, the following basic filters are provided:

  • SinkFilter
  • PassAllFilter
  • BlockAllFilter
  • RowKeyRegexFilter
  • RowSampleFilter
  • FamilyNameRegexFilter
  • ColumnQualifierRegexFilter
  • TimestampRangeFilter
  • ColumnRangeFilter
  • ValueRegexFilter
  • ValueRangeFilter
  • CellsRowOffsetFilter
  • CellsRowLimitFilter
  • CellsColumnLimitFilter
  • StripValueTransformerFilter
  • ApplyLabelFilter

In addition, these filters can be combined into composite filters with

  • RowFilterChain
  • RowFilterUnion
  • ConditionalRowFilter

These rules can be nested arbitrarily, with a basic filter at the lowest level. For example:

# Filter in a specified column (matching any column family).
col1_filter = ColumnQualifierRegexFilter(b'columnbia')

# Create a filter to label results.
label1 = u'label-red'
label1_filter = ApplyLabelFilter(label1)

# Combine the filters to label all the cells in columnbia.
chain1 = RowFilterChain(filters=[col1_filter, label1_filter])

# Create a similar filter to label cells blue.
col2_filter = ColumnQualifierRegexFilter(b'columnseeya')
label2 = u'label-blue'
label2_filter = ApplyLabelFilter(label2)
chain2 = RowFilterChain(filters=[col2_filter, label2_filter])

# Bring our two labeled columns together.
row_filter = RowFilterUnion(filters=[chain1, chain2])