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Test and build

Elasticmagic query filters for attributes

Library to store, filter and build facets for custom attributes

The problem

Each attribute pair can be stored in the index as a nested document. We can use following mapping for that:

attrs:
  type: nested
  properties:
    attr_id:
      type: integer
    # usually only one of the next fields should be populated
    value_int:
      type: integer
    value_bool:
      type: boolean
    value_float:
      type: float

This makes it possible to filter documents by an attribute id and its value (for example we want to find all the documents with attr_id = 1234 and value = 5678):

query:
  bool:
    filter:
    - nested:
        path: attrs
        query:
          bool:
            must:
            - term:
                attrs.attr_id: 1234
            - term:
                attrs.value_int: 5678

It is also possible to build a facets for all attributes at once:

aggs:
  attrs_nested:
    nested:
      path: attrs
    aggs:
      attrs:
        terms:
          field: attrs.attr_id
        aggs:
          values:
            field: attrs.value_int

or for a single attribute:

aggs:
  attrs_nested:
    nested:
      path: attrs
    aggs:
      attr_1234:
        filter:
          term:
            attrs.attr_id: 1234
        aggs:
          values:
            field: attrs.value_int

But nested documents have some drawbacks. Every nested document is stored in the index as different document. For instance, next document will be stored as 5 lucene documents:

name: "I'm a document with nested attributes"
attrs:
- attr_id: 1
  value_int: 42
- attr_id: 2
  value_int: 43
- attr_id: 3
  value_bool: true
- attr_id: 4
  value_float: 99.9

Nested queries are slow by itself:

In particular, joins should be avoided. nested can make queries several times slower and parent-child relations can make queries hundreds of times slower.

But what is worse regular queries are also slower when there are nested documents in the index. It is because of all the fields of main documents becomes sparse. This in turn degrades performance of all filters and accesses to doc_values.

The solution

The idea is to encode pair of an attribute id and a corresponding value into a single value. If our attribute ids are 32-bit integers and all value types also fit into 32 bits we can store them as a single 64-bit value.

So our mapping can be:

attrs:
  type: object
  properties:
    int:
      type: long
    bool:
      type: long
    float:
      type: long

Document with encoded attributes:

name: "I'm a document with packed attributes"
attrs:
# (1 << 32) | 42
- int: 0x1_0000002a
# (2 << 32) | 43
- int: 0x2_0000002b
# (3 << 1) | 1
- bool: 0x7
# (4 << 32) | {integer representation of 99.9}
# (4 << 32) | struct.unpack('=I', struct.pack('=f', 99.9))[0]
- float: 0x4_42c7cccd

Now with a bit of bit magic we can emulate nested queries.

Filtering by attribute id 1234 with value 5678:

query:
  bool:
    filter:
    - term:
        attrs.int: 0x4d2_0000162e

Building facet for all attribute values:

aggs:
  attrs_int:
    terms:
      field: attrs.int
      # specify big enough aggregation size
      # so all flat attrite values should fit
      size: 10000

One more step that client should do is to decode and group values by attribute id.

How to use it in python

from elasticsearch import Elasticsearch

from elasticmagic import Cluster, Document, Field
from elasticmagic.types import List, Long
from elasticmagic.ext.queryfilter import QueryFilter

from elasticmagic_qf_attrs import AttrBoolFacetFilter
from elasticmagic_qf_attrs import AttrIntFacetFilter
from elasticmagic_qf_attrs import AttrRangeFacetFilter
from elasticmagic_qf_attrs.util import merge_attr_value_bool
from elasticmagic_qf_attrs.util import merge_attr_value_float
from elasticmagic_qf_attrs.util import merge_attr_value_int

# Specify document
class AttrsDocument(Document):
    __doc_type__ = 'attrs'

    ints = Field(List(Long))
    bools = Field(List(Long))
    floats = Field(List(Long))

# Create an index
index_name = 'test-attrs'
client = Elasticsearch()
client.indices.create(index=index_name)
cluster = Cluster(client)
index = cluster.get_index(index_name)
index.put_mapping(AttrsDocument)

# Index example document
index.add([
    AttrsDocument(
        ints=[
            merge_attr_value_int(1, 42),
            merge_attr_value_int(2, 43),
        ],
        bools=[merge_attr_value_bool(3, True)],
        floats=[merge_attr_value_float(4, 99.9)],
    ),
], refresh=True)

# Define a query filter
class AttrsQueryFilter(QueryFilter):
    ints = AttrIntFacetFilter(AttrsDocument.ints, alias='a')
    bools = AttrBoolFacetFilter(AttrsDocument.bools, alias='a')
    ranges = AttrRangeFacetFilter(AttrsDocument.floats, alias='a')

# Now we can build facets
qf = AttrsQueryFilter()
sq = index.search_query()
sq = qf.apply(sq, {})
res = sq.get_result()
assert res.total == 1
qf_res = qf.process_result(res)

# And finally lets print results
for attr_id, facet in qf_res.ints.facets.items():
    print(f'> {attr_id}:')
    for facet_value in facet.all_values:
        print(f'  {facet_value.value}: ({facet_value.count_text})')

for attr_id, facet in qf_res.bools.facets.items():
    print(f'> {attr_id}:')
    for facet_value in facet.all_values:
        print(f'  {facet_value.value}: ({facet_value.count_text})')

for attr_id, facet in qf_res.ranges.facets.items():
    print(f'> {attr_id}: ({facet.count})')

# Also we can filter documents:
qf = AttrsQueryFilter()
sq = index.search_query()
sq = qf.apply(
    sq,
    {
        'a1': '42',
        'a3': 'true',
        'a4__lte': '100',
    }
)
res = sq.get_result()
assert res.total == 1

qf = AttrsQueryFilter()
sq = index.search_query()
sq = qf.apply(
    sq,
    {
        'a4__gte': '100',
    }
)
res = sq.get_result()
assert res.total == 0

This script should print:

> 1:
  42: (1)
> 2:
  43: (1)
> 3:
  True: (1)
> 4: (1)

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