def test_to_dask_with_partitions_use_json_query(engine):
query_string = '''
{
"query": {
"range" : {
"score" : {
"gte" : 0,
"lte" : 150,
"boost" : 2.0
}
}
}
}
'''
source = ElasticSearchTableSource(query_string, npartitions=2, **CONNECT)
dd = source.to_dask()
assert dd.npartitions == 2
assert set(dd.columns) == set(df.columns)
out = dd.compute()
assert len(out) == len(df)
assert all([d in out.to_dict(orient='records')
for d in df.to_dict(orient='records')])
def test_to_dask(engine):
source = ElasticSearchTableSource('score:[0 TO 150]',
qargs={"sort": 'rank'},
**CONNECT)
dd = source.to_dask()
assert dd.npartitions == 1
assert set(dd.columns) == set(df.columns)
out = dd.compute()
assert out[df.columns].equals(df)
def test_to_dask_empty_shard(engine):
source = ElasticSearchTableSource('score:[0 TO 150]', npartitions=5, qargs={
"sort": 'rank'}, **CONNECT)
dd = source.to_dask()
assert dd.npartitions == 5
assert set(dd.columns) == set(df.columns)
out = dd.compute()
assert len(out) == len(df)
assert all([d in out.to_dict(orient='records')
for d in df.to_dict(orient='records')])
