# One Million Scale Benchmark#

## Methodology#

We created a DocumentArray with one million Documents based on sift1m, a dataset containing 1 million objects (each of 128 dimensions) and using L2 distance metrics.

We benchmarked the document stores as summarized below:

Name

Usage

Client version

Database version

In-memory DocumentArray

DocumentArray()

DocArray 0.18.2

N/A

SQLite

DocumentArray(storage='sqlite')

2.6.0

N/A

Weaviate

DocumentArray(storage='weaviate')

3.9.0

1.16.1

Qdrant

DocumentArray(storage='qdrant')

0.10.3

0.10.1

AnnLite

DocumentArray(storage='anlite')

0.3.13

N/A

ElasticSearch

DocumentArray(storage='elasticsearch')

8.4.3

8.2.0

Redis

DocumentArray(storage='redis')

4.3.4

2.6.0

We focused on the following tasks:

1. Create: Add one million Documents to the document store via extend() and backend capabilities when applicable.

2. Read: Retrieve existing Documents from the document store by .id, i.e. da['some_id'].

3. Update: Update existing Documents in the document store by .id, i.e. da['some_id'] = Document(...).

4. Delete: Delete Documents from the document store by .id, i.e. del da['some_id']

5. Find by condition: Search existing Documents by .tags via find() in the document store by boolean filters and use backend side filtering when possible, as described in Query by Conditions.

6. Find by vector: Retrieve existing Documents by .embedding via find() using nearest neighbor search or approximate nearest neighbor search, as described in Find Nearest Neighbours.

The above tasks are often atomic operations in the high-level DocArray API. Hence, understanding their performance gives users a good estimation of the experience when using DocumentArray with different backends.

### Parametric combinations#

Most of these document stores use their own implementation of HNSW (an approximate nearest neighbor search algorithm) but with different parameters:

1. ef_construct - the HNSW build parameter that controls the index time/index accuracy. Higher ef_construct leads to longer construction, but better index quality.

2. m - maximum connections, the number of bi-directional links created for every new element during construction. Higher m works better on datasets with high intrinsic dimensionality and/or high recall, while lower m works better for datasets with low intrinsic dimensionality and/or low recall.

3. ef - the size of the dynamic list for the nearest neighbors. Higher ef at search leads to more accuracy but slower performance.

## Experiment setup#

We are interested in the single query performance on the above six tasks with different combinations of the above three parameters. Single query performance is measured by evaluating one Document at a time, repeatedly for tasks 2, 3, 4, 5, and 6. Finally the average number is reported.

We now elaborate the setup of our experiments. First some high-level statistics of the experiment:

Parameter

Value

Number of created Documents

1,000,000

Number of Documents on tasks 2, 3, 4, 5, 6

1

Dimension of .embedding

128

Number of results for the task “Find by vector”

10,000

Each Document follows the structure:

{
"id": "94ee6627ee7f582e5e28124e78c3d2f9",
"tags": {"i": 10},
"embedding": [0.49841760378680844, 0.703959752118305, 0.6920759535687985, 0.10248648858410625, ...]
}


We use Recall@K value as an indicator of search quality. The in-memory and SQLite store do not implement approximate nearest neighbor search but use exhaustive search instead. Hence, they give the maximum Recall@K but are the slowest.

The experiments were conducted on an AWS EC2 t2.2xlarge instance (Intel Xeon CPU E5-2676 v3 @ 2.40GHz) with Python 3.10.6 and DocArray 0.18.2.

As Weaviate, Qdrant, ElasticSearch, and Redis follow a client/server pattern, we set them up with their official Docker images in a single node configuration, with 32 GB of RAM allocated. That is, only 1 replica and shard are operated during the benchmarking. We did not opt for a cluster setup because our benchmarks mainly aim to assess the capabilities of a single instance of the server.

## Latency result#

m

ef_construct

ef

Recall@10

Find by vector (s)

Find by condition (s)

Create 1M (s)

Update (ms)

Delete (ms)

N/A

N/A

N/A

1.000

2.37

11.17

1.06

0.17

0.05

0.14

m

ef_construct

ef

Recall@10

Find by vector (ms)

Find by condition (ms)

Create 1M (s)

Update (ms)

Delete (ms)

16

64

32

0.873

1.42

0.40

114.30

0.36

12.93

18.01

16

64

64

0.942

1.51

0.37

114.18

0.38

14.43

15.38

16

64

128

0.977

1.76

0.39

135.75

0.35

12.30

13.66

16

64

256

0.986

1.98

0.36

111.66

0.32

12.39

14.51

16

128

32

0.897

1.43

0.37

134.94

0.34

17.82

18.08

16

128

64

0.960

1.53

0.38

148.67

0.36

24.42

46.17

16

128

128

0.988

1.67

0.37

136.90

0.37

13.76

31.10

16

128

256

0.996

1.99

0.37

134.40

0.36

13.95

30.39

16

256

32

0.905

1.51

0.37

200.29

0.37

16.94

18.10

16

256

64

0.965

1.54

0.37

186.36

0.36

32.40

45.42

16

256

128

0.990

1.68

0.39

173.68

0.37

12.42

14.60

16

256

256

0.997

2.07

0.36

183.66

0.36

18.86

35.82

32

64

32

0.895

1.49

0.37

116.49

0.33

12.63

17.55

32

64

64

0.954

1.59

0.37

112.83

0.34

11.74

12.26

32

64

128

0.983

1.75

0.36

114.32

0.37

17.26

16.86

32

64

256

0.993

2.06

0.37

114.64

0.34

14.64

15.88

32

128

32

0.930

1.52

0.38

142.51

0.35

14.17

15.93

32

128

64

0.975

1.58

0.40

156.41

0.34

16.17

31.42

32

128

128

0.993

1.81

0.37

147.05

0.35

19.81

39.87

32

128

256

0.998

2.15

0.38

144.64

0.34

29.62

40.21

32

256

32

0.946

1.49

0.38

196.37

0.36

20.55

15.37

32

256

64

0.984

1.62

0.37

211.81

0.35

32.65

35.15

32

256

128

0.996

1.88

0.37

194.97

0.33

12.72

13.93

32

256

256

0.999

2.25

0.37

204.65

0.35

22.13

31.54

m

ef_construct

ef

Recall@10

Find by vector (ms)

Find by condition (ms)

Create 1M (s)

Update (ms)

Delete (ms)

16

64

32

0.965

3.50

403.70

448.99

3.74

1.88

3.74

16

64

64

0.986

4.11

396.10

453.71

3.25

1.80

3.95

16

64

128

0.995

5.09

418.13

456.74

1.59

2.00

4.03

16

64

256

0.998

5.24

410.67

459.59

1.57

1.98

4.00

16

128

32

0.974

5.03

412.48

462.62

1.45

1.90

4.08

16

128

64

0.993

5.13

392.27

460.42

1.56

1.79

3.81

16

128

128

0.998

4.32

379.69

461.63

1.48

1.86

3.96

16

128

256

0.999

5.67

381.22

459.57

1.53

1.79

3.85

16

256

32

0.982

5.26

387.67

462.77

1.58

1.80

4.07

16

256

64

0.995

5.94

386.60

463.52

1.47

1.92

3.96

16

256

128

0.998

5.76

385.84

463.80

1.58

1.78

4.11

16

256

256

0.999

6.29

393.34

464.37

1.62

1.84

4.03

32

64

32

0.969

4.53

390.39

459.58

1.54

1.83

3.80

32

64

64

0.992

3.94

399.62

459.31

1.56

1.85

4.10

32

64

128

0.997

5.34

390.16

458.17

1.62

1.87

3.85

32

64

256

0.999

5.51

426.42

459.17

1.50

1.97

4.00

32

128

32

0.983

5.42

385.82

460.31

1.57

1.88

3.99

32

128

64

0.995

4.26

381.91

462.88

1.51

1.81

3.89

32

128

128

0.998

5.69

389.73

462.65

1.47

1.85

3.82

32

128

256

0.999

6.05

399.89

464.31

1.55

1.85

3.76

32

256

32

0.990

6.13

385.43

463.11

1.48

1.91

3.82

32

256

64

0.997

3.96

397.49

462.75

1.46

1.87

3.92

32

256

128

0.999

3.82

375.39

464.36

1.45

1.79

3.95

32

256

256

0.999

4.42

381.84

462.36

1.48

1.67

3.83

m

ef_construct

ef

Recall@10

Find by vector (ms)

Find by condition (ms)

Create 1M (s)

Update (ms)

Delete (ms)

16

64

32

0.871

4.84

2.30

574.92

7.80

4.88

19.43

16

64

64

0.939

4.99

2.38

580.66

4.52

4.15

9.42

16

64

128

0.977

5.44

2.40

577.36

2.62

3.81

9.66

16

64

256

0.984

6.43

2.37

639.69

2.55

3.62

9.63

16

128

32

0.897

4.82

2.47

655.69

4.51

4.61

26.50

16

128

64

0.960

5.11

2.34

659.43

2.64

4.23

26.89

16

128

128

0.988

7.20

2.36

818.47

4.55

4.59

34.54

16

128

256

0.996

6.38

2.38

659.77

2.62

4.81

26.89

16

256

32

0.906

4.86

2.32

787.04

2.58

5.29

26.00

16

256

64

0.965

5.09

2.49

782.85

2.62

5.36

26.50

16

256

128

0.990

5.60

2.35

784.30

2.65

5.16

25.51

16

256

256

0.997

6.49

2.30

780.20

2.60

5.05

26.55

32

64

32

0.895

4.62

2.34

1022.22

4.30

3.87

22.42

32

64

64

0.955

7.08

2.38

939.90

15.23

3.58

25.71

32

64

128

0.983

5.39

2.42

1001.35

2.75

3.57

21.51

32

64

256

0.995

7.92

2.37

981.43

2.71

3.64

29.34

32

128

32

0.929

4.92

2.49

675.61

4.50

5.06

8.61

32

128

64

0.975

5.15

2.39

673.31

4.49

5.16

27.09

32

128

128

0.993

7.26

2.45

1297.00

4.47

5.34

6.04

32

128

256

0.998

6.38

2.40

1383.55

2.62

4.69

8.49

32

256

32

0.946

6.46

2.40

1846.17

4.63

4.94

6.51

32

256

64

0.984

6.29

3.14

1926.27

2.56

6.99

21.92

32

256

128

0.996

6.36

2.41

1364.68

4.42

5.66

25.66

32

256

256

0.999

6.64

2.58

1966.97

2.64

5.91

22.22

m

ef_construct

ef

Recall@10

Find by vector (ms)

Find by condition (ms)

Create 1M (s)

Update (ms)

Delete (ms)

16

64

32

0.889

4.39

7.50

508.94

14.04

70.96

69.41

16

64

64

0.947

5.51

6.31

449.22

11.92

41.26

34.40

16

64

128

0.980

7.20

6.36

434.35

12.91

71.19

57.10

16

64

256

0.990

8.81

5.93

504.32

11.71

67.09

57.06

16

128

32

0.897

4.24

6.45

688.20

14.00

72.93

61.18

16

128

64

0.953

5.10

6.64

678.95

15.25

47.03

43.08

16

128

128

0.981

6.43

7.11

719.78

12.25

55.61

46.85

16

128

256

0.993

8.59

7.01

720.77

16.59

64.65

58.07

16

256

32

0.902

4.37

6.46

1,048.13

13.13

68.83

71.74

16

256

64

0.958

5.43

7.19

1,138.32

18.90

73.47

62.13

16

256

128

0.983

6.60

6.54

1,077.97

11.58

73.65

56.86

16

256

256

0.993

8.80

6.80

1,108.34

12.93

60.73

47.59

32

64

32

0.945

5.02

7.32

471.34

11.26

69.82

55.91

32

64

64

0.976

6.18

6.48

480.60

11.58

51.82

43.04

32

64

128

0.992

7.29

7.32

527.22

11.92

72.21

57.79

32

64

256

0.997

11.42

6.77

487.11

11.72

52.50

46.61

32

128

32

0.954

4.90

6.73

790.79

13.68

69.82

66.17

32

128

64

0.984

5.72

7.00

812.03

12.65

48.82

42.13

32

128

128

0.996

7.65

7.46

861.62

12.32

61.79

57.73

32

128

256

0.999

10.44

6.61

840.29

14.27

67.59

58.75

32

256

32

0.959

4.80

6.69

1,424.29

11.77

68.75

73.07

32

256

64

0.987

6.08

7.51

1,506.04

15.66

66.59

55.46

32

256

128

0.997

8.02

6.63

1,408.87

11.89

72.99

65.46

32

256

256

0.999

11.55

7.69

1,487.95

13.37

50.19

58.59

m

ef_construct

ef

Recall@10

Find by vector (ms)

Find by condition (ms)

Create 1M (s)

Update (ms)

Delete (ms)

16

64

32

0.872

1.67

0.63

563.15

1.00

1.88

25.58

16

64

64

0.941

1.78

0.63

563.27

0.98

1.85

25.17

16

64

128

0.976

1.98

0.70

563.09

0.95

1.99

24.89

16

64

256

0.991

2.32

0.56

562.37

0.91

2.01

25.39

16

128

32

0.897

1.73

0.62

754.93

0.91

2.96

25.36

16

128

64

0.959

1.78

0.51

721.33

0.89

2.31

23.23

16

128

128

0.988

2.37

0.70

775.26

1.24

4.25

28.60

16

128

256

0.997

2.63

0.64

799.26

1.06

2.72

27.36

16

256

32

0.905

1.70

0.58

1091.72

0.93

3.20

10.65

16

256

64

0.965

2.06

0.66

1196.05

1.03

5.24

28.84

16

256

128

0.990

2.33

0.62

1232.47

1.02

3.67

27.35

16

256

256

0.998

2.80

0.67

1203.37

1.05

4.44

27.85

32

64

32

0.896

1.74

0.56

625.56

0.85

2.05

6.05

32

64

64

0.954

1.86

0.65

626.49

0.92

1.74

25.02

32

64

128

0.982

2.05

0.56

626.09

0.94

1.79

25.99

32

64

256

0.994

2.47

0.59

625.44

0.99

1.64

25.05

32

128

32

0.930

1.79

0.73

871.87

0.94

8.99

4.52

32

128

64

0.975

2.10

0.67

953.10

1.06

2.35

27.11

32

128

128

0.993

2.49

0.69

921.87

1.03

3.06

27.58

32

128

256

0.998

3.06

0.64

926.96

1.06

2.45

27.27

32

256

32

0.947

1.82

0.59

1315.16

0.92

4.21

9.03

32

256

64

0.984

2.28

0.79

1489.83

1.05

4.92

29.27

32

256

128

0.996

2.75

0.79

1511.17

1.05

4.03

28.48

32

256

256

0.999

3.15

0.63

1534.68

1.03

3.26

28.19

m

ef_construct

ef

Recall@10

Find by vector (s)

Find by condition (s)

Create 1M (s)

Update (ms)

Delete (s)

N/A

N/A

N/A

1.000

54.32

78.63

16,421.51

1.09

0.40

28.87

## QPS result#

When we consider each query as a Document, we can convert the above metrics into query/document per second, i.e. QPS/DPS. Higher values are better.

m

ef_construct

ef

Recall@10

Find by vector

Find by condition

Create 1M

Update

Delete

N/A

N/A

N/A

1.000

0.42

0.09

947,284

6,061

21,505

7,246

m

ef_construct

ef

Recall@10

Find by vector

Find by condition

Create 1M

Update

Delete

16

64

32

0.873

706

2,519

8,749

2,762

77

56

16

64

64

0.942

662

2,674

8,758

2,625

69

65

16

64

128

0.977

570

2,597

7,366

2,825

81

73

16

64

256

0.986

504

2,762

8,956

3,155

81

69

16

128

32

0.897

698

2,710

7,411

2,976

56

55

16

128

64

0.960

652

2,611

6,726

2,786

41

22

16

128

128

0.988

599

2,670

7,304

2,721

73

32

16

128

256

0.996

504

2,729

7,440

2,751

72

33

16

256

32

0.905

663

2,695

4,993

2,681

59

55

16

256

64

0.965

648

2,695

5,366

2,762

31

22

16

256

128

0.990

594

2,540

5,758

2,730

81

68

16

256

256

0.997

483

2,786

5,445

2,786

53

28

32

64

32

0.895

671

2,674

8,585

3,003

79

57

32

64

64

0.954

629

2,725

8,863

2,915

85

82

32

64

128

0.983

572

2,762

8,747

2,710

58

59

32

64

256

0.993

487

2,681

8,723

2,976

68

63

32

128

32

0.930

657

2,625

7,017

2,882

71

63

32

128

64

0.975

632

2,500

6,394

2,959

62

32

32

128

128

0.993

553

2,703

6,800

2,825

50

25

32

128

256

0.998

465

2,660

6,914

2,985

34

25

32

256

32

0.946

672

2,646

5,092

2,809

49

65

32

256

64

0.984

618

2,703

4,721

2,874

31

28

32

256

128

0.996

531

2,734

5,129

3,040

79

72

32

256

256

0.999

445

2,740

4,886

2,874

45

32

m

ef_construct

ef

Recall@10

Find by vector

Find by condition

Create 1M

Update

Delete

16

64

32

0.965

286

2.48

2,227

267

532

267

16

64

64

0.986

244

2.52

2,204

308

557

253

16

64

128

0.995

197

2.39

2,189

629

501

248

16

64

256

0.998

191

2.44

2,176

636

505

250

16

128

32

0.974

199

2.42

2,162

691

527

245

16

128

64

0.993

195

2.55

2,172

641

559

263

16

128

128

0.998

231

2.63

2,166

675

537

253

16

128

256

0.999

176

2.62

2,176

653

559

260

16

256

32

0.982

190

2.58

2,161

633

554

246

16

256

64

0.995

168

2.59

2,157

680

521

253

16

256

128

0.998

174

2.59

2,156

632

561

244

16

256

256

0.999

159

2.54

2,153

616

543

248

32

64

32

0.969

221

2.56

2,176

648

547

263

32

64

64

0.992

254

2.50

2,177

643

541

244

32

64

128

0.997

187

2.56

2,183

616

535

260

32

64

256

0.999

182

2.35

2,178

666

508

250

32

128

32

0.983

184

2.59

2,172

635

533

251

32

128

64

0.995

235

2.62

2,160

663

554

257

32

128

128

0.998

176

2.57

2,161

682

540

262

32

128

256

0.999

165

2.50

2,154

647

541

266

32

256

32

0.990

163

2.59

2,159

676

524

262

32

256

64

0.997

252

2.52

2,161

685

535

255

32

256

128

0.999

262

2.66

2,154

687

560

253

32

256

256

0.999

226

2.62

2,163

676

598

261

m

ef_construct

ef

Recall@10

Find by vector

Find by condition

Create 1M

Update

Delete

16

64

32

0.871

207

436

1,739

128

205

51

16

64

64

0.939

200

421

1,722

221

241

106

16

64

128

0.977

184

416

1,732

381

262

103

16

64

256

0.984

156

422

1,563

392

277

104

16

128

32

0.897

207

405

1,525

222

217

38

16

128

64

0.960

196

427

1,516

380

236

37

16

128

128

0.988

139

424

1,222

220

218

29

16

128

256

0.996

157

421

1,516

381

208

37

16

256

32

0.906

206

430

1,271

388

189

38

16

256

64

0.965

197

402

1,277

382

187

38

16

256

128

0.990

179

425

1,275

378

194

39

16

256

256

0.997

154

435

1,282

384

198

38

32

64

32

0.895

217

427

978

233

258

45

32

64

64

0.955

141

421

1,064

66

279

39

32

64

128

0.983

185

414

999

364

280

46

32

64

256

0.995

126

422

1,019

370

275

34

32

128

32

0.929

203

402

1,480

222

198

116

32

128

64

0.975

194

418

1,485

223

194

37

32

128

128

0.993

138

409

771

224

187

166

32

128

256

0.998

157

417

723

382

213

118

32

256

32

0.946

155

418

542

216

202

154

32

256

64

0.984

159

318

519

391

143

46

32

256

128

0.996

157

415

733

226

177

39

32

256

256

0.999

151

387

508

378

169

45

m

ef_construct

ef

Recall@10

Find by vector

Find by condition

Create 1M

Update

Delete

16

64

32

0.889

228

133

1,965

71

14

14

16

64

64

0.947

182

159

2,226

84

24

29

16

64

128

0.980

139

157

2,302

77

14

18

16

64

256

0.990

113

169

1,983

85

15

18

16

128

32

0.897

236

155

1,453

71

14

16

16

128

64

0.953

196

151

1,473

66

21

23

16

128

128

0.981

155

141

1,389

82

18

21

16

128

256

0.993

116

143

1,387

60

15

17

16

256

32

0.902

229

155

954

76

15

14

16

256

64

0.958

184

139

878

53

14

16

16

256

128

0.983

151

153

928

86

14

18

16

256

256

0.993

114

147

902

77

16

21

32

64

32

0.945

199

137

2,122

89

14

18

32

64

64

0.976

162

154

2,081

86

19

23

32

64

128

0.992

137

137

1,897

84

14

17

32

64

256

0.997

88

148

2,053

85

19

21

32

128

32

0.954

204

149

1,265

73

14

15

32

128

64

0.984

175

143

1,231

79

20

24

32

128

128

0.996

131

134

1,161

81

16

17

32

128

256

0.999

96

151

1,190

70

15

17

32

256

32

0.959

208

149

702

85

15

14

32

256

64

0.987

165

133

664

64

15

18

32

256

128

0.997

125

151

710

84

14

15

32

256

256

0.999

87

130

672

75

20

17

m

ef_construct

ef

Recall@10

Find by vector

Find by condition

Create 1M

Update

Delete

16

64

32

0.872

600

1,585

1,776

1,001

533

39

16

64

64

0.941

563

1,595

1,775

1,018

541

40

16

64

128

0.976

504

1,425

1,776

1,058

504

40

16

64

256

0.991

431

1,795

1,778

1,094

499

39

16

128

32

0.897

579

1,621

1,325

1,099

338

39

16

128

64

0.959

562

1,961

1,386

1,125

432

43

16

128

128

0.988

422

1,420

1,290

804

235

35

16

128

256

0.997

380

1,567

1,251

943

368

37

16

256

32

0.905

587

1,726

916

1,080

313

94

16

256

64

0.965

485

1,527

836

971

191

35

16

256

128

0.990

429

1,616

811

978

273

37

16

256

256

0.998

357

1,493

831

951

225

36

32

64

32

0.896

574

1,799

1,599

1,173

488

165

32

64

64

0.954

537

1,541

1,596

1,083

575

40

32

64

128

0.982

488

1,789

1,597

1,065

560

38

32

64

256

0.994

405

1,695

1,599

1,014

612

40

32

128

32

0.930

558

1,378

1,147

1,060

111

221

32

128

64

0.975

476

1,490

1,049

948

425

37

32

128

128

0.993

402

1,456

1,085

970

327

36

32

128

256

0.998

326

1,570

1,079

943

408

37

32

256

32

0.947

548

1,682

760

1,083

238

111

32

256

64

0.984

438

1,266

671

951

203

34

32

256

128

0.996

364

1,263

662

952

248

35

32

256

256

0.999

318

1,600

652

971

306

35

m

ef_construct

ef

Recall@10

Find by vector

Find by condition

Create 1M

Update

Delete

N/A

N/A

N/A

1.000

0.02

0.01

61

915

2,476

0.03

## Recall@10 vs QPS#

In particular to the find by vector queries task, the chart below depicts Recall@10 (the fraction of true nearest neighbors found, on average over all queries) against the QPS. The smaller the time values and the more upper-right in the chart, the better.

## Rationale on experiment design#

Our experiments are designed to be fair and the same across all backends while favouring document stores that benefit DocArray users the most. Note that such a benchmark was impossible to set up before DocArray, as each store has its own API and the definition of a task varies.

Our benchmark is based on the following principles:

• Cover the most important operations: We understand that some backends are better at some operations than others, and some offer better quality. Therefore, we try to benchmark on six operations (CRUD + Find by vector + Find by condition) and report quality measurement (Recall@K).

• Not just speed, but also quality: We show the trade-off between quality and speed as you tune your parameters in each document store.

• Same experiment, same API: DocArray offers the same API across all backends and therefore we built on top of it the same benchmarking experiment. Furthermore, we made sure to run the experiment with a series of HNSW parameters for backends that support approximate nearest neighbor search. All backends are run on official Docker containers, local to the DocArray client which allows having similar network overhead. We also allocate the same resources for those Docker containers and all servers are run in a single node setup.

• Benefit users as much as possible: We offer the same conditions and resources to all backends, but our experiment favors backends that use resources efficiently. Therefore, some backends might not use the network, or use gRPC instead of HTTP, or use batch operations. We’re okay with that, as long as it benefits the DocArray and Jina user.

• Open to improvements: We are constantly improving the performance of storage backends from the DocArray side and updating benchmarks accordingly. If you believe we missed an optimization (e.g. perform an operation in batches, benefit from a recent feature in upstream, avoid unnecessary steps), feel free to raise a PR or issue. We’re open to your contributions!

## Known limitations#

• Incompleteness on the stores: We do not benchmark algorithms or ANN libraries like Faiss, Annoy, ScaNN. We only benchmark backends that can be used as document stores. In fact, we do not benchmark HNSW itself, but it is used by some backends internally. Other storage backends that support vector search are not yet integrated with DocArray. We’re open to contributions to DocArray’s repository to support them.

• Client/server setup introduces random network latency: Although a real-life scenario would be the clients and server living on different machines with potentially multiple clients in parallel, we chose to keep both on the same machine and have only one client process to minimize network overhead.

• Benchmarks are conducted end-to-end: We benchmark function calls from DocArray, not just the underlying backend vector database. Therefore, results for a particular backend can be influenced (positively or negatively) by our interface. If you spot bottlenecks we would be thrilled to know about them and improve our code accordingly.

• We use similar underlying search algorithms but different implementations: In this benchmark we try a set of parameters ef, ef_construct and max_connections from HNSW applied equally on all backends. Note that there might be other parameters that storage backends can fix than might or might not be accessible and can have a big impact on performance. This means that even similar configurations cannot be easily compared.

• Benchmark is for DocArray users, not for research: This benchmark showcases what a user can expect to get from DocArray without tuning hyper-parameters of a vector database. In practice, we strongly recommend tuning them to achieve high quality results.

## Conclusions#

We hope our benchmark result can help users select the store that suits their use cases. Depending on the dataset size and the desired quality, some stores may be preferable than others. Here are some of our conclusions:

• If you’re experimenting on a dataset with fewer than 10,000 Documents, you can use the in-memory DocumentArray as-is to enjoy the best quality for nearest neighbor search with reasonable latency (say, less than 20 ms/query).

• If your dataset is large but still fits into memory (say 1 million Documents), Redis and AnnLite offer great speed in CRUD and vector search operations. In particular, AnnLite is designed as a monolithic package, which saves a lot network overhead unlike other backends. Unfortunately, this also means one can not scale-out AnnLite natively. Nonetheless, if you are using DocArray with Jina together, you can always leverage Jina’s sharding features, an agnostic solution to scale out any document store. This of course also includes AnnLite.

• Finally, if your dataset does not fit in memory, and you do not care much about the speed of nearest neighbor search, you can use SQLite as storage. Otherwise, Weaviate, Qdrant and Elasticsearch are good options.