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import argparse
import functools
import random
from time import perf_counter
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from docarray import Document, DocumentArray
from rich.console import Console
from rich.table import Table
n_index_values = [1_000_000]
n_query = 1
D = 128
TENSOR_SHAPE = (512, 256)
K = 10
n_vector_queries = 1000
np.random.seed(123)
parser = argparse.ArgumentParser()
parser.add_argument(
'--default-hnsw',
help='Whether to use default HNSW configurations',
action='store_true',
)
args = parser.parse_args()
times = {}
def timer(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
start = perf_counter()
res = func(*args, **kwargs)
return (perf_counter() - start, res)
return wrapper
@timer
def create(da, docs):
da.extend(docs)
@timer
def read(da, ids):
da[ids]
@timer
def update(da, docs):
da[[d.id for d in docs]] = docs
@timer
def delete(da, ids):
del da[ids]
@timer
def find_by_condition(da, query):
da.find(query)
@timer
def find_by_vector(da, query):
return da.find(query, limit=K)
def get_docs(n, n_dim, tensor_shape, n_query):
return [
Document(
embedding=np.random.rand(n_dim),
# tensor=np.random.rand(*tensor_shape),
tags={'i': int(i / n_query)},
)
for i in range(n)
]
def fmt(value, unit):
return '{:.3f} {}'.format(value, unit)
def recall(predicted, relevant, eval_at):
if eval_at == 0:
return 0.0
predicted_at_k = predicted[:eval_at]
n_predicted_and_relevant = len(
set(predicted_at_k[:, 'id']).intersection(set(relevant[:, 'id']))
)
return n_predicted_and_relevant / len(relevant)
if args.default_hnsw:
storage_backends = [
# ('memory', None),
# ('sqlite', None),
# (
# 'annlite',
# {'n_dim': D},
# ),
(
'qdrant',
{
'n_dim': D,
'scroll_batch_size': 8,
'port': '41233',
},
),
(
'weaviate',
{
'n_dim': D,
'port': '41234',
},
),
(
'elasticsearch',
{
'n_dim': D,
'port': '41235',
},
),
(
'redis',
{
'n_dim': D,
'port': '41236',
},
),
]
else:
storage_backends = [
# ('memory', None),
# ('sqlite', None),
# (
# 'annlite',
# {
# 'n_dim': D,
# 'ef_construction': 100,
# 'ef_search': 100,
# 'max_connection': 16,
# },
# ),
(
'qdrant',
{
'n_dim': D,
'scroll_batch_size': 8,
'ef_construct': 100,
'm': 16,
'port': '41233',
},
),
(
'weaviate',
{
'n_dim': D,
'ef': 100,
'ef_construction': 100,
'max_connections': 16,
'port': '41234',
},
),
(
'elasticsearch',
{'n_dim': D, 'ef_construction': 100, 'm': 16, 'port': '41235'},
),
('redis', {'n_dim': D, 'ef_construction': 100, 'm': 16, 'port': '41236'}),
]
table = Table(
title=f'DocArray Benchmarking n_index={n_index_values[-1]} n_query={n_query} D={D} K={K}'
)
benchmark_df = pd.DataFrame(
{
'Storage Backend': [],
'Indexing time (C)': [],
'Query (R)': [],
'Update (U)': [],
'Delete (D)': [],
'Find by vector': [],
f'Recall at k={K} for vector search': [],
'Find by condition': [],
}
)
for col in benchmark_df.columns:
table.add_column(col)
console = Console()
find_by_vector_values = {str(n_index): [] for n_index in n_index_values}
create_values = {str(n_index): [] for n_index in n_index_values}
for idx, n_index in enumerate(n_index_values):
console.print(f'generating {n_index} docs...')
docs = get_docs(n_index, D, TENSOR_SHAPE, n_query)
docs_to_delete = random.sample(docs, n_query)
docs_to_update = random.sample(docs, n_query)
vector_queries = [np.random.rand(n_query, D) for _ in range(n_vector_queries)]
ground_truth = []
for backend, config in storage_backends:
try:
console.print('Backend:', backend.title())
# for n_i in n_index:
if not config:
da = DocumentArray(storage=backend)
else:
da = DocumentArray(storage=backend, config=config)
console.print(f'indexing {n_index} docs ...')
create_time, _ = create(da, docs)
# for n_q in n_query:
console.print(f'reading {n_query} docs ...')
read_time, _ = read(
da,
random.sample([d.id for d in docs], n_query),
)
console.print(f'updating {n_query} docs ...')
update_time, _ = update(da, docs_to_update)
console.print(f'deleting {n_query} docs ...')
delete_time, _ = delete(da, [d.id for d in docs_to_delete])
console.print(
f'finding {n_query} docs by vector averaged {n_vector_queries} times ...'
)
if backend == 'sqlite':
find_by_vector_time, result = find_by_vector(da, vector_queries[0])
recall_at_k = recall(result, ground_truth[0], K)
else:
recall_at_k_values = []
find_by_vector_times = []
for i, query in enumerate(vector_queries):
find_by_vector_time, results = find_by_vector(da, query)
find_by_vector_times.append(find_by_vector_time)
if backend == 'memory':
ground_truth.append(results)
recall_at_k_values.append(1)
else:
recall_at_k_values.append(recall(results, ground_truth[i], K))
recall_at_k = sum(recall_at_k_values) / len(recall_at_k_values)
find_by_vector_time = sum(find_by_vector_times) / len(
find_by_vector_times
)
console.print(f'finding {n_query} docs by condition ...')
find_by_condition_time, _ = find_by_condition(da, {'tags__i': {'$eq': 0}})
if idx == len(n_index_values) - 1:
table.add_row(
backend.title(),
fmt(create_time, 's'),
fmt(read_time * 1000, 'ms'),
fmt(update_time * 1000, 'ms'),
fmt(delete_time * 1000, 'ms'),
fmt(find_by_vector_time, 's'),
'{:.3f}'.format(recall_at_k),
fmt(find_by_condition_time, 's'),
)
benchmark_df.append(
pd.DataFrame(
[
[
backend.title(),
create_time,
read_time,
update_time,
delete_time,
find_by_vector_time,
recall_at_k,
find_by_condition_time,
]
],
columns=benchmark_df.columns,
)
)
find_by_vector_values[str(n_index)].append(find_by_vector_time)
create_values[str(n_index)].append(create_time)
except Exception as e:
console.print(f'Storage Backend {backend} failed: {e}')
find_df = pd.DataFrame(find_by_vector_values)
find_df.index = [backend for backend, _ in storage_backends]
find_df = find_df.drop(['sqlite'])
print(find_df)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(17, 5))
find_df.plot(
kind="bar",
ax=ax1,
fontsize=16,
color=sns.color_palette('muted')[1:4],
# title='Find by vector per backend and dataset size',
# ylabel='seconds',
rot=0,
)
ax1.set_ylabel('seconds', fontsize=18)
ax1.set_title('Find by vector per backend and dataset size', fontsize=18)
threshold = 0.3
ax1.hlines(y=threshold, xmin=-20, xmax=20, linewidth=2, color='r', linestyle='--')
create_df = pd.DataFrame(create_values)
create_df.index = [backend for backend, _ in storage_backends]
create_df = create_df.drop(['memory'])
print(create_df)
create_df.plot(
kind="bar",
ax=ax2,
fontsize=16,
color=sns.color_palette('muted')[1:4],
# title='Indexing per backend and dataset size',
# ylabel='seconds',
rot=0,
)
ax2.set_ylabel('seconds', fontsize=18)
ax2.set_title('Indexing per backend and dataset size', fontsize=18)
plt.tight_layout()
ax1.legend(fontsize=15)
ax2.legend(fontsize=15)
plt.savefig('benchmark.svg')
console.print(table)
benchmark_df.to_csv('benchmark-seconds.csv')
benchmark_df['Indexing time (C)'] = benchmark_df['Indexing time (C)'].apply(
lambda value: 1_000_000 / value
)
benchmark_df['Query (R)'] = benchmark_df['Query (R)'].apply(lambda value: 1 / value)
benchmark_df['Update (U)'] = benchmark_df['Update (U)'].apply(lambda value: 1 / value)
benchmark_df['Delete (D)'] = benchmark_df['Delete (D)'].apply(lambda value: 1 / value)
benchmark_df['Find by vector'] = benchmark_df['Find by vector'].apply(
lambda value: 1 / value
)
benchmark_df['Find by condition'] = benchmark_df['Find by condition'].apply(
lambda value: 1 / value
)
benchmark_df.to_csv('benchmark-qps.csv')