Performance comparison: ReductStore vs. Minio

Jan 7, 2023. | By: Alexey Timin

We often use blob storage like S3, if we need to store data of different formats and sizes somewhere in the cloud or in our internal storage. Minio is an S3 compatible storage which you can run on your private cloud, bare-metal server or even on an edge device. You can also adapt it to keep historical data as a time series of blobs. The most straightforward solution would be to create a folder for each data source and save objects with timestamps in their names:


If you need to query data, you should request a list of objects in the cv_camera folder and filter them by name according to the given time interval. This approach is simple to implement, but it has some disadvantages:

  • the more objects the folder has, the longer the querying takes.
  • big overhead for small objects: timestamps as strings and minimum file size is 1Kb or 512 due to the block size of the file system.
  • FIFO quota, to remove old data when we reach a certain limit, may not work for intensive write operations.

ReductStore aims to solve these issues. It has a strong FIFO quota, an HTTP API for querying data via time intervals, and it composes objects (or records) into blocks for efficient disk usage and search.

Minio and ReductStore have Python SDKs, so we can use them to implement read and write operations and compare the performance.

Read/Write Data With Minio

For benchmarks, we create two functions to write and read CHUNK_COUNT chunks:

from minio import Minio
import time

minio_client = Minio("", access_key="minioadmin", secret_key="minioadmin", secure=False)

def write_to_minio():
    count = 0
    for i in range(CHUNK_COUNT):
        count += CHUNK_SIZE
        object_name = f"data/{str(int(time.time_ns() / 1000))}.bin"
        minio_client.put_object(BUCKET_NAME, object_name, io.BytesIO(CHUNK),
    return count  # count data to print it in main function

def read_from_minio(t1, t2):
    count = 0

    t1 = str(int(t1 * 1000_000))
    t2 = str(int(t2 * 1000_000))

    for obj in minio_client.list_objects("test", prefix="data/"):
        if t1 <= obj.object_name[5:-4] <= t2:
            resp = minio_client.get_object("test", obj.object_name)
            count += len(

    return count

You can see that minio_client doesn’t provide any API query data with patterns, so we have to browse the whole folder on the client side to find the needed object. If you have billions of objects, it stops working. You have to store object paths in some time series database or create a hierarchy of folders, e.g., create one new folder per day.

Read/Write Data With ReductStore

With ReductStore this is a much easier:

from reduct import Client as ReductClient

reduct_client = ReductClient("")

async def write_to_reduct():
    count = 0
    bucket = await reduct_client.create_bucket("test", exist_ok=True)
    for i in range(CHUNK_COUNT):
        await bucket.write("data", CHUNK)
        count += CHUNK_SIZE
    return count

async def read_from_reduct(t1, t2):
    count = 0
    bucket = await reduct_client.get_bucket("test")
    async for rec in bucket.query("data", int(t1 * 1000000), int(t2 * 1000000)):
        count += len(await rec.read_all())
    return count


When we have the write/read functions, we can finally write our benchmarks:

import io
import random
import time
import asyncio

from minio import Minio
from reduct import Client as ReductClient

CHUNK_SIZE = 100000
BUCKET_NAME = "test"

CHUNK = random.randbytes(CHUNK_SIZE)

minio_client = Minio("", access_key="minioadmin", secret_key="minioadmin", secure=False)
reduct_client = ReductClient("")

# Our function were here..

if __name__ == "__main__":
    print(f"Chunk size={CHUNK_SIZE / 1000_000} Mb, count={CHUNK_COUNT}")
    ts = time.time()
    size = write_to_minio()
    print(f"Write {size / 1000_000} Mb to Minio: {time.time() - ts} s")

    ts_read = time.time()
    size = read_from_minio(ts, time.time())
    print(f"Read {size / 1000_000} Mb from Minio: {time.time() - ts_read} s")

    loop = asyncio.new_event_loop();
    ts = time.time()
    size = loop.run_until_complete(write_to_reduct())
    print(f"Write {size / 1000_000} Mb to ReductStore: {time.time() - ts} s")

    ts_read = time.time()
    size = loop.run_until_complete(read_from_reduct(ts, time.time()))
    print(f"Read {size / 1000_000} Mb from ReductStore: {time.time() - ts_read} s")

For testing, we need to run the databases. It is easy to do with docker-compose:

    image: reductstorage/engine:v1.0.1
      - ./reduct-data:/data
      - 8383:8383

    image: minio/minio
      - ./minio-data:/data
    command: minio server /data --console-address :9002
      - 9000:9000
      - 9002:9002

Run the docker compose configuration and the benchmarks:

docker-compose up -d


The script print the results for given CHUNK_SIZE and CHUNK_COUNT. On my device, I got the following numbers:

Chunk Operation Minio ReductStore
10.0 Mb (100 requests) Write 8.69 s 0.53 s
  Read 1.19 s 0.57 s
1.0 Mb (1000 requests) Write 12.66 s 1.30 s
  Read 2.04 s 1.38 s
.1 Mb (10000 requests) Write 61.86 s 13.73 s
  Read 9.39 s 15.02 s

As you can see, ReductStore is always faster for write operations (16 times faster for 10 Mb blobs!!!) and a bit slower for reading when we have many small objects. You may notice that the speed decreases for both databases when we reduce the size of the chunks. This can be explained with HTTP overhead because we spend a dedicated HTTP request for each write or read operation.


ReductStore could be a good option for applications where you need to store blobs historically with timestamps and write data continuously. It has a strong FIFO quota to avoid problems with disk space, and it is very fast for intensive write operations.


#Comparisons #Computer-vision #Iot


ReductStore is a time series database designed specifically for storing and managing large amounts of blob data. It has high performance for writing and real-time querying, making it suitable for edge computing, computer vision, and IoT applications. ReductStore is 100% open source under Mozilla Public License v2.0.