使用OmniSci与pymapd查询5M行数据排序报错，求解决办法

Hey there! Let's break down your problem and walk through the practical options you have here.

1. 让OmniSci强行执行排序 #

OmniSci throws the Sorting the result would be too slow error because it's designed to prioritize real-time, low-latency analysis by default—large-scale sorting operations can hog server resources and cripple performance. But if you're okay with waiting, you can bypass this restriction with a simple session parameter:

First, run this command to allow slow queries:

SET ALLOW_SLOW_QUERIES = 1;

Then execute your original sorted query:

SELECT a, b, c, d FROM my_table ORDER BY a, b;

Important Notes: #

• This will consume a huge amount of CPU and memory on your OmniSci server, especially with 5 million rows. If the server is shared with other workloads, expect slowdowns or even temporary unavailability for other users.
• If the server lacks sufficient memory, the sorting process might crawl to a halt or fail entirely.

2. Offload sorting to pandas #

A more stable alternative is to pull the unsorted data locally first, then handle sorting with pandas. Here's how to do it:

import pymapd
import pandas as pd

# Establish connection
conn = pymapd.connect(user="your_username", password="your_password", host="your_host", dbname="your_db")

# Fetch unsorted data from OmniSci
raw_df = conn.execute("SELECT a, b, c, d FROM my_table").to_df()

# Sort with pandas
sorted_df = raw_df.sort_values(by=['a', 'b'])

Pros & Cons: #

• Pros: No impact on your OmniSci server's performance; you can directly work with the sorted data in pandas for follow-up analysis, which is super flexible.
• Cons: Pulling 5 million rows eats up network bandwidth, and you'll need enough local memory to hold the dataset. If memory is tight, you can split the data into chunks, sort each chunk, then merge the sorted results (though this adds a bit of complexity).

Which option should you choose? #

• Go with OmniSci-side sorting only if you have a dedicated, resource-rich server with no competing workloads.
• For shared servers or if your local machine has enough memory, offloading to pandas is the safer, more reliable choice—it keeps your database free for other tasks.

内容的提问来源于stack exchange，提问作者Udi