Test effect of many vs. fewer columns of data on client-side speed of queries against Amazon DynamoDB.
Refer to the write-up at https://aws.amazon.com/blogs/database/optimizing-amazon-dynamodb-scan-latency-through-schema-design/
Thanks to switch180 & Chad Tindel for their contributions and suggestions.
- There are many variables to consider when measuring latency, such as distance between server and client, available CPU & memory, bandwidth, etc. Testing in your own environment is the most reliable benchmark.
usage: run.py [-h] [--table TABLE] [--schema SCHEMA] [--seed SEED]
[--query QUERY] [--region REGION] [--endpoint ENDPOINT]
[--rounds ROUNDS] [--skip-seed] [--mode MODE] [--rcu RCU]
[--wcu WCU]
Test DynamoDB query API speed.
optional arguments:
-h, --help show this help message and exit
--table TABLE dynamodb table to use for testing
--schema SCHEMA
--seed SEED number of items to write to table
--query QUERY number of items to query per API call
--region REGION Region name for auth and endpoint construction
--endpoint ENDPOINT Override endpoint
--rounds ROUNDS Number of rounds
--skip-seed
--mode MODE Table capacity mode = 'PAY_PER_REQUEST' or
'PROVISIONED')
--rcu RCU Read capacity units (RCUs) (only for provisioned
capacity mode)
--wcu WCU Write capacity units (WCUs) (only for provisioned
capacity mode)
Default settings are below:
# Default settings:
# -----------------------------
# region = us-east-1
# table = query_testing_table
# schema = schemas/long.schema
# seed = 5000
# query = 2500
# rounds = 10
# mode = PROVISIONED
# rcu = 200
# wcu = 100
# endpoint = (blank) - only needed if running local DDB
- Run test with default settings:
python run.py