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Motivation

Redshift is the distributed data warehousing solution by AWS. The redshift monitoring console displays:

1. Hardware metrics like CPU, Disk Space, Read/Write IOPs for the clusters.
2. Query level information such as:
   a. Expected versus actual execution plan
   b. Username query mapping
   c. Time Taken for query

Redeye Overview

The tool gathers the following metrics on redshift performance:

1. Hardware Metrics:
   a. CPU Utilization
   b. Disk Space Utilization
   c. Read/Write IOPs
   d. Read Latency/Throughput
   e. Write Latency/Throughput
   f. Network Transmit/Throughput

2. Software Metrics:
   a. Aggregate Metrics:
   - Queries fired by user
   - Queries in Queue/Running/Returning State in each queue
   - Average time taken in Queue/Running/Returning State in each queue
   b. Query Level Metrics: Number of diskhits, Number of rows broadcast across nodes, queue used and user at a query_id granularity
   c. Table Level Metrics: Least and most used tables in warehouse

There is a short post highlighting some of the insights that can be gained from this utility.

Requirements:

For this tool to work, you will need:

1. Statsd endpoint
2. Opentsdb endpoint
3. MySQL endpoint
4. Redshift Credentials

Quick Setup

vim $REDYEYE/redshift_monitoring/base_config.py

This file contains configuration properties common to all the clusters. Edit the statsd, opentsdb and mysql endpoints here.

mkdir $REDEYE/cluster_new
cp $REDEYE/cluster_dir/config.py $REDEYE/cluster_new/
vim $REDEYE/cluster_new/config.py

This file will contain configuration specific to your redshift console. Change the credentials to point to your cluster. You should provide username and password that can access the redshift system tables.

cp $REDEYE/cluster_dir/start_monitoring.py $REDEYE/cluster_new/
python $REDEYE/cluster_new/start_monitoring.py

The tool has been designed to run for an hour. It can be scheduled as part of a workflow scheduler like Azkaban to keep the monitoring persistent.