Tag: Monitoring

Infrastructure Kubernetes (EKS) Cost Monitoring & Optimization

As an integral part of the DevOps culture, Cost Monitoring & Optimization is the most important element in monitoring and optimizing the use of infrastructure, especially in today’s cloud computing era. In this event, we will discuss the strategy of cost monitoring & optimization of infrastructure in using Kubernetes (EKS) on AWS.

In this session, we will discuss provisioning estimation costs, autoscaling systems, downscale schedules, and alerting systems for cost usage notifications from cost limitation budgets.

Don’t miss ZX Talk – Infrastructure Kubernetes (EKS) Cost Monitoring & Optimization which will be held on:

Date: Thursday, 23 June 2022
Time: 14.00 – 15.30 (2 – 3.30 pm) Jakarta
Place: Virtual Meet

ZXTalk: Infrastructure Kubernetes (EKS) Cost Monitoring & Optimization

Registration Links:
https://bit.ly/3Qy7Ejx

#ZebraX #DigitalTransformation #MonitoringTools #Kubernetes #CostMonitoring #Optimization #Industry40 #ZXTalk #AWSCommunityBuilders #DevOpsCorner

[RFC] Rate Limits API Requests

This documentation refer to rate limits of AWS and Datadog API.

A. AWS Throttle API Requests

References:
https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-request-throttling.html

Account-level throttling per Region

By default, API Gateway limits the steady-state requests per second (rps) across all APIs within an AWS account, per Region. It also limits the burst (that is, the maximum bucket size) across all APIs within an AWS account, per Region. In API Gateway, the burst limit corresponds to the maximum number of concurrent request submissions that API Gateway can fulfill at any moment without returning 429 Too Many Requests error responses. For more information on throttling quotas, see Amazon API Gateway quotas and important notes.

To help understand these throttling limits, here are a few examples, given a burst limit of 5,000 and an account-level rate limit of 10,000 requests per second in the Region:

  • If a caller submits 10,000 requests in a one-second period evenly (for example, 10 requests every millisecond), API Gateway processes all requests without dropping any.
  • If the caller sends 10,000 requests in the first millisecond, API Gateway serves 5,000 of those requests and throttles the rest in the one-second period.
  • If the caller submits 5,000 requests in the first millisecond and then evenly spreads another 5,000 requests through the remaining 999 milliseconds (for example, about 5 requests every millisecond), API Gateway processes all 10,000 requests in the one-second period without returning 429 Too Many Requests error responses.
  • If the caller submits 5,000 requests in the first millisecond and waits until the 101st millisecond to submit another 5,000 requests, API Gateway processes 6,000 requests and throttles the rest in the one-second period. This is because at the rate of 10,000 rps, API Gateway has served 1,000 requests after the first 100 milliseconds and thus emptied the bucket by the same amount. Of the next spike of 5,000 requests, 1,000 fill the bucket and are queued to be processed. The other 4,000 exceed the bucket capacity and are discarded.
  • If the caller submits 5,000 requests in the first millisecond, submits 1,000 requests at the 101st millisecond, and then evenly spreads another 4,000 requests through the remaining 899 milliseconds, API Gateway processes all 10,000 requests in the one-second period without throttling.

B. Datadog API

References:
https://docs.datadoghq.com/api/latest/rate-limits/

Rate Limits

All of the API endpoints are rate limited. Once you exceed a certain number of requests in a specific period, Datadog returns an error.

If you are rate limited, you will see a 429 in the response code. Datadog recommends to either wait the time designated by the X-RateLimit-Limit before making calls again, or you should switch to making calls at a frequency slightly longer than the X-RateLimit-Limit / X-RateLimit-Period.

Rate limits can be increased from the defaults by contacting the Datadog support team.

Regarding the API rate limit policy:

  • Datadog does not rate limit on data point/metric submission (see metrics section for more info on how the metric submission rate is handled). Limits encounter is dependent on the quantity of custom metrics based on your agreement.
  • The rate limit for metric retrieval is 100 per hour per organization.
  • The rate limit for event submission is 500,000 events per hour per organization.
  • The rate limit for event aggregation is 1000 per aggregate per day per organization. An aggregate is a group of similar events.
  • The rate limit for the Query a Timeseries API call is 1600 per hour per organization. This can be extended on demand.
  • The rate limit for the Log Query API call is 300 per hour per organization. This can be extended on demand.
  • The rate limit for the Graph a Snapshot API call is 60 per hour per organization. This can be extended on demand.
  • The rate limit for the Log Configuration API is 6000 per minute per organization. This can be extended on demand.
Rate Limit HeadersDescription
X-RateLimit-Limitnumber of requests allowed in a time period.
X-RateLimit-Periodlength of time in seconds for resets (calendar aligned).
X-RateLimit-Remainingnumber of allowed requests left in the current time period.
X-RateLimit-Resettime in seconds until next reset.

[RFC] Postmortem Report

This is sample postmortem reporting to review chronologies, provide the mitigation from the issue and solving the problem during period time

Title

  • YYYY-MM-DD Issue Name.
    eg:
    2020-09-01 Failed to Replicate Database Slave in Node-2.

Issue Summary

  • Summary of issue that describe all chronologies.
    eg:
    We had issue in replication slave server database in node-2. This issue running at 07:00 due to can’t connect the slave server DNS to DNS server master. Impacted to unable connected for some of microservices that using slave server as pointing reading / query read to database.

    List of microservices impacted:
    • Microservices 1: Auth
    • Microservices 2: OTP

Impact

  • List of microservices or other infrastructure resources impacted for this issue.
    eg:
    Impacted microservices:
    • Microservices 1: Auth
    • Microservices 2: OTP

Impacted infra:
DNS slave

Trigger

  • List of trigger issue.
    eg:
    • Cloud provider running on maintenance starting at 2020-09-01 02:00 GMT+7 and end at 2020-09-01 03:00.
    • Some of DNS changed as the impacted of maintenance.

Detection

  • List of detection issue.
    eg:
    • Detect on Metrics for failed replication (with snapshot picture)
    • Detect on Log for dns changes (with snapshot picture)

Root Cause

  • List of root cause for the issue.
    eg:
    • Slave server database in node-2 can’t running due to can’t connect to DNS server master.
    • DNS server master had been moved to other pointing address due to cloud provider maintenance.

Timeline

  • List timeline issue from beginning until end (resolved).
    eg:
    2020-09-01 07:00 Metrics show failed to replicate the slave server database in node-2
    2020-09-01 07:10 Raise the alert on P3 Escalation
    2020-09-01 07:12 Oncall ack the issue
    2020-09-01 07:15 Taking action for manual replication slave server
    2020-09-01 07:30 All Replication had been restored
    2020-09-01 07:35 Monitoring phase replication (for about 10-15 minutes)
    2020-09-01 08:00 Operation slave server database in node-2 is back to normal

Resolution & Recovery

  • List of resolution & recovery action
    eg:
    • Manual replication for slave server
    • Repointing DNS slave node-2 to new DNS master

Corrective and Preventive Measurements

  • List of action item / procedure to make correction & prevention (as mitigation)
    eg:
    • Update threshold metrics for alerting, raise to P2 for escalation level.
    • Raise open ticket for cloud provider dns issue moving impact.

Financial Impact

Product Impacted Start DateTime – End DateTime Impact Type
(Outage, Error Rates, Latency Spike)		 Monitoring Links Log Links
         
         
  • Detail of Financial Impact

Division / Team Name

List of division / team which impacted for this postmortem

Related documentation for this issue (JIRA / Confluences)

[RFC] Performance Testing K6

Monitoring Dashboard

  • Monitoring Dashboard URL

Logging

  • Logging Dashboard URL

Operations (Executors)

PIC Name Department
DevOps Engineer – 1 DevOps
DevOps Engineer – 2
QA Engineer – 1 QA
QA Engineer – 2
Software Engineer – 1 Engineering
Software Engineer – 2

Supervisors

Supervisor Name Department Remark
@zeroc0d3-devops DevOps  
@zeroc0d3-engineer Engineering  
@zeroc0d3-iot IoT  
@zeroc0d3-data Data  

HelmChart

Deployments Request Limit
CPI (mi) Mem (mb) CPU (mi) Mem (mb)
         

Performance Test Report

Cycle Virtual User (vus) Duration (seconds) Date / Time Service Component Before Inprogress After Jenkins
Link		 Monitoring Link Remark (Logs)
Start End CPU (mi) Mem (mb) CPU (mi) Mem (mb) CPU (mi) Mem (mb) Performance Test Process After Performance Test Process
1         EKS <deployment-name>                    
RDS <rds-name>/<db-name>             N/A     N/A

References:

K6 Website:    https://k6.io/
K6 SourceCode: https://github.com/grafana/k6

[RFC] Logging

A. Concepts

Standardization export log path and name

eg:
----
/var/log/[microservice-name]/[microservice-name]-error.log   # error only
/var/log/[microservice-name]/[microservice-name].log         # info, warning & debug

Log using JSON formatted

Severity logs & formatting logs

eg: INFO
---
{
  "datetime": "2020-10-10 20:01:59TZ+0700"
  "severity": "info",
  "message": "yes, this is info"
}

eg: WARNING
---
{
  "datetime": "2020-10-10 20:01:59TZ+0700"
  "severity": "warning",
  "message": "this is warning"
}

eg: ERROR
---
{
  "datetime": "2020-10-10 20:01:59TZ+0700"
  "severity": "error",
  "code": 404
  "message": "not found"
}

eg: DEBUG (optional)
---
{
  "datetime": "2020-10-10 20:01:59TZ+0700"
  "severity": "debug",
  "code": 100
  "message": "describe debug information (criteria by number) "
}

Logrotation & compression

# /etc/logrotate.d/[microservice-name]
---
/var/log/[microservice-name]/[microservice-name].log {
        rotate 12
        weekly
        missingok
        notifempty
        compress
        delaycompress
        size 50M
        notifempty
        sharedscripts
        postrotate
           /usr/bin/killall -HUP [microservice-name]
        endscript
}

/var/log/[microservice-name]/[microservice-name]-error.log {
        rotate 12
        weekly
        missingok
        notifempty
        compress
        delaycompress
        size 50M
        notifempty
        sharedscripts
        postrotate
           /usr/bin/killall -HUP [microservice-name]
        endscript
}

Log4j (JAVA)

# log4j.properties
---
log4j.rootLogger=INFO, fileLogger
log4j.appender.fileLogger=org.apache.log4j.RollingFileAppender
log4j.appender.fileLogger.layout=org.apache.log4j.PatternLayout
log4j.appender.fileLogger.layout.ConversionPattern=%d [%t] %-5p (%F:%L) - %m%n
log4j.appender.fileLogger.File=example.log
log4j.appender.fileLogger.MaxFileSize=50MB
log4j.appender.fileLogger.MaxBackupIndex=12

Schedule logging (log exporter)

  • Schedule with cron (crontab)
/etc/cron.d/[microservice-name]
  • Schedule with systemd
/etc/systemd/system/[microservice-name].service
/etc/systemd/system/[microservice-name].timer

B. Tools

  • GO

https://github.com/sirupsen/logrus

  • Python
from datetime import datetime
import logging
import time
import json
       
def main():
    print("--- Staring Log Exporter Agent ---")
    logging.basicConfig(level=logging.INFO, filename="/var/log/[microservice-name]/[microservice-name].log", format="%(message)s")

if __name__ == '__main__':
    main()