How would you identify the root cause of a sudden drop in application performance?

To identify the root cause of a sudden drop in application performance, analyze recent changes in code, configurations, or deployments, and monitor system metrics such as CPU, memory usage, and database performance for anomalies.

The Story Behind Rajkumari Dress Up Like a Princess LinkedIn Post - The Story Behind Rajkumari Dress Up Like a Princess I'm thrilled to share how "Rajkumari Dress Up Like a Princess" began. I used to be a software engineer, but with five sisters and a mother running a boutique, fashion was always in my blood. The turning point was when my sister Tanishka made a beautiful dress for me. The compliments it received sparked our idea to start a fashion brand. Tanishka handled designing, while I managed finances and operations. Starting on Instagram, Amazon, and Flipkart, we eventually launched our own website. Today, Rajkumari has crossed a turnover of 1 crore! Thanks to our amazing team and customers, we've come a long way since 2017.

User Reports and Symptoms: Start by gathering information from user reports or monitoring alerts. Understand the nature and scope of the performance issue, such as specific features or times when the performance drop occurs. Verify if the issue is reproducible and if it affects all users or a subset. Recent Changes: Review recent changes in the application, including code updates, configuration changes, or deployments. A performance drop often correlates with recent modifications. Roll back recent changes temporarily to see if performance improves, which can indicate whether the changes are related to the issue.

To identify the root cause of a sudden drop in application performance, start by examining recent changes in the system, such as deployments, configuration updates, or infrastructure modifications. Use performance monitoring tools to analyze metrics like CPU, memory usage, and response times. Check logs for error messages or unusual patterns, and review database performance for slow queries or locks. Network issues, such as increased latency or packet loss, should also be considered. Collaborate with team members to gather insights and use a systematic approach to isolate and test potential causes until the root issue is identified and resolved.

For Performance drop checks:- 1. If that issue is on prod env first then will take the memory dump . 2. post that import & analyze dump in tool like IntelliJ IDEA or Visual Studio. 3.now check the areas(cpu usage, memory usage, threads , garbage collection , exceptions) 4. Now take a another dump when performance is normal and identifying the difference between areas along with calls stacks difference. 5.Also checks the applications logs

You should look for an early anomaly which may caused chains of effects. Check CPU usage RAM Usage and IO-ps of disk in Host Level. Check cache and DB metrics both service level and node level. Check Tracing mechanism if you have one if everything is in low performance you have infrastructure issues like network or CPU throttle. Check internal and external Network performance. Use dummy code alongside actual code to check for network bottlenecks. Check slow queries on you databases maybe Marketing running query on production! (yeah it happened :D)

As a first step, identify if the performance drop was for once or it is still ongoing. Are there any application errors logged e.g. Time out error? Review if there are any recent code changes to the application? Review if it is due to any resource bottleneck? For that, we can review System metrics like CPU utilization, memory usage, if there is an increase in the throughput? APM(Application Performance Monitoring) tools can provide great insights for this. Based on initial findings, narrow down the potential causes and reproduce the issue in non-production environment if possible. And, then take corrective measures to resolve it.

Key Performance Indicators (KPIs): Examine KPIs such as response times, throughput, error rates, and resource utilization (CPU, memory, disk I/O). Use monitoring tools like New Relic, Datadog, or Prometheus. Identify any anomalies or spikes in these metrics that coincide with the reported performance drop. Historical Data: Compare current metrics with historical data to identify patterns or deviations. This helps determine if the issue is a one-time event or part of a recurring trend.

Having defined Metrics for Monitoring Helps, If the application has a dependency on different components and once we have identified the component causing the issue, we can look for CPU Utilization and memory Consumption and compare based on historical data, (Various tools are available to perform run-time metric analysis like new relic, Splunk etc

by examining these metrics over time, you can identify trends and patterns that may reveal underlying issues. For instance, a gradual increase in memory consumption might indicate a memory leak, while fluctuating response times could point to intermittent network issues or database contention. Utilize tools such as New Relic, Datadog, or Prometheus to gather and visualize these metrics, making it easier to detect anomalies and diagnose problems.

To identify the root cause of a sudden drop in application performance, systematically analyze logs, recent changes, system and application metrics, network conditions and, and use performance profiling tools to pinpoint the bottleneck. Sometimes we need to assess API performance, as increased latency or high error rates from the server can occur due to high load on their end. and this directly impact our mobile application +++ Bonus Tip Alerting is very important to catch sudden anomalies in any application to protect the suddun business loss.

Dive right into the logs if application performance is inconsistent or performance changes as its the starting point of all troubleshooting to identify underlying issues.

Application Logs: Analyze application logs for errors, warnings, or unusual activity. Look for logs that correspond with the time frame of the performance issue. Use centralized logging solutions like ELK Stack (Elasticsearch, Logstash, Kibana) or Splunk to aggregate and search logs efficiently. System and Network Logs: Check system and network logs for hardware failures, network issues, or other infrastructure problems that might impact performance. Investigate any anomalies or repeated error messages that could indicate underlying issues.

Using log analysis tools like ELK Stack, Splunk, or Graylog can make this process more efficient. These tools allow you to search, filter, and visualize log data, helping you identify correlations between different log entries, spot recurring errors, and track the exact sequence of events leading to performance issues.

- Review application logs for errors or warnings. - Look for unusual events that may be related to performance degradation. - Analyze system and associated service logs to detect communication issues or internal errors.

In Python, 'cProfile' is an inbuilt deterministic profiler that is has minimal overhead and also produces a detailed and verbose output for the functions in scope. Example: import cProfile def fib(n): return n if n < 2 else fib(n - 2) + fib(n - 1) cProfile.run("fib(27)") Will produce the following output: 635624 function calls (4 primitive calls) in 0.215 seconds ncalls cumtime percall function 635621/1 0.215 0.000 <stdin>:1(fib) ... Running fibonacci without memoization for a small number like 27, leads to 63561 recursive calls, taking a total time of 215 milliseconds. This significant amount of time in computational terms and highlights the importance of using profilers to address performance bottlenecks.

Code Profiling: Use profiling tools like VisualVM, JProfiler, or YourKit to analyze the application’s runtime behavior. Identify bottlenecks, such as inefficient algorithms, excessive resource usage, or memory leaks. Focus on functions or methods that consume a significant amount of CPU time or memory. Database Performance: Profile database queries using tools like MySQL’s EXPLAIN, PostgreSQL’s EXPLAIN ANALYZE, or SQL Server’s Query Analyzer. Look for slow queries, index issues, or locking problems. Optimize problematic queries and consider adding indexes or adjusting database configurations.

take advantage of various profiling tools that cater to different programming languages and environments. For instance, tools like PyCharm Profiler for Python, VisualVM for Java, and Perf for Linux systems can provide detailed insights into your application's performance. By using these tools, you can drill down into specific parts of your code to see where the bottlenecks are occurring.

- Use profiling tools to identify bottlenecks in the code. - Identify functions or SQL queries that consume significant resources. - Evaluate the performance of database queries and I/O operations.

Load Testing: Perform load testing using tools like Apache JMeter, Gatling, or LoadRunner to simulate high traffic and assess how the application behaves under stress. Identify performance degradation points and determine if the application can handle expected load levels. Stress Testing Scenarios: Create stress testing scenarios that mimic real-world usage patterns, including peak loads, sustained usage, and sudden traffic spikes. Analyze the results to pinpoint where the application fails to meet performance expectations.

tools like Apache JMeter, LoadRunner, or Gatling can help you conduct comprehensive stress tests. These tools allow you to create detailed test plans that simulate various user interactions and load conditions. By doing this, you can see how different parts of your application respond under pressure.

- Conduct stress tests to simulate heavy loads and observe the application's behavior under pressure. - Measure response times and system stability during the tests. - Identify if performance degradation occurs only under specific load conditions.

Resource Utilization: Evaluate the utilization of resources such as CPU, memory, disk, and network bandwidth. Ensure that the infrastructure has sufficient capacity to handle the application’s demands. Investigate any resource contention issues that might be causing performance drops. Cloud and Network Services: Check the status and performance of cloud services (e.g., AWS, Azure) and network components (e.g., load balancers, firewalls). Ensure that these services are operating optimally and are not introducing latency. Review configurations and service limits to ensure they align with the application’s needs.

I think infrastructure evaluation should be part of the initial checks, or right after it. It can be frustrating to dive into all of the suggested metrics just to find out there is an infrastructure issue.

- Review the status and configuration of servers and services supporting the application. - Verify network capacity and connectivity between system components. - Evaluate if there are insufficient resources, such as CPU, memory, or storage, that may be affecting performance.

- We can start with performance monitoring tools - Then we can also check for CPU, Memory and disk IO utilization - We can also check for slow queries using database monitoring tools and check for long-running queries, inefficient joins, or missing indexes.

Always design your app with BIG in your mind, no matter how small a project seems. Keep your requests , and hardware resource use, database queries to a minimum , and plan a strategy for how future updates will work and everything will be fine