Tech Devins

  We have Spring Boot  Rest API deployed in Kubernetes cluster which integrates with MongoDB to fetch the data.  MongoDB is fed with data by a real time Spark & NiFi job.  Our clients complained that for a request what they send they don't have response within 90 seconds. Consider it like an OMS ( Order ManagEment System).  On further analysis, we found that Spark & NiFi processing is happenning within 10 seconds after consuming response data from Kafka. Thus, initally out thought was that it due to delay from upstream to produce data in to Kafka.  Thankfully, our data had create / request  timestamp, and when response was received, and when response was inserted into MongoDB. Subtracting response insert time from request time seemed to be well within 90 seconds. But, still client did timeout on not seeing a response within 90 seconds. This led to confusion on our side.  But, then we realized it was due to Read Preference . We updated this...

  Register a script function like below -  function time(command) { const t1 = new Date(); const result = command(); const t2 = new Date(); print("Execution time: " + (t2 - t1) + "ms"); return result; } Then run query like below -  time(()=>  db.test.aggregate( { $match: { macAddress: { $regex: "^XX:?08:?XX:?1X:?2X:?X5$(?i)", $options: "i" }, requestTimestamp: { $gte: ISODate("2025-11-04T00:00:00Z") } } }, { $sort: { requestTimestamp: -1 } }, { $limit: 100 } ).toArray()); That should print execution time and results -  Execution time: 152ms [{....}, {...}]

   We came across a situation where-in, MongoDB Query was taking more time in Production like 10 seconds and 4.2 seconds but same query performed well in UAT taking under 400 ms. The very first thought that was evident to us that it is because of amount of data which differed in UAT and Production. Then we ran following to see the execution plan -   db.collection.aggregate(<queries>).explain() This gave us Winning and Rejected Plans. Under which, we analyzed that although it was using 'IXSCAN.' But, it was incorrect index- as we had one compound index built on time field and other fields, and there was other index just on time field for TTL purposes. Winning plan picked TTL index rather than compound index. Thus, we dropped TTL index and built TTL index on a different time field.  That got our query performance time from 10 seconds to 726 ms. Also, for other query the performance came down from 8 seconds to 4.3 seconds. Then, we ran following -  ...

  Error requests.exceptions.SSLError: HTTPSConnectionPool  Max retries exceeded with url:  (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: Missing Authority Key Identifier (_ssl.c:1028)'))). Analysis & Solution Recently, we updated from Python 3.11 to 3.13, which resulted in error above. We did verify AKI = SKI in chain of certificates. Also, imported chain of certificates into certifi. Nothing worked for us. Seemingly, it is a bug with Python 3.13. So, we downgraded to Python 3.12 and it started working. Other problems and solution -  '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1006)'))) solution  pip install pip-system-certs [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired  (_ssl.c:1006) solution  1# openssl s_client -showcerts -connect  signin.aws.amazon.com:443  </dev/...