Skip to main content



HTTP 400 Bad Request - Apache HTTP Client - HTTP POST JSON Message

 

For one of our projects, we faced issue where-in - 

  • We were utilizing Apache HTTP Client 4.5 and doing HTTP Post of JSON Message.
  • This was resulting HTTP Response as 
    • HTTP /1.1 400 Bad Request ...  
On analysis, we found that HTTP Post was failing just for Big JSON Files of size greater then 7 MB. So, initially we thought it to be a server side issue. 

But after further analysis we found that Unix CURL command was able to successfully POST message to API. Thus, we came to know that something was wrong with Scala (JAVA) client code that was using HTTP Client. 

Solution - 

  • We further updated the code and used java.net.{HttpURLConnection, URL} instead of org.apache.http.client.methods.{HttpPost} and it worked fine for us.

But, we were still buzzed with the problem why HTTPClient is not working. We tried using a proxy to capture HTTP Header and Body. So, as to compare difference between various user agents. 

  • And, we identified that Content-Length  for User-Agent : Apache-HttpClient/4.5 (Java/1.8.0._333) was little less then curl/7.83.1 and Java/1.8.0_333, which for us was 7783713 for prior and 7783726 for later. 
  • On further analysis, we found that code was using org.apache.http.entity.StringEntity - 
    • which by default uses ISO 8859-1 encoding (text/plain)
    • instead of UTF-8 (application/json)
UTF-8 is a multibyte encoding that represent any Unicode character. ISO 8859-1 is a single byte encoding that represents only first 256 Unicode characters.

  • We update our code to specify correct ContentType and it worked fine with HTTPClient as well.
    • new StringEntity(string, ContentType.APPLICATION_JSON)


Comments

Post a Comment

Popular posts

Python [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: Missing Authority Key Identifier

  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/...
Post a Comment
Read more




Spring MongoDB Rest API not returning response in 90 seconds which is leading to client timeout

  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...
Post a Comment
Read more




MongoDB Regex Query taking more time in Production but same query perform well in UAT

   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 -  ...
Post a Comment
Read more




What is Leadership

 
Post a Comment
Read more




Spark MongoDB Connector Not leading to correct count or data while reading

  We are using Scala 2.11 , Spark 2.4 and Spark MongoDB Connector 2.4.4 Use Case 1 - We wanted to read a Shareded Mongo Collection and copy its data to another Mongo Collection. We noticed that after Spark Job successful completion. Output MongoDB did not had many records. Use Case 2 -  We read a MongoDB collection and doing count on dataframe lead to different count on each execution. Analysis,  We realized that MongoDB Spark Connector is missing data on bulk read as a dataframe. We tried various partitioner, listed on page -  https://www.mongodb.com/docs/spark-connector/v2.4/configuration/  But, none of them worked for us. Finally, we tried  MongoShardedPartitioner  this lead to constant count on each execution. But, it was greater than the actual count of records on the collection. This seems to be limitation with MongoDB Spark Connector. But,  MongoShardedPartitioner  seemed closest possible solution to this kind of situation. But, it per...
Post a Comment
Read more