Skip to main content

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

By Dinesh

 

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

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

By Dinesh
  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




Scala Spark building Jar leads java.lang.StackOverflowError

By Dinesh
  Exception -  [Thread-3] ERROR scala_maven.ScalaCompileMojo - error: java.lang.StackOverflowError [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.generic.TraversableForwarder$class.isEmpty(TraversableForwarder.scala:36) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.ListBuffer.isEmpty(ListBuffer.scala:45) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.ListBuffer.toList(ListBuffer.scala:306) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.ListBuffer.result(ListBuffer.scala:300) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.Stack$StackBuilder.result(Stack.scala:31) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.Stack$StackBuilder.result(Stack.scala:27) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.generic.GenericCompanion.apply(GenericCompanion.scala:50) [Thread-3] INFO scala_maven.ScalaCompile...
Post a Comment
Read more




MongoDB Chunk size many times bigger than configure chunksize (128 MB)

By Dinesh
  Shard Shard_0 at Shard_0/xyz.com:27018 { data: '202.04GiB', docs: 117037098, chunks: 5, 'estimated data per chunk': '40.4GiB', 'estimated docs per chunk': 23407419 } --- Shard Shard_1 at Shard_1/abc.com:27018 { data: '201.86GiB', docs: 116913342, chunks: 4, 'estimated data per chunk': '50.46GiB', 'estimated docs per chunk': 29228335 } Per MongoDB-  Starting in 6.0.3, we balance by data size instead of the number of chunks. So the 128MB is now only the size of data we migrate at-a-time. So large data size per chunk is good now, as long as the data size per shard is even for the collection. refer -  https://www.mongodb.com/community/forums/t/chunk-size-many-times-bigger-than-configure-chunksize-128-mb/212616 https://www.mongodb.com/docs/v6.0/release-notes/6.0/#std-label-release-notes-6.0-balancing-policy-changes
Post a Comment
Read more




AWS EMR Spark – Much Larger Executors are Created than Requested

By Dinesh
  Starting EMR 5.32 and EMR 6.2 you can notice that Spark can launch much larger executors that you request in your job settings. For example - We started a Spark Job with  spark.executor.cores  =   4 But, one can see that the executors with 20 cores (instead of 4 as defined by spark.executor.cores) were launched. The reason for allocating larger executors is that there is a AWS specific Spark option spark.yarn.heterogeneousExecutors.enabled (exists in EMR only, does not exist in Open Source Spark) that is set to true by default that combines multiple executor creation requests on the same node into a larger executor container. So as the result you have fewer executor containers than you expected, each of them has more memory and cores that you specified. If you disable this option (--conf "spark.yarn.heterogeneousExecutors.enabled=false"), EMR will create containers with the specified spark.executor.memory and spark.executor.cores settings and will not co...
Post a Comment
Read more




Hive Count Query not working

By Dinesh
Hive with Tez execution engine -  count(*) not working , returning 0 results.  Solution -  set hive.compute.query.using.stats=false Refer -  https://cwiki.apache.org/confluence/display/Hive/Configuration+Properties hive.compute.query.using.stats Default Value:  false Added In: Hive 0.13.0 with  HIVE-5483 When set to true Hive will answer a few queries like min, max, and count(1) purely using statistics stored in the metastore. For basic statistics collection, set the configuration property  hive.stats.autogather   to true. For more advanced statistics collection, run ANALYZE TABLE queries.
Post a Comment
Read more