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

Spring MongoDB Rename field with derived Value of another field

Input Collection -  [ { 'k' : 'Troubleshooting' , 'hour' : '2024-10-10T16' , 'v' : [ 'WebPage, Login' ] }, { 'k' : 'TroubleshootingMe' , 'hour' : '2024-10-07T01' , 'v' : [ 'Accounts, etc' ] }  ] Expected Output -  [ { 'hour' : '2024-10-10T16' , 'Troubleshooting' : [ 'WebPage, Login' ] }, { 'hour' : '2024-10-07T01' , 'TroubleshootingMe' : [ 'Accounts, etc' ] }  ]   Above Can be achieved by  $replaceRoot / $replaceWith as follows - { $replaceWith : { $mergeObjects : [ { hour : "$hour" }, { "$arrayToObject" : [ [ { k : "$k" , v : "$v" } ] ] } ] } } or { $replaceRoo...
Post a Comment
Read more




What is Leadership

 
Post a Comment
Read more




Spark Error - missing part 0 of the schema, 2 parts are expected

 Exception -  Caused by: org.apache.spark.sql.AnalysisException : Could not read schema from the hive metastore because it is corrupted. (missing part 0 of the schema, 2 parts are expected).; Analysis -  ·          Check for table definition. In TBLProperties, you might find something like this – > spark.sql.sources.schema.numPartCols > 'spark.sql.sources.schema.numParts' 'spark.sql.sources.schema.part.0' > 'spark.sql.sources.schema.part.1' 'spark.sql.sources.schema.part.2' > 'spark.sql.sources.schema.partCol.0' > 'spark.sql.sources.schema.partCol.1' That’s what error seems to say that part1 is defined but part0 is missing.  Solution -  Drop & re-create table. If Table was partitioned  then all partitions  would have been removed. So do either of below -  ·          Msck repair table <db_name>.<table_name> ·    ...
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




Read from a hive table and write back to it using spark sql

In context to Spark 2.2 - if we read from an hive table and write to same, we get following exception- scala > dy . write . mode ( "overwrite" ). insertInto ( "incremental.test2" ) org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; 1. This error means that our process is reading from same table and writing to same table. 2. Normally, this should work as process writes to directory .hiveStaging... 3. This error occurs in case of saveAsTable method, as it overwrites entire table instead of individual partitions. 4. This error should not occur with insertInto method, as it overwrites partitions not the table. 5. A reason why this happening is because Hive table has following Spark TBLProperties in its definition. This problem will solve for insertInto met...
Post a Comment
Read more