title: Parquet Data timestamp columns INT96 not yet implemented in Druid Overlord Hadoop task
body: <p><strong>Context:</strong></p>
  
  <p>I am able to submit a MapReduce job from druid overlord to an EMR. My Data source is in S3 in Parquet format. I have a timestamp column (INT96) in parquet data which is not supported in Avroschema. </p>
  
  <p>Error is while parsing the timestamp </p>
  
  <p><strong>Issue Stack trace is:</strong> </p>
  
  <pre><code>Error: java.lang.IllegalArgumentException: INT96 not yet implemented.
  at org.apache.parquet.avro.AvroSchemaConverter$1.convertINT96(AvroSchemaConverter.java:279)
  at org.apache.parquet.avro.AvroSchemaConverter$1.convertINT96(AvroSchemaConverter.java:264)
  at org.apache.parquet.schema.PrimitiveType$PrimitiveTypeName$7.convert(PrimitiveType.java:223)
  </code></pre>
  
  <p><strong>Environment:</strong></p>
  
  <pre><code>Druid version: 0.11
  EMR version : emr-5.11.0
  Hadoop version: Amazon 2.7.3
  </code></pre>
  
  <p><strong>Druid input json</strong></p>
  
  <pre><code>{
    "type": "index_hadoop",
    "spec": {
      "ioConfig": {
        "type": "hadoop",
        "inputSpec": {
          "type": "static",
          "inputFormat": "io.druid.data.input.parquet.DruidParquetInputFormat",
          "paths": "s3://s3_path"
        }
      },
      "dataSchema": {
        "dataSource": "parquet_test1",
        "granularitySpec": {
          "type": "uniform",
          "segmentGranularity": "DAY",
          "queryGranularity": "ALL",
          "intervals": ["2017-08-01T00:00:00/2017-08-02T00:00:00"]
        },
        "parser": {
          "type": "parquet",
          "parseSpec": {
            "format": "timeAndDims",
            "timestampSpec": {
              "column": "t",
              "format": "yyyy-MM-dd HH:mm:ss:SSS zzz"            
            },
            "dimensionsSpec": {
              "dimensions": [
                "dim1","dim2","dim3"
              ],
              "dimensionExclusions": [],
              "spatialDimensions": []
            }
          }
        },
        "metricsSpec": [{
          "type": "count",
          "name": "count"
        },{
            "type" : "count",
            "name" : "pid",
            "fieldName" : "pid"
          }]
      },
      "tuningConfig": {
        "type": "hadoop",
        "partitionsSpec": {
          "targetPartitionSize": 5000000
        },
        "jobProperties" : {
          "mapreduce.job.user.classpath.first": "true",
          "fs.s3.awsAccessKeyId" : "KEYID",
          "fs.s3.awsSecretAccessKey" : "AccessKey",
          "fs.s3.impl" : "org.apache.hadoop.fs.s3native.NativeS3FileSystem",
          "fs.s3n.awsAccessKeyId" : "KEYID",
          "fs.s3n.awsSecretAccessKey" : "AccessKey",
          "fs.s3n.impl" : "org.apache.hadoop.fs.s3native.NativeS3FileSystem",
          "io.compression.codecs" : "org.apache.hadoop.io.compress.GzipCodec,org.apache.hadoop.io.compress.DefaultCodec,org.apache.hadoop.io.compress.BZip2Codec,org.apache.hadoop.io.compress.SnappyCodec"
        },
        "leaveIntermediate": true
      }
    }, "hadoopDependencyCoordinates": ["org.apache.hadoop:hadoop-client:2.7.3", "org.apache.hadoop:hadoop-aws:2.7.3", "com.hadoop.gplcompression:hadoop-lzo:0.4.20"]
  }
  </code></pre>
  
  <p><strong>Possible solution</strong></p>
  
  <pre><code> 1. Save the data in parquet efficiently instead of transforming in Avro to remove the dependencies.
  
   2. Fixing AvroSchema to support INT96 timestamp format of Parquet.
  </code></pre>