emr

https://docs.aws.amazon.com/zh_cn/emr/latest/ManagementGuide/emr-what-is-emr.html https://docs.aws.amazon.com/zh_cn/emr/latest/ReleaseGuide/emr-release-3x.html https://blog.csdn.net/Agony__X/article/details/78707927 原文：https://www.cnblogs.com/575dsj/p/9235744.html

查看电子病历系统演示 医院医疗信息管理系统，EMR电子病历系统，功能模块如下所示： 1.住院医生站 2.住院护士站 3.病案浏览工作站 4.质量控制工作站 5.系统维护工作站 本店出售系统全套源码，包含接口平台和报表平台源码。软件开发语言是.net c#，开发工具vs2010，数据库oracle11g。包含书写控件源码（C++语言）。 功能模块截图如下： 文章来源: 新品成熟EMR源码电子病历系统软件NET网络版CS可用带数据库全文档

I am trying to create a simple sql query on S3 events using Spark. I am loading ~30GB of JSON files as following: val d2 = spark.read.json("s3n://myData/2017/02/01/1234"); d2.persist(org.apache.spark.storage.StorageLevel.MEMORY_AND_DISK); d2.registerTempTable("d2"); Then I am trying to write to file the result of my query: val users_count = sql("select count(distinct data.user_id) from d2"); users_count.write.format("com.databricks.spark.csv").option("header", "true").save("s3n://myfolder/UsersCount.csv"); But Spark is throwing the following exception: java.lang.IllegalArgumentException: Size

I'm launching my spark-based hiveserver2 on Amazon EMR, which has an extra classpath dependency. Due to this bug in Amazon EMR: https://petz2000.wordpress.com/2015/08/18/get-blas-working-with-spark-on-amazon-emr/ My classpath cannot be submitted through "--driver-class-path" option So I'm bounded to modify /etc/spark/conf/spark-env.conf to add the extra classpath: # Add Hadoop libraries to Spark classpath SPARK_CLASSPATH="${SPARK_CLASSPATH}:${HADOOP_HOME}/*:${HADOOP_HOME}/../hadoop-hdfs/*:${HADOOP_HOME}/../hadoop-mapreduce/*:${HADOOP_HOME}/../hadoop-yarn/*:/home/hadoop/git/datapassport/*"

Context: 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. Error is while parsing the timestamp Issue Stack trace is: 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

Once in 2 hours, spark job is running to convert some tgz files to parquet. The job appends the new data into an existing parquet in s3: df.write.mode("append").partitionBy("id","day").parquet("s3://myBucket/foo.parquet") In spark-submit output I can see significant time is being spent on reading old parquet files, for example: 16/11/27 14:06:15 INFO S3NativeFileSystem: Opening 's3://myBucket/foo.parquet/id=123/day=2016-11-26/part-r-00003-b20752e9-5d70-43f5-b8b4-50b5b4d0c7da.snappy.parquet' for reading 16/11/27 14:06:15 INFO S3NativeFileSystem: Stream for key 'foo.parquet/id=123/day=2016-11-26

There are similar questions on Stack overflow but none of them answer the question. The problem arises when as per the following link http://grepalex.com/2013/02/25/hadoop-libjars/ ,we need to use export HADOOP_CLASSPATH=/path/jar1:/path/jar2 to get it to work. So how can I execute export HADOOP_CLASSPATH=/path/jar1:/path/jar2 for -libjars option to work. I have implemented a Tool Runner . It works perfectly on hadoop and HDFS. I tried executing this while using custom jar but it gives Exception java.lang.NoClassDefFoundError: org/json/simple/parser/JSONParser : This is what I ran in EMR where