Hadoop-MapReduce | 基于MapReduce的应用案例 ear_of_rice

ABFS is a "real" file system, so the S3A zero rename committers are not needed. Indeed, they won't work. And the client is entirely open source - look into the hadoop-azure module.

the ADLS gen2 store does have scale problems, but unless you are trying to commit 10,000 files, or clean up massively deep directory trees -you won't hit these. If you do get error messages about Elliott to rename individual files and you are doing Jobs of that scale (a) talk to Microsoft about increasing your allocated capacity and (b) pick this up https://github.com/apache/hadoop/pull/2971

This isn't it. I would guess that actually you have multiple jobs writing to the same output path, and one is cleaning up while the other is setting up. In particular -they both seem to have a job ID of "0". Because of the same job ID is being used, what only as task set up and task cleanup getting mixed up, it is possible that when an job one commits it includes the output from job 2 from all task attempts which have successfully been committed.

I believe that this has been a known problem with spark standalone deployments, though I can't find a relevant JIRA. SPARK-24552 is close, but should have been fixed in your version. SPARK-33402 Jobs launched in same second have duplicate MapReduce JobIDs. That is about job IDs just coming from the system current time, not 0. But: you can try upgrading your spark version to see if it goes away.

My suggestions

1. make sure your jobs are not writing to the same table simultaneously. Things will get in a mess.
2. grab the most recent version spark you are happy with

Solution to your first problem - --hcatalog-database mydb --hcatalog-table airquality and remove --export dir parameter.

Sqoop export cannot replace data. Pls issue a sqoop eval statement before loading main table to truncate it.

Remove the $ at the beginning. That's what $: command not found means. Easy to miss when copy pasting code

I was able to sort the above issue and few other ClassNotFoundException by copying the following jar files from extlib to lib. Both folder are in oozie_install/oozie-5.2.1.

• libext/hadoop-common-2.6.0.jar
• libext/commons-configuration-1.6.jar
• libext/hadoop-mapreduce-client-core-2.6.0.jar
• libext/hadoop-hdfs-2.6.0.jar

While I am not sure how many more jars need to be moved from libext to lib while I try to run an example workflow/job in oozie. This fix brought up Oozie web site at http://localhost:11000/oozie/

I am also not sure why Oozie doesn't load the libraries in the libext/ folder.

For solving this issue. Read Documantation

In this case is think you should use `

`Integer.parseInt(input);

I couldn't find an explanation for this problem, but I solved it by turning off compression of mapper output:

Missing org.apache.parquet.hadoop.ColumnChunkPageWriteStore(org.apache.parquet.hadoop.CodecFactory$BytesCompressor,org.apache.parquet.schema.MessageType,org.apache.parquet.bytes.ByteBufferAllocator,int) [java.lang.NoSuchMethodException: org.apache.parquet.hadoop.ColumnChunkPageWriteStore.(org.apache.parquet.hadoop.CodecFactory$BytesCompressor, org.apache.parquet.schema.MessageType, org.apache.parquet.bytes.ByteBufferAllocator, int)]

Means you are using the Constructor of ColumnChunkPageWriteStore, which takes in 4 parameters, of types (org.apache.parquet.hadoop.CodecFactory$BytesCompressor, org.apache.parquet.schema.MessageType, org.apache.parquet.bytes.ByteBufferAllocator, int)

It cant find the constructor you are using. That why NoSuchMethodError

According to https://jar-download.com/artifacts/org.apache.parquet/parquet-hadoop/1.8.1/source-code/org/apache/parquet/hadoop/ColumnChunkPageWriteStore.java , you need 1.8.1 of parquet-hadoop

Change your mvn import to an older version. I looked at 1.8.1 source code and it has the proper constructor you need.

This is a classic case example for the use of regular expressions in order to filter out the HTML entities and symbols of punctuation from the text inside the input files.

In order to do that, we need to create the two regular expressions that are going to be used to match the HTML entities and punctuation respectively and remove them from the text to finally set as key-value pairs the remaining valid words.

Starting with the HTML entities like  , <, and >, we can figure out that those tokens always start with the & character and end with the ; character with a number of alphabetical characters in-between. So based on the RegEx syntax (which you can study on your own, it's really valuable if you haven't yet), the following expression matches all these tokens: