I have a mapreduce program that first scans an HBase table.
I want some reducer output to go to hdfs and some reducer output to be written to an hbase table. Can a reducer be configured to output to two different locations/formats like this?
A reducer can be configured to use multiple files to output using the MulitpleOutputsclass. The documentation at the top of that class provides a clear example for writing to multiple files. However, since there is no built in Outputformat for writing to HBase you might consider writing the 2nd stream to specific place on HDFS and then using another job to insert it into HBase.
If you don't want to write too much code, just open a Table in your mapper's or reducer's setup method and do a put statement into your hbase table. On the other hand, write your job such that the output file is an hdfs file. This way you get to both write to hbase and hdfs.
To be more elaborate, when you do a context.write(), you would write to the hdfs file, and on the other hand, the table.put can happen when you do a put.
Also, don't forget to close the table and anything else in your cleanup() method. The only backdrop is, if there are let's say 1000 mappers your table connection would be opened a 1000 times, but at any given point, only the max number of your mappers really run, so that would probably be 50, depending on your setup. Works for me at least!
i think multiple output can do the job..
chk tis out
http://hadoop.apache.org/mapreduce/docs/r0.21.0/api/org/apache/hadoop/mapreduce/lib/output/MultipleOutputs.html
Related
I recently did analysis on a static log file with Spark SQL (find out stuff like the ip addresses which appear more than ten times). The problem was from this site. But I used my own implementation for it. I read the log into an RDD, turned that RDD to a DataFrame (with the help of a POJO) and used DataFrame operations.
Now I'm supposed to do a similar analysis using Spark Streaming for a streaming log file for a window of 30 mins as well as aggregated results for a day. The solution can again be found here but I want to do it another way. So what I've done is this
Use Flume to write data from the log file to an HDFS directory
Use JavaDStream to read the .txt files from HDFS
Then I can't figure out how to proceed. Here's the code I use
Long slide = 10000L; //new batch every 10 seconds
Long window = 1800000L; //30 mins
SparkConf conf = new SparkConf().setAppName("StreamLogAnalyzer");
JavaStreamingContext streamingContext = new JavaStreamingContext(conf, new Duration(slide));
JavaDStream<String> dStream = streamingContext.textFileStream(hdfsPath).window(new Duration(window), new Duration(slide));
Now I can't seem to decide if I should turn each batch to a DataFrame and do what I previously did with the static log file. Or is this way time consuming and overkill.
I'm an absolute noob to Streaming as well as Flume. Could someone please guide me with this?
Using DataFrame (and Dataset) in Spark is most promoted way in latest versions of Spark, so it's a right choice to go with. I think that some obscurity appears because of non-explicit nature of stream, when you move files into HDFS rather than read from any event log.
Main point here is to choose correct batch time size (or slide size as in your snippet), so application would process data it loaded under that time slot and there would not be batch queue.
I have multiple small parquet files generated as output of hive ql job, i would like to merge the output files to single parquet file?
what is the best way to do it using some hdfs or linux commands?
we used to merge the text files using cat command, but will this work for parquet as well?
Can we do it using HiveQL itself when writing output files like how we do it using repartition or coalesc method in spark?
According to this https://issues.apache.org/jira/browse/PARQUET-460
Now you can download the source code and compile parquet-tools which is built in merge command.
java -jar ./target/parquet-tools-1.8.2-SNAPSHOT.jar merge /input_directory/
/output_idr/file_name
Or using a tool like https://github.com/stripe/herringbone
You can also do it using HiveQL itself, if your execution engine is mapreduce.
You can set a flag for your query, which causes hive to merge small files at the end of your job:
SET hive.merge.mapredfiles=true;
or
SET hive.merge.mapfiles=true;
if your job is a map-only job.
This will cause the hive job to automatically merge many small parquet files into fewer big files. You can control the number of output files with by adjusting hive.merge.size.per.task setting. If you want to have just one file, make sure you set it to a value which is always larger than the size of your output. Also, make sure to adjust hive.merge.smallfiles.avgsize accordingly. Set it to a very low value if you want to make sure that hive always merges files. You can read more about this settings in hive documentation.
Using duckdb :
import duckdb
duckdb.execute("""
COPY (SELECT * FROM '*.parquet') TO 'merge.parquet' (FORMAT 'parquet');
""")
Loading many small files (>200000, 4kbyte) from a S3 Bucket into HDFS via Hive or Pig on AWS EMR is extremely slow. It seems that only one mapper is used to get the data, though I cannot exactly figure out where the bottleneck is.
Pig Code Sample
data = load 's3://data-bucket/' USING PigStorage(',') AS (line:chararray)
Hive Code Sample
CREATE EXTERNAL TABLE data (value STRING) LOCATION 's3://data-bucket/';
Are there any known settings that speed up the process or increase the number of mappers used to fetch the data?
I tried the following without any noticeable effects:
Increase #Task Nodes
set hive.optimize.s3.query=true
manually set #mappers
Increase instance type from medium up to xlarge
I know that s3distcp would speed up the process, but I could only get better performance by doing a lot of tweaking including setting #workerThreads and would prefer changing parameters directly in my PIG/Hive scripts.
You can either :
use distcp to merge the file before your job starts : http://snowplowanalytics.com/blog/2013/05/30/dealing-with-hadoops-small-files-problem/
have a pig script that will do it for you, once.
If you want to do it through PIG, you need to know how many mappers are spawned. You can play with the following parameters :
// to set mapper = nb block size. Set to true for one per file.
SET pig.noSplitCombination false;
// set size to have SUM(size) / X = wanted number of mappers
SET pig.maxCombinedSplitSize 250000000;
Please provide metrics for thoses cases
I want to understand MapReduce of Hbase from application point of view, Need some real use cases of it to better understand the efficient use case of writing these jobs.
If there is any link to document or examples that explains the real use cases, Please share.
I can give some example based on my use cases. If you already store your data in hbase, you can write a java program, which scans a table and do something, then write the output to hbase or somewhere else. OR you can use mapreduce to do the same. The difference is, mapreduce will run where the data is and network traffic is used only for result data. We have hourly jobs to calculate sum and average of kpis and input data is huge but output data is tiny for this task. If i did not use mapreduce, i need to move one hour of data over network which is 18gb. But mapreduce output is only 1mb and i can write it to hbase or file or somewhere else.
Also mapreduce gives you parallel task execution ability, which you can have in java but why :)
Keep in mind that YARN creates map tasks according to your hbase table's split count. So if you need more map task, split your table.
If you already store your data in hadoop hdfs, you are lucky, a mapreduce reading from hdfs is much faster than reading from hbase. Also you can still write mapreduce output to hbase, if you want.
Please look into the usecases given
1. here.
2. And a small reference here - 30.Joins
3. May be an end to end example here
In the end, it all depends on your understanding of each concept Map reduce, Hbase and use it as per your need in your project. The same task can be done with or without map reduce. Happy coding
Is there a way to instruct Hive to split data into multiple output files? Or maybe cap the size of the output files.
I'm planning to use Redshift, which recommends splitting data into multiple files to allow parallel loading http://docs.aws.amazon.com/redshift/latest/dg/t_splitting-data-files.html
We preprocess all out data in hive, and I'm wondering if there's a way to create, say 10 1GB files which might make copying to redshift faster.
I was looking at https://cwiki.apache.org/Hive/adminmanual-configuration.html and https://cwiki.apache.org/confluence/display/Hive/Configuration+Properties but I can't find anything
There are a couple of ways you could go about splitting Hive output. The first and easiest way is to set the number of reducers. Since each reduces writes to its own output file, the number of reducers you specify will correspond to the number of output files written. Note that some Hive queries will not result in the number of reducers you specify (for example, SELECT COUNT(*) FROM some_table always results in one reducer). To specify the number of reducers run this before your query:
set mapred.reduce.tasks=10
Another way you could split into multiple output files would be to have Hive insert the results of your query into a partitioned table. This would result in at least one file per partition. For this to make sense you must have some reasonable column to partition on. For example, you wouldn't want to partition on a unique id column or you would have one file for each record. This approach will guarantee at least output file per partition, and at most numPartitions * numReducers. Here's an example (don't worry too much about hive.exec.dynamic.partition.mode, it needs to be set for this query to work).
hive.exec.dynamic.partition.mode=nonstrict
CREATE TABLE table_to_export_to_redshift (
id INT,
value INT
)
PARTITIONED BY (country STRING)
INSERT OVERWRITE TABLE table_to_export_to_redshift
PARTITION (country)
SELECT id, value, country
FROM some_table
To get more fine grained control, you can write your own reduce script to pass to hive and have that reduce script write to multiple files. Once you are writing your own reducer, you can do pretty much whatever you want.
Finally, you can forgo trying to maneuver Hive into outputting your desired number of files and just break them apart yourself once Hive is done. By default, Hive stores its tables uncompressed and in plain text in it's warehouse directory (ex, /apps/hive/warehouse/table_to_export_to_redshift). You can use Hadoop shell commands, a MapReduce job, Pig, or pull them into Linux and break them apart however you like.
I don't have any experience with Redshift, so some of my suggestions may not be appropriate for consumption by Redshift for whatever reason.
A couple of notes: Splitting files into more, smaller files is generally bad for Hadoop. You might get a speed increase for Redshift, but if the files are consumed by other parts of the Hadoop ecosystem (MapReduce, Hive, Pig, etc) you might see a performance loss if the files are too small (though 1GB would be fine). Also make sure that the extra processing/developer time is worth the time savings you get for paralleling your Redshift data load.