We have a Hadoop cluster with over 100TB data in HDFS. I want to delete data older than 13 weeks in certain Hive tables.
Are there any tools or way I can achieve this?
Thank you
To delete data older then a certain time frame, you have a few options.
First, if the Hive table is partitioned by date, you could simply DROP the partitions within Hive and remove their underlying directories.
Second option would be to run an INSERT to a new table, filtering out the old data using a datestamp (if available). This is likely not a good option since you have 100TB of data.
A third option would be to recursively list the data directories for your Hive tables. hadoop fs -lsr /path/to/hive/table. This will output a list of the files and their creation dates. You can take this output, extract the date and compare against the time frame you want to keep. If the file is older then you want to keep, run a hadoop fs -rm <file> on it.
A fourth option would be to grab a copy of the FSImage: curl --silent "http://<active namenode>:50070/getimage?getimage=1&txid=latest" -o hdfs.image Next turn it into a text file. hadoop oiv -i hdfs.image -o hdfs.txt. The text file will contain a text representation of HDFS, the same as what hadoop fs -ls ... would return.
Related
In a cluster of hdfs, i receive multiple files on a daily basis which can be of 3 types :
1) product_info_timestamp
2) user_info_timestamp
3) user_activity_timestamp
The number of files received can be of any number but they will belong to one of these 3 categories only.
I want to merge all the files(after checking whether they are less than 100mb) belonging to one category into a single file.
for eg: 3 files named product_info_* should be merged into one file named product_info.
How do i achieve this?
You can use getmerge toachieve this, but the result will be stored in your local node (edge node), so you need to be sure you have enough space there.
hadoop fs -getmerge /hdfs_path/product_info_* /local_path/product_inf
You can move them back to hdfs with put
hadoop fs -put /local_path/product_inf /hdfs_path
You can use hadoop archive (.har file) or sequence file. It is very simple to use - just google "hadoop archive" or "sequence file".
Another set of commands along the similar lines as suggested by #SCouto
hdfs dfs -cat /hdfs_path/product_info_* > /local_path/product_info_combined.txt
hdfs dfs -put /local_path/product_info_combined.txt /hdfs_path/
I have the multiple text files.
The total size of them exceeds the largest disk size available to me (~1.5TB)
A spark program reads a single input text file from HDFS. So I need to combine those files into one. (I cannot re-write the program code. I am given only the *.jar file for execution)
Does HDFS have such a capability? How can I achieve this?
What I understood from your question is you want to Concatenate multiple files into one. Here is a solution which might not be the most efficient way of doing it but it works. suppose you have two files: file1 and file2 and you want to get a combined file as ConcatenatedFile
.Here is the script for that.
hadoop fs -cat /hadoop/path/to/file/file1.txt /hadoop/path/to/file/file2.txt | hadoop fs -put - /hadoop/path/to/file/Concatenate_file_Folder/ConcatenateFile.txt
Hope this helps.
HDFS by itself does not provide such capabilities. All out-of-the-box features (like hdfs dfs -text * with pipes or FileUtil's copy methods) use your client server to transfer all data.
In my experience we always used our own written MapReduce jobs to merge many small files in HDFS in distributed way.
So you have two solutions:
Write your own simple MapReduce/Spark job to combine text files with
your format.
Find already implemented solution for such kind of
purposes.
About solution #2: there is the simple project FileCrush for combining text or sequence files in HDFS. It might be suitable for you, check it.
Example of usage:
hadoop jar filecrush-2.0-SNAPSHOT.jar crush.Crush -Ddfs.block.size=134217728 \
--input-format=text \
--output-format=text \
--compress=none \
/input/dir /output/dir 20161228161647
I had a problem to run it without these options (especially -Ddfs.block.size and output file date prefix 20161228161647) so make sure you run it properly.
You can do a pig job:
A = LOAD '/path/to/inputFiles' as (SCHEMA);
STORE A into '/path/to/outputFile';
Doing a hdfs cat and then putting it back to hdfs means, all this data is processed in the client node and will degradate your network
So there is one condition with the cluster i'm working on. Nothing can be taken out of cluster to linux box.
Files on which hive table are built are in sequence file format or text format.
I need to change those files to CSV format with out outputting them to linux box and also i can create table from existing table which can be STORED AS CSVfile if possible. (i'm not sure if i can do that).
I have tried lot things..but couldn't do it unless i output it to linux box. Any help is appreciated.
You can create another hive table like this:
CREATE TABLE hivetable_csv ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n' as
select * from hivetable;
Then copy the table contents to a new directory
hadoop fs -cat /user/hive/warehouse/csv_dump/* | hadoop fs -put - /user/username/hivetable.csv
Alternatively, you can also try
hadoop fs -cp
Background:
I have some gzip files in a HDFS directory. These files are named in the format yyyy-mm-dd-000001.gz, yyyy-mm-dd-000002.gz and so on.
Aim:
I want to build a hive script which produces a table with the columns: Column 1 - date (yyyy-mm-dd), Column 2 - total file size.
To be specific, I would like to sum up the sizes of all of the gzip files for a particular date. The sum will be the value in Column 2 and the date in Column 1.
Is this possible? Are there any in-built functions or UDFs that could help me with my use case?
Thanks in advance!
A MapReduce job for this doesn't seem efficient since you don't actually have to load any data. Plus, doing this seems kind of awkward in Hive.
Can you write a bash script or python script or something like that to parse the output of hadoop fs -ls? I'd imagine something like this:
$ hadoop fs -ls mydir/*gz | python datecount.py | hadoop fs -put - counts.txt
I have multiple tables on my Hbase shell that I would like to copy onto my file system. Some tables exceed 100gb. However, I only have 55gb free space left in my local file system. Therefore, I would like to know the size of my hbase tables so that I could export only the small sized tables. Any suggestions are appreciated.
Thanks,
gautham
try
hdfs dfs -du -h /hbase/data/default/ (or /hbase/ depending on hbase version you use)
This will show how much space is used by files of your tables.
Hope that will help.
for 0.98+ try hadoop fs -du -s -h $hbase_root_dir/data/data/$schema_name/ (or /hbase/ for 0.94)
You can find hbase_root_dir from hbase-site.xml file of your cluster.
The above command will provide you summary of disk used by each table.
use du
Usage: hdfs dfs -du [-s] [-h] URI [URI …]
Displays sizes of files and directories contained in the given directory or the length of a file in case its just a file.
Options:
The -s option will result in an aggregate summary of file lengths being displayed, rather than the individual files.
The -h option will format file sizes in a "human-readable" fashion (e.g 64.0m instead of 67108864)
Example:
hdfs dfs -du -h /hbase/data/default
output for me:
1.2 M /hbase/data/default/kylin_metadata
14.0 K /hbase/data/default/kylin_metadata_acl
636 /hbase/data/default/kylin_metadata_user
5.6 K /hbase/data/default/test