I'm trying to read a large gzip file into hive through spark runtime
to convert into SequenceFile format
And, I want to do this efficiently.
As far as I know, Spark supports only one mapper per gzip file same as it does for text files.
Is there a way to change the number of mappers for a gzip file being read? or should I choose another format like parquet?
I'm stuck currently.
The problem is that my log file is json-like data save into txt-format and then was gzip - ed, so for reading I used org.apache.spark.sql.json.
The examples I have seen that show - converting data into SequenceFile have some simple delimiters as csv-format.
I used to execute this query:
create TABLE table_1
USING org.apache.spark.sql.json
OPTIONS (path 'dir_to/file_name.txt.gz');
But now I have to rewrite it in something like that:
CREATE TABLE table_1(
ID BIGINT,
NAME STRING
)
COMMENT 'This is table_1 stored as sequencefile'
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS SEQUENCEFILE;
LOAD DATA INPATH 'dir_to/file_name.txt.gz' OVERWRITE INTO TABLE table_1;
LOAD DATA INPATH 'dir_to/file_name.txt.gz' INTO TABLE table_1;
INSERT OVERWRITE TABLE table_1 SELECT id, name from table_1_text;
INSERT INTO TABLE table_1 SELECT id, name from table_1_text;
Is this the optimal way of doing this, or is there a simpler approach to this problem?
Please help!
As gzip textfile file is not splitable ,only one mapper will be launched or
you have to choose other data formats if you want to use more than one
mappers.
If there are huge json files and you want to save storage on hdfs use bzip2
compression to compress your json files on hdfs.You can query .bzip2 json
files from hive without modifying anything.
Related
I have a HIVE Table (test) that I need to create in the PARQUET format. I will be using a bunch of SEQUENCE files in order to create and insert into a table.
Once the table is created, is there a way to convert into PARQUET? I mean I know we could have done, say
CREATE TABLE default.test( user_id STRING, location STRING)
PARTITIONED BY ( dt INT ) STORED AS PARQUET
initially while creating the table itself. However, in my case I am forced to use SEQUENCE files to create the table first because it is the format that I have to begin with and cannot directly convert to PARQUET.
Is there a way I could convert into parquet after the table is created and data inserted?
To convert form sequence file to Parquet you need to load the data (CTAS) into a new table.
The question is tagged with presto, so I am giving you Presto syntax for this. I am including partitioning, because example in the question contains it.
CREATE TABLE test_parquet WITH(format='PARQUET', partitioned_by=ARRAY['dt']) AS
SELECT * FROM test_sequencefile;
I completed my hadoop course now I want to work on Hadoop. I want to know the workflow from data ingestion to visualize the data.
I am aware of how eco system components work and I have built hadoop cluster with 8 datanodes and 1 namenode:
1 namenode --Resourcemanager,Namenode,secondarynamenode,hive
8 datanodes--datanode,Nodemanager
I want to know the following things:
I got data .tar structured files and first 4 lines have got description.how to process this type of data im little bit confused.
1.a Can I directly process the data as these are tar files.if its yes how to remove the data in the first four lines should I need to untar and remove the first 4 lines
1.b and I want to process this data using hive.
Please suggest me how to do that.
Thanks in advance.
Can I directly process the data as these are tar files.
Yes, see the below solution.
if yes, how to remove the data in the first four lines
Starting Hive v0.13.0, There is a table property, tblproperties ("skip.header.line.count"="1") while creating a table to tell Hive the number of rows to ignore. To ignore first four lines - tblproperties ("skip.header.line.count"="4")
CREATE TABLE raw (line STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' LINES TERMINATED BY '\n';
CREATE TABLE raw_sequence (line STRING)
STORED AS SEQUENCEFILE
tblproperties("skip.header.line.count"="4");
LOAD DATA LOCAL INPATH '/tmp/test.tar' INTO TABLE raw;
SET hive.exec.compress.output=true;
SET io.seqfile.compression.type=BLOCK; -- NONE/RECORD/BLOCK (see below)
INSERT OVERWRITE TABLE raw_sequence SELECT * FROM raw;
To view the data:
select * from raw_sequence
Reference: Compressed Data Storage
Follow the below steps to achieve your goal:
Copy the data(ie.tar file) to the client system where hadoop is installed.
Untar the file and manually remove the description and save it in local.
Create the metadata(i.e table) in hive based on the description.
Eg: If the description contains emp_id,emp_no,etc.,then create table in hive using this information and also make note of field separator used in the data file and use the corresponding field separator in create table query. Assumed that file contains two columns which is separated by comma then below is the syntax to create the table in hive.
Create table tablename (emp_id int, emp_no int)
Row Format Delimited
Fields Terminated by ','
Since, data is in structured format, you can load the data into hive table using the below command.
LOAD DATA LOCAL INPATH '/LOCALFILEPATH' INTO TABLE TABLENAME.
Now, local data will be moved to hdfs and loaded into hive table.
Finally, you can query the hive table using SELECT * FROM TABLENAME;
My current system is architected in this way.
Log parser will parse raw log at every 5 minutes with format TSV and output to HDFS. I created Hive table out of the TSV file from HDFS.
From some benchmark, I found that Parquet can save up to 30-40% of the space usage. I also found that I can create Hive table out of Parquet file starting Hive 0.13. I would like know if I can convert TSV to Parquet file.
Any suggestion is appreciated.
Yes, in Hive you can easily convert from one format to another by inserting from one table to the other.
For example, if you have a TSV table defined as:
CREATE TABLE data_tsv
(col1 STRING, col2 INT)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t';
And a Parquet table defined as:
CREATE TABLE data_parquet
(col1 STRING, col2 INT)
STORED AS PARQUET;
You can convert the data with:
INSERT OVERWRITE TABLE data_parquet SELECT * FROM data_tsv;
Or you can skip the Parquet table DDL by:
CREATE TABLE data_parquet STORED AS PARQUET AS SELECT * FROM data_tsv;
I have a data file which is in .txt format. I am using the file to load data into Hive tables. When I load the file in a table like
CREATE TABLE test_details_txt(
visit_id INT,
store_id SMALLINT) STORED AS TEXTFILE;
the data is loaded correctly using
LOAD DATA LOCAL INPATH '/home/user/test_details.txt' INTO TABLE test_details_txt;
and I can run a SELECT * FROM test_details_txt; on the table in Hive.
However If I try to load the data in a table that is
CREATE TABLE test_details_txt(
visit_id INT,
store_id SMALLINT) STORED AS ORC;
I receive the following error on trying to run a SELECT:
Failed with exception java.io.IOException:java.io.IOException: Malformed ORC file hdfs://master:6000/user/hive/warehouse/test.db/transaction_details/test_details.txt. Invalid postscript.
While loading the data using above LOAD statement I do not receive any error or exception.
Is there anything else that needs to be done while using the LOAD DATA IN PATH.. command to store data into an ORC table?
LOAD DATA just copies the files to hive datafiles. Hive does not do any transformation while loading data into tables.
So, in this case the input file /home/user/test_details.txt needs to be in ORC format if you are loading it into an ORC table.
A possible workaround is to create a temporary table with STORED AS TEXT, then LOAD DATA into it, and then copy data from this table to the ORC table.
Here is an example:
CREATE TABLE test_details_txt( visit_id INT, store_id SMALLINT) STORED AS TEXTFILE;
CREATE TABLE test_details_orc( visit_id INT, store_id SMALLINT) STORED AS ORC;
-- Load into Text table
LOAD DATA LOCAL INPATH '/home/user/test_details.txt' INTO TABLE test_details_txt;
-- Copy to ORC table
INSERT INTO TABLE test_details_orc SELECT * FROM test_details_txt;
Steps:
First create a table using stored as TEXTFILE (i.e default or in
whichever format you want to create table)
Load data into text table.
Create table using stored as ORC as select * from text_table;
Select * from orc table.
Example:
CREATE TABLE text_table(line STRING);
LOAD DATA 'path_of_file' OVERWRITE INTO text_table;
CREATE TABLE orc_table STORED AS ORC AS SELECT * FROM text_table;
SELECT * FROM orc_table; /*(it can now be read)*/
Since Hive does not do any transformation to our input data, the format needs to be the same: either the file should be in ORC format, or we can load data from a text file to a text table in Hive.
ORC file is a binary file format, so you can not directly load text files into ORC tables.
ORC stands for Optimized Row Columnar which means it can store data in an optimized way than the other file formats. ORC reduces the size of the original data up to 75%. As a result the speed of data processing also increases. ORC shows better performance than Text, Sequence and RC file formats.
An ORC file contains rows data in groups called as Stripes along with a file footer. ORC format improves the performance when Hive is processing the data.
First you need to create one normal table as textFile, load your data into the textFile table and then you can use insert overwrite query to write your data into ORC file.
create table table_name1 (schema of the table) row format delimited by ',' | stored as TEXTFILE
create table table_name2 (schema of the table) row format delimited by ',' | stored as ORC
load data local inpath ‘path of your file’ into table table_name1;(loading data from a local system)
INSERT OVERWRITE TABLE table_name2 SELECT * FROM table_name1;
Now all your data will be stored in an ORC file.
The similar procedure is applied to all the binary file formats i.e., Sequence files, RC files and Parquet files in Hive.
You can refer to the below link for more details.
https://acadgild.com/blog/file-formats-in-apache-hive/
Steps to load data into ORC file format in hive
1.Create one normal table using textFile format
2.Load the data normally into this table
3.Create one table with the schema of the expected results of your normal hive table using stored as orcfile
4.Insert overwrite query to copy the data from textFile table to orcfile table
Refer the blog to learn the handson of how to load data into all file formats in hive
Load data into all file formats in hive
I have loaded bunch of .gz file into HDFS and when I create a raw table on top of them I am seeing strange behavior when counting number of rows. Comparing the result of the count(*) from the gz table versus the uncompressed table results in ~85% difference. The table that has the file gz compressed has less records. Has anyone seen this?
CREATE EXTERNAL TABLE IF NOT EXISTS test_gz(
col1 string, col2 string, col3 string)
ROW FORMAT DELIMITED
LINES TERMINATED BY '\n'
LOCATION '/data/raw/test_gz'
;
select count(*) from test_gz; result 1,123,456
select count(*) from test; result 7,720,109
I was able to resolve this issue. Somehow the gzip files were not fully getting decompressed in map/reduce jobs (hive or custom java map/reduce). Mapreduce job would only read about ~450 MB of the gzip file and write out the data out to HDFS without fully reading the 3.5GZ file. Strange, no errors at all!
Since the files were compressed on another server, I decompressed them manually and re-compressed them on the hadoop client server. After that, I uploaded the newly compressed 3.5GZ file to HDFS, and then hive was able to fully count all the records reading the whole file.
Marcin