I have a design question where in in my CDH 4.1.2(Cloudera) installation I have daily rolling log data dumped into the HDFS. I have some reports to calculate the success and failure rates per day.
I have two approaches
load the daily log data into Hive Tables and create a complex query.
Run a MapReduce job upfront everyday to generate the summary (which
is essentially few lines) and keep appending to a common file which is a Hive Table. Later while running the report I could use a simple select query to fetch the summary.
I am trying to understand which would be a better approach among the two or if there is a better one.
The second approach adds some complexity in terms of merging files. If not merged I would have lots of very small files which seems to be a bad idea.
Your inputs are appreciated.
Thanks
Hive seems well suited to this kind of tasks, and it should be fairly simple to do:
Create an EXTERNAL table in Hive which should be partitioned by day. The goal is that the directory where you will dump your data will be directly in your Hive table. You can specify the delimiter of the fields in your daily logs like shown below where I use commas:
create external table mytable(...) partitioned by (day string) row format delimited keys terminated by ',' location '/user/hive/warehouse/mytable`
When you dump your data in HDFS, make sure you dump it on the same directory with day= so it can be recognized as a hive partition. For example in /user/hive/warehouse/mytable/day=2013-01-23.
You need then to let Hive know that this table has a new partition:
alter table mytable add partition (day='2013-01-23')
Now the Hive metastore knows about your partition, you can run your summary query. Make sure you're only querying the partition by specifying ... where day='2013-01-23'
You could easily script that to run daily in cron or something else and get the current date (for example with the shell date command) and substitute variables to this in a shell script doing the steps above.
Related
At my organization we are trying to use HIVE Or PIG as alternative
Primary goal : reduce process time
NETEZZA process time : 90 min
looking to end process : within 30 min
How does process works:
Process is about to maintain incremental history.There are two tables history_table and new_table.History table maintains total history and new
_table has updated records.So every day updated records are added to history table.Process has very complex stored procedures (Joins/deletion/insert/update)
same process is being applied on multiple tables.Every history table has almost billions of records.
Doubts I have :
Does HIVE/PIG perform better than NETEZZA ?
Is UDF in hive a good alternative for Stored Procedure as I want to create generic process for multiple tables (where I can pass table name as argument) ?
Which performs better HIVE or PIG for really complex joins with multiple condition, generating create statement dynamically and exception handling?
use impala which is Netezza on Hadoop, try Kudu for real time and batch or use HBase for real time and impala for batch , but you can query HBase using impala
I'm currently implementing ETL (Talend) of monitoring data to HDFS, and Hive table.
I am now facing concerns about duplicates. More in details, if we need to run one ETL Job 2 times with the same input, we will end up with duplicates in our Hive table.
The solution to that in RDMS would have been to store the input file name and to "DELETE WHERE file name=..." before sending the data. But Hive is not a RDBMS, and does not support deletes.
I would like to have your advice on how to handle this. I envisage two solutions :
Actually, the ETL is putting CSV files to the HDFS, which are used to feed an ORC table with a "INSERT INTO TABLE ... SELECT ..." The problem is that, with this operation, I'm losing the file name, and the ORC file is named 00000. Is it possible to specify the file name of this created ORC file ? If yes, I would be able to search the data by it's file name and delete it before launching the ETL.
I'm not used to Hive's ACID capability (feature on Hive 0.14+). Would you recommend to enable ACID with Hive ? Will I be able to "DELETE WHERE" with it ?
Feel free to propose should you have any other solution to that.
Bests,
Orlando
If the data volume in target table is not too large, I would advise
INSERT INTO TABLE trg
SELECT ... FROM src
WHERE NOT EXISTS
(SELECT 1
FROM trg x
WHERE x.key =src.key
AND <<additional filter on target to reduce data volume>>
)
Hive will automatically rewrite the correlated sub-query into a MapJoin, extracting all candidate keys in target table into a Java HashMap, and filtering source rows on-the-fly. As long as the HashMap can fit in the RAM available for Mappers heap size (check your default conf files, increase with a set command in Hive script if necessary) the performance will be sub-optimal, but you can be pretty sure that you will not have any duplicate.
And in your actual use case you don't have to check each key but only a "batch ID", more precisely the original file name; the way I've done it in my previous job was
INSERT INTO TABLE trg
SELECT ..., INPUT__FILE__NAME as original_file_name
FROM src
WHERE NOT EXISTS
(SELECT DISTINCT 1
FROM trg x
WHERE x.INPUT__FILE__NAME =src.original_file_name
AND <<additional filter on target to reduce data volume>>
)
That implies an extra column in your target table, but since ORC is a columnar format, it's the number of distinct values that matter -- so that the overhead would stay low.
Note the explicit "DISTINCT" in the sub-query; a mature DBMS optimizer would automatically do it at execution time, but Hive does not (not yet) so you have to force it. Note also the "1" is just a dummy value required because of "SELECT" semantics; again, a mature DBMS would allow a dummy "null" but some versions of Hive would crash (e.g. with Tez in V0.14) so "1" or "'A'" are safer.
Reference:
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+SubQueries#LanguageManualSubQueries-SubqueriesintheWHEREClause
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+VirtualColumns
I'm answering myself. I found a solution :
I partitionned my table with (date,input_file_name) (note, I can get the input_file_name with SELECT INPUT__FILE__NAME in Hive.
Once I did this, before running the ETL, I can send to Hive an ALTER TABLE DROP IF EXISTS PARTITION (file_name=...) so that the folder containing the input data is deleted if this INPUT_FILE has already been sent to the ORC table.
Thank you everyone for your help.
Cheers,
Orlando
lets say, I created Hive external table "myTable" from file myFile.csv ( located in HDFS ).
myFile.csv is changed every day, then I'm interested to update "myTable" once a day too.
Is there any HiveQL query that tells to update the table every day?
Thank you.
P.S.
I would like to know if it works the same way with directories: lets say, I create Hive partition from HDFS directory "myDir", when "myDir" contains 10 files. next day "myDIr" contains 20 files (10 files were added). Should I update Hive partition?
There are two types of tables in Hive basically.
One is Managed table managed by hive warehouse whenever you create a table data will be copied to internal warehouse.
You can not have latest data in the query output.
Other is external table in which hive will not copy its data to internal warehouse.
So whenever you fire query on table then it retrieves data from the file.
SO you can even have the latest data in the query output.
That is one of the goals of external table.
You can even drop the table and the data is not lost.
If you add a LOCATION '/path/to/myFile.csv' clause to your table create statement, you shouldn't have to update anything in Hive. It will always use the latest version of the file in queries.
I am evaluating the combination of hadoop & hive (& impala) as a repolacement for a large data warehouse. I already set up a version and performance is great in read access.
Can somebody give me any hint what concept should be used for daily data deliveries to a table?
I have a table in hive based on a file I put into hdfs. But now I have on a daily basis new transactional data coming in.
How do I add them ti the table in hive.
Inserts are not possible. HDFS cannot append. So whats the gernal concept I need to follow.
Any advice or direction to documentation is appreciated.
Best regards!
Hive allows for data to be appended to a table - the underlying implementation of how this happens in HDFS doesn't matter. There are a number of things you can do append data:
INSERT - You can just append rows to an existing table.
INSERT OVERWRITE - If you have to process data, you can perform an INSERT OVERWRITE to re-write a table or partition.
LOAD DATA - You can use this to bulk insert data into a table and, optionally, use the OVERWRITE keyword to wipe out any existing data.
Partition your data.
Load data into a new table and swap the partition in
Partitioning is great if you know you're going to be performing date based searches and gives you the ability to use options 1, 2, & 3 at either the table or partition level.
Inserts are not possible
Inserts are possible ,like you can create a new table and insert the data from new table to old table.
But simple solution is You can load data of the file into Hive table with the below command.
load data inpath '/filepath' [overwrite] into table tablename;
If you use overwrite then only existing data replced with new data otherwise It is appending only.
You can even schedule the script by creating a shell script.
recently I want to load the log files into hive tables, I want a tool which can read data from a certain directory and load them into hive automatically. This directory may include lots of subdirectories, for example, the certain directory is '/log' and the subdirectories are '/log/20130115','/log/20130116','/log/201301017'. Is there some ETL tools which can achieve the function that:once the new data is stored in the certain directory, the tool can detect this data automatically and load them into hive table. Is there such tools, do I have to write script by myself?
You can easily do this using Hive external tables and partitioning your table by day. For example, create your table as such:
create external table mytable(...)
partitioned by (day string)
location '/user/hive/warehouse/mytable';
This will essentially create an empty table in the metastore and make it point to /user/hive/warehouse/mytable.
Then you can load your data in this directory with the format key=value where key is your partition name (here "day") and value is the value of your partition. For example:
hadoop fs -put /log/20130115 /user/hive/warehouse/mytable/day=20130115
Once your data is loaded there, it is in the HDFS directory, but the Hive metastore doesn't know yet that it belongs to the table, so you can add it this way:
alter table mytable add partition(day='20130115');
And you should be good to go, the metastore will be updated with your new partition, and you can now query your table on this partition.
This should be trivial to script, you can create a cron job running once a day that will do these command in order and find the partition to load with the date command, for example continuously doing this command:
hadoop fs -test /log/`date +%Y%m%d`
and checking if $? is equal to 0 will tell you if the file is here and if it is, you can transfer it and add the partition as described above.
You can make use of LOAD DATA command provided by Hive. It exactly matches your use case. Specify a directory in your local file system and make Hive tables from it.
Example usage -
LOAD DATA LOCAL INPATH '/home/user/some-directory'
OVERWRITE INTO TABLE table