What would happen if I'm trying to read from a hive table , while concurrently there is someone inserting into the hive table.
Does it lock the files while querying into it, or does it do a dirty read???
Hadoop is meant for parallel proceesing. So in a cluster parallel querying can be done on a hive table.
While some data is being inserted in the table if another user queries the same table, the files are not locked, rather a job is accepted is put to execution.
Now if the new data insert is successful before the second query
is processed the the result of the second query will
acknowledge the inserted data.
Note: In most cases data insertion takes lesser time than
querying a table because while querying MR jobs are created and
are run in backend
Related
We are stuck with a problem where-in we are trying to do a near real time sync between a RDBMS(Source) and hive (Target). Basically the source is pushing the changes (inserts, updates and deletes) into HDFS as avro files. These are loaded into external tables (with avro schema), into the Hive. There is also a base table in ORC, which has all the records that came in before the Source pushed in the new set of records.
Once the data is received, we have to do a de-duplication (since there could be updates on existing rows) and remove all deleted records (since there could be deletes from the Source).
We are now performing a de-dupe using rank() over partitioned keys on the union of external table and base table. And then the result is then pushed into a new table, swap the names. This is taking a lot of time.
We tried using merges, acid transactions, but rank over partition and then filtering out all the rows has given us the best possible time at this moment.
Is there a better way of doing this? Any suggestions on improving the process altogether? We are having quite a few tables, so we do not have any partitions or buckets at this moment.
You can try with storing all the transactional data into Hbase table.
Storing data into Hbase table using Primary key of RDBMS table as Row Key:-
Once you pull all the data from RDBMS with NiFi processors(executesql,Querydatabasetable..etc) we are going to have output from the processors in Avro format.
You can use ConvertAvroToJson processor and then use SplitJson Processor to split each record from array of json records.
Store all the records in Hbase table having Rowkey as the Primary key in the RDBMS table.
As when we get incremental load based on Last Modified Date field we are going to have updated records and newly added records from the RDBMS table.
If we got update for the existing rowkey then Hbase will overwrite the existing data for that record, for newly added records Hbase will add them as a new record in the table.
Then by using Hive-Hbase integration you can get the Hbase table data exposed using Hive.
https://cwiki.apache.org/confluence/display/Hive/HBaseIntegration
By using this method we are going to have Hbase table that will take care of all the upsert operations and we cannot expect same performance from hive-hbase table vs native hive table will perform faster,as hbase tables are not meant for sql kind of queries, hbase table is most efficient if you are accessing data based on Rowkey,
if we are going to have millions of records then we need to do some tuning to the hive queries
Tuning Hive Queries That Uses Underlying HBase Table
I have an external hive table and I would like to refresh the data files on a daily basis. What is the recommended way to do this?
If I just overwrite the files, and if we are unlucky enough to have some other hive queries to execute in parallel against this table, what will happen to those queries? Will they just fail? Or will my HDFS operations fail? Or will they block until the queries complete?
If availability is a concern and space isn't an issue, you can do the following:
Make a synonym for the external table. Make sure all queries use this synonym when accessing the table.
When loading new data, load it to a new table with a different name.
When the load is complete, point the synonym to the newly loaded table.
After an appropriate length of time (long enough for any running queries to finish), drop the previous table.
First of all.. if you are accessing any table it may have two types of locks:
exclusive(if data is getting added) and shared(if data is getting read)..
so if you insert overwrite and add data into the table then at that time if you access the table with other queries, they wont get executed because there will be an exclusive lock on it and once the insert overwrite query completes then you may access the table.
Please refer to the following link:
https://cwiki.apache.org/confluence/display/Hive/Locking
I recently did an integration between Hive and HBase. I created a hive table with HBase serde and when i insert the records into the hive table it gets loaded into the HBase table. I am trying to understand what if the insert into HiveHBase table fails in between ? (HBase service fails / network issue). I assume the records which have already loaded into the HBase will be there and when i do a rerun of the operation i will have two copies of data with different timestamp (Assuming out of 20K records 10k was inserted and the failure occured).
What is the best way to insert records into HBase ?
Can Hive provide me the security check to see if the data is already there ?
Is mapreduce the best shot for scenarios like these ? I will write a mapreduce program that reads data from hive and checks record by record in hbase before the insertion . This makes sure there are no duplicate writes
Any help on this would be greatly appreciated.
Yes, you will have 2 versions of data when you rerun the load operation. But that's ok since the 2nd version will get cleaned up on the next compaction. As long as your inserts are idempotent (which they most likely are), you won't have a problem.
At Lithium+Klout, we use a custom built HBaseSerDe which writes HFiles, instead of using Put's to insert the data. So we generate the HFiles and use the bulk load tool to load all of the data after the job has completed. That's another way you can integrate Hive and HBase.
HIVE 0.13 will SHARED lock the entire database(I see a node like LOCK-0000000000 as a child of the database node in Zookeeper) when running a select statement on any table in the database. HIVE creates a shared lock on the entire schema even when running a select statement - this results in a freeze on CREATE/DELETE statements on other tables in the database until the original query finishes and the lock is released.
Does anybody know a way around this? Following link suggests concurrency to be turned off but we can't do that as we are replacing the entire table and we have to make sure that no select statement is accessing the table before we replace the entire contents.
http://mail-archives.apache.org/mod_mbox/hive-user/201408.mbox/%3C0eba01cfc035$3501e4f0$9f05aed0$#com%3E
use mydatabase;
select count(*) from large_table limit 1; # this table is very large and hive.support.concurrency=true`
In another hive shell, meanwhile the 1st query is executing:
use mydatabase;
create table sometable (id string) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' STORED AS TEXTFILE ;
The problem is that the “create table” does not execute untill the first query (select) has finished.
Update:
We are using Cloudera's distribution of Hive CDH-5.2.1-1 and we are seeing this issue.
I think they never made such that in Hive 0.13. Please verify your Resource manager and see that you have enough memory when you are executing multiple Hive queries.
As you know each Hive query will trigger a map reduce job and if YARN doesn't have enough resources it will wait till the previous running job completes. Please approach your issue from memory point of view.
All the best !!
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.