I am accessing HBase data from JSP using Hive queries.Now since Hbase can store huge data like Terabytes of data.If the data is in so much the hive query (which converts into map reduce tasks) will take several minutes of time.So will the JSP page wait say 10 minutes to display the data.What should be the strategy.Is this the correct approach.If not so what is the best approach to show huge hbase data on JSP.
Hive/Any hadoop map-reduce system for that matter, is designed for offline batch processing. Submitting Hive queries from JSP and waiting for an arbitrary amount of time for data to be ready and be shown on the front-end is a definite no-no. If the cluster is super-busy , your jobs might not be even scheduled within the specified time.
What exactly do you want to show from Hbase on the front end ?
If it is a set of rows from a table and you know what the rows are (meaning you have the row key or your application can compute it at run time) , just fetch those rows from and display.
if you have to do some SQL-like operations(joins/ selects etc), then I guess you do realize , HBase is a No-SQL system and you are supposed to do these operations in the application and then fetch the appropriate rows using the row key.
For eg: If you have 2 HBase tables , say Dept (dept Id as row key and a string column(employees) with commma separated list of empIds) and Employee( emp Id as row key and columns Name, Age, Salary) . To find the employee with highest salary in a dept, you have to
a.Fetch the row from the Dept table (using dept Id)
b. Iterate the list of empIds from the employees column.
c. In each iteration , fetch the row from Employee table(by empId row key)
and find the max
Yes HBase can handle TBs of data, but you ll almost never have to show that much of data on the front-end using JSP. Im guessing, you ll most likely be interested in only a portion of the data , though the backing HBase table is much bigger
Related
I am sqooping a table from oracle database to AWS S3 & then creating a hive table over it.
After importing the data, is the order of records present in database preserved in hive table?
I want to fetch few hundred rows from database as well as hive using java JDBC then compare each row present in ResultSet. Assuming I don't have a primary key, can I compare the rows from both ResultSets as they appear(sequentially, using resultSet.next()) or does the order gets changed due to parallel import?
If order isn't preserved whether ORDER BY is a good option?
Order is not preserved during import, also order is not determined when selecting without ORDER BY or DISTRIBUTE+SORT due to parallel select processing.
You need to specify order by when selecting data, does not matter how it was inserted.
ORDER BY orders all data, will work on single reducer, DISTRIBUTE BY + SORT orders per reducer and works in distributed mode.
Also see this answer https://stackoverflow.com/a/40264715/2700344
I wanna know how hive partitioning works I know the concept but I am trying to understand how its working and store the in exact partition.
Let say I have a table and I have created partition on year its dynamic, ingested data from 2013 so how hive create partition and store the exact data in exact partition.
If the table is not partitioned, all the data is stored in one directory without order. If the table is partitioned(eg. by year) data are stored separately in different directories. Each directory is corresponding to one year.
For a non-partitioned table, when you want to fetch the data of year=2010, hive have to scan the whole table to find out the 2010-records. If the table is partitioned, hive just go to the year=2010 directory. More faster and IO efficient
Hive organizes tables into partitions. It is a way of dividing a table into related parts based on the values of partitioned columns such as date.
Partitions - apart from being storage units - also allow the user to efficiently identify the rows that satisfy a certain criteria.
Using partition, it is easy to query a portion of the data.
Tables or partitions are sub-divided into buckets, to provide extra structure to the data that may be used for more efficient querying. Bucketing works based on the value of hash function of some column of a table.
Suppose you need to retrieve the details of all employees who joined in 2012. A query searches the whole table for the required information. However, if you partition the employee data with the year and store it in a separate file, it reduces the query processing time.
I have a bucketed hive table. It has 4 buckets.
CREATE TABLE user(user_id BIGINT, firstname STRING, lastname STRING)
COMMENT 'A bucketed copy of user_info'
CLUSTERED BY(user_id) INTO 4 BUCKETS;
Initially i have inserted some records into this table using the following query.
set hive.enforce.bucketing = true;
insert into user
select * from second_user;
After this operation In HDFS I see that 4 files are created under this table dir.
Again i needed to insert another set of data into user table. So i ran the below query.
set hive.enforce.bucketing = true;
insert into user
select * from third_user;
Now another 4 files are crated under user folder dir. Now it has total 8 files.
Is this fine to do this kind of multiple inserts into a bucketed table?
Does it affect the bucketing of the table?
I figured it out!!
Actually if you do multiple inserts on a bucketed hive table. Hive wont complain as such.
All hive queries will work fine.
Having said that, Such operation spoils the bucketing concept of the table. I mean after multiple inserts into a bucketed table the sampling fails.
The TABLASAMPLE doesnt work properly after multiple inserts.
Even sort merge bucket map join also doesnt work after such operation.
I dont think that should be a issue because you have declared that you want bucketing on user_id. so every time you would insert it will create 4 more files.
Bucketing is used for faster query processing so if it is making 4 more files everytime it will be making your query processing even faster.
My target is to perform a SELECT query using Hive
When I have a small data on a single machine (namenode), I start by:
1-Creating a table that contains this data: create table table1 (int col1, string col2)
2-Loading the data from a file path: load data local inpath 'path' into table table1;
3-Perform my SELECT query: select * from table1 where col1>0
I have huge data, of 10 millions rows that doesn't fit into a single machine. Lets assume Hadoop divided my data into for example 10 datanodes and each datanode contains 1 million row.
Retrieving the data to a single computer is impossible due to its huge size or would take alot of time in case it is possible.
Will Hive create a table at each datanode and perform the SELECT query
or will Hive move all the data a one location (datanode) and create one table? (which is inefficient)
Ok, so I will walk through what happens when you load data into Hive.
The 10 million line file will be cut into 64MB/128MB blocks.
Hadoop, not Hive, will distribute the blocks to the different slave nodes on the cluster.
These blocks will be replicated several times. Default is 3.
Each slave node will contain different blocks that make up the original file, but no machine will contain every block. However, since Hadoop replicates the blocks there must be at least enough empty space on the cluster to accommodate 3x the file size.
When the data is in the cluster Hive will project the table onto the data. The query will be run on the machines Hadoop chooses to work on the blocks that make up the file.
10 million rows isn't that large though. Unless the table has 100 columns you should be fine in any case. However, if you were to do a select * in your query just remember that all that data needs to be sent to the machine that ran the query. That could take a long time depending on file size.
I hope I covered your question. If not please let me know and I'll try to help further.
The query
select * from table1 where col1>0
is just a map side job. So the data block is processed locally at every node. There is no need to collect data centrally.
I need to query table1 find all orders and created date ( key is order number an date)).
In table 2 ( key is order number an date) Check if the order exists for a a date.
For this i am scanning table 1 and for each record checking if it exists in table 2. Any better way to do this
In this situation in which your key is identical for both tables, it makes sense to have a single table in which you store both data for Table 1 and Table 2. In that way you can do a single scan on your data and know straight away if the data exists for both criteria.
Even more so, if you want to use this data in MapReduce, you would simply scan that single table. If you only want to get the relevant rows, you could define a filter on the Scan. For example, in the case where you will not be populating rows at all in Table 2, you would simply use a ColumnPrefixFilter
If, however, you do need to keep this data separately in 2 tables, you could pre-split the tables with the same region boundaries for both tables - this will be helpful when you do the query that you are aiming for - load all rows in Table 1 when row exists in Table 2. Essentially this would be a map-side join. You could define multiple inputs in your MapReduce job, and since the region borders are the same, the splits will be such that each mapper will have corresponding rows from both tables. You would probably need to implement your own MultipleInput format for that (the MultiTableInputFormat class recently introduced in 0.96 does not seem to do that map side join)