Hi everybody
I'm quite new with bigdata, I have installed a HDFS + Hbase test database and I use Talend Big Data (an ETL) to make my test.
I would like to know : if I put a file directly in the HDFS, without going via hbase, I could never request these data ? I mean, I have to read the entire file if I want to filter data I want to chose, is that right ?
Thanks a lot for any help !
HDFS is just a distributed file system, you cannot query your files without passing by an intermidiate component.
Hbase is a nosql database that persist your data on the HDFS, use it when you need a random access to your data.
If you want to store your files on the HDFS as they are and query them, you can create an external table upon them using Hive.
The best option is to use hive on the top of the files which are on the HDFS. You can use bucketing and partitioning in the hive for performance improvement.
Related
I want to save and access a table like data structure in HDFS with MapReduce programming. Part of this DS is shown in the following picture. This DS have tens of thousands of columns and hundreds of rows and All nodes should have access to it.
My Question is: How can I save this DS in HDFS and access it with MapReduce programming. Should I use arrays? (Or Hive tables ? Or Hbase?)
Thank you.
HDFS is distributed file System which stores your big files in distributed servers.
You can copy your files from local system to HDFS using command
hadoop fs -copyFromLocal /source/local/path destincation/hdfs/path
Once copy completed an External hive table can be formed on destincation/hdfs/path.
This table can be queried using hive shell.
Do consider Hive for this scenario. If you want to do table type of processing like SAS dataset or R dataframe/dataTable or python pandas; almost always an equivalent thing is possible in SQL. Hive provides powerful SQL abstraction through MapReduce and Tez engines. If you want to graduate to Spark sometime then you can read Hive tables in dataframes. As #sumit pointed you just need to transfer your data from local to HDFS (using HDFS copyFromLocal or put command) and define an external Hive table on that.
If in case you want to write some custom map-reduce on this data then access the background hive table data (more likely at /user/hive/warehouse). After reading the data from stdin, parse it in mapper (separator could be find using describe extended <hive_table>) and emit in key-value pair format.
I am new to Hadoop. I ran a map reduce on my data and now I want to query it so I can put it into my website. Is Apache Hive the best way to do that? I would greatly appreciate any help.
Keep in mind that Hive is a batch processing system, which under the hoods converts the SQL statements to bunch of MapReduce jobs with stage builds in between. Also, Hive is a high latency system i.e. based on your dataset sizes you are looking at minutes to hours or even days to process a complicated query.
So, if you want to serve the results from your MapReduce job output in your website, its highly recommended you export the results back to a RDBMS using sqoop and then take it from there.
Or, if the data itself is huge and cannot be exported back to RDBMS. Then another option you could think of is using a NoSQL system like HBase.
welcome to Hadoop!
I highly recommend you watch Cloudera Essentials for Apache Hadoop | Chapter 5: The Hadoop Ecosystem and familiarize yourself with the different ways to transfer data inbound and outbound from your HDFS cluster. The video is easy-to-watch and describes advantages / disadvantages to each tool, but this outline should give you the basics of the Hadoop Ecosystem:
Flume - Data integration and import of flat files into HDFS. Designed for asynchronous data streams (e.g., log files). Distributed, scalable, and extensible. Supports various endpoints. Allows preprocessing on data before loading to HDFS.
Sqoop - Bidirectional transfer of structured data (RDBMS) and HDFS. Permits incremental import to HDFS. RDBMS must support JDBC or ODBC.
Hive - SQL-like interface to Hadoop. Requires table structure. JDBC and/or ODBC is required.
Hbase - Allows interactive access of HDFS. Sits on top of HDFS and apply structure to data. Allows for random reads, scales horizontally with cluster. Not a full query language; only permits get/put/scan operations (can be used with Hive and/or Impala). Row-key indexes only on data. Does not use Map Reduce paradigm.
Impala - Similar to Hive, high-performance SQL Engine for querying vast amounts of data stored in HDFS. Does not use Map Reduce. Good alternative to Hive.
Pig - Data flow language for transforming large datasets. Permits schema optionally defined at runtime. PigServer (Java API) permits programmatic access.
Note: I assume the data you are trying to read already exists in HDFS. However, some of the products in the Hadoop ecosystem may be useful for your application or as a general reference, so I included them.
If you're only looking to get data from HDFS then yes, you can do so via Hive.
However, you'll most beneficiate from it if your data are already organized (for instance, in columns).
Lets take an example : your map-reduce job produced a csv file named wordcount.csv and containing two rows : word and count. This csv file is on HDFS.
Let's now suppose you want to know the occurence of the word "gloubiboulga". You can simply achieve this via the following code :
CREATE TABLE data
(
word STRING,
count INT,
text2 STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ",";
LOAD DATA LOCAL INPATH '/wordcount.csv'
OVERWRITE INTO TABLE data;
select word, count from data where word=="gloubiboulga";
Please note that while this language looks highly like SQL, you'll still have to learn a few things about it.
As per my understanding both HIVE and HBASE are using HDFS to store the data. When we integrate HIVE and HBASE ----
How the data is moved between them? Or is it like the data wont move and it simply reflects? I am interested to know in 2 scenarios.
One: Table_1 has data and its in HIVE, Table_2 has data and its in HBASE. Now integration happened (whether this scenario possible?).
How the data movement happens? Is it from HBASE to HIVE or HIVE to HBASE.
Two: Setup as scenario One. Now for newly inserted records. Where would they go?
I am new to HBASE and interested in understanding the data movement in detail with and example.
Please improve the question if needed. Thanks in advance.
HDFS is a distributed file system that is well suited for the storage of large files but does not provide fast individual record lookups.
Hive is simply a SQL-like abstraction for interacting with the data in HDFS.
HBase is also built on top of HDFS. It provides fast reads and writes for large tables. HBase accomplishes this by storing your data in indexed "StoreFiles" that exist on HDFS for high-speed lookups.
So in both cases, data reside in HDFS. That's "where they go."
As for the details of how they work, that's a huge topic where you have to familiarize yourself with such topics as the Hive metastore and storage handlers and the HBase API. I believe this tutorial (Part 1 and Part 2) can help you.
We are setting up Hadoop and Hive in our organization.
Also we will be having the sample data created by data generator tool. The data will be around 1 TB.
My question is - i have to load that data into Hive and Hadoop. What is the process i need to follow for this?
Also we will be having HBase installed with Hadoop.
We need to create the same database design which is right now there in SQL Server..But using Hive. Cz after this data loaded into hive we want to use the Business Objects 4.1 as a front end to create the Reports.
The challage is to load the sample data into the Hive..
Please help me as we want to do all the things asap.
First ingest your data in HDFS
Use Hive external tables, pointing to the location where you ingested the data i.e. your hdfs directory.
You are all set to query the data from the tables you created in Hive.
Good luck.
For the first case you need to put data in hdfs.
Transport your data file(s) to a client node (app node)
put your files en distribute file system (hdfs dfs -put ... )
create an external Table pointing the hdfs directory in which you uploaded those files. Your data have been structure of some way. For instance delimited by semicolon symbol.
Now you can operate over the data with sql queries.
For the second case you can create another hive table (using HBaseStorageHandler , https://cwiki.apache.org/confluence/display/Hive/HBaseIntegration) and load from the first table with Insert statement.
I hope this can help you.
I have started working with Hadoop recently. There is table named Checkout that I access through Hive. And below is the path where the data goes to HDFS and other info. So what information I can get if I have to read the below three lines?
Path Size Record Count Date Loaded
/sys/edw/dw_checkout_trans/snapshot/2012/07/04/00 1.13 TB 9,294,245,800 2012-07-05 07:26
/sys/edw/dw_checkout_trans/snapshot/2012/07/03/00 1.13 TB 9,290,477,963 2012-07-04 09:37
/sys/edw/dw_checkout_trans/snapshot/2012/07/02/00 1.12 TB 9,286,199,847 2012-07-03 07:08
So my question is-
1) Firstly, We are loading the data to HDFS and then through Hive I am querying it to get the result back? Right?
2) Secondly, When you look into the above path and other things, the only thing that I am confuse is, when I will be querying using Hive then I will be getting data from all the three paths above? or the most recent one at the top?
As I am new to these stuff, so I am having lot of problem. Can anyone explain me hive gets the data from where? And we store all the data in HDFS and then we use Hive or Pig to get data back from HDFS? And it will be great if some one give high level knowledge of Hadoop and Hive.
I think you need to get the difference between Hive's native table and Hive's external table.
Hive native table mean that you load data into hive, and it takes care how data is stored in the HDFS. We usually do not care what is directory structure in this case.
Hive External table mean that we put data in some directory (if we forget about partitioning for the moment) and tell to Hive - it is table's data. Please treat is as such. And hive enable us to query it, join with other external or regular table. And it is our responsibility to add data, delete it, etc