We are exploring options to archive data in warehouse or RDMS to Hadoop.
As matter of fact I have to use sqoop to load data in to HDFS and probably have to compress it. Then delete the rows which are to be archived.
Trouble is when I have foreign key relation between two tables. I need to maintain data consistency between tables. Please help me with approach.
Luckyly I could find a solution for this using Sqoop API. I was triggering a join query to select the data from child table first then from parent tables. I had writen all the logic in a java program using Sqoop API.
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I'm new to Hive; so, I'm not sure how companies use Hive. Let me give you a scenario and see if I'm conceptually correct about the use of Hive.
Let's say my company wants to keep some web server log files and be able to always search through and analyze the logs. So, I create a table columns of which correspond to the columns in the log file. Then I load the log file into the table. Now, I can start query the data. So, as the data comes in at future dates, I just keep adding the data to this table, and thus I always have my log files as a table in Hive that I can search through and analyze.
Is that scenario above a common use? And if it is, then how do I keep adding new log files to the table? Do I have to keep adding them to the table manually each day?
You can use Hive, for analysis over static datasets, but if you have streaming logs, I really wouldn't suggest Hive for this. It's not a search engine and will take minutes just to find any reasonable data you're looking for.
HBase would probably be a better alternative if you must stay within the Hadoop ecosystem. (Hive can query Hbase)
Use Splunk, or the open source alternatives of Solr / Elasticsearch / Graylog if you want reasonable tools for log analysis.
But to answer your questions
how do I keep adding new log files to the table? Do I have to keep adding them to the table manually each day?
Use an EXTERNAL Hive table over an HDFS location for your logs. Use Flume to send log data to that path (or send your logs to Kafka, and from Kafka to HDFS, as well as a search/analytics system)
You only need to update the table if you're adding date partitions (which you should because that's how you get faster Hive queries). You'd use MSCK REPAIR TABLE to detect missing partitions on HDFS. Or run ALTER TABLE ADD PARTITION yourself on a schedule. Note: Confluent's HDFS Kafka Connect will automatically create Hive table partitions for you
If you must use Hive, you can improve the queries better if you convert the data into ORC or Parquet format
everyone
I am new to Hadoop World and i have some problem with Hbase join.
I have two cluster,clusterA's Hbase have employee table ,clusterB's Hbase have department table.
So,how to join empolyee and department ?
Should i need to install Hive ?
If the tables are in two separate clusters, you'll need to get one of the HBase tables from one cluster to another. This can be done via sqoop.
From there, you could, in theory, use Phoenix as suggested by Vignesh I in the comments, however, there are some limitations there. You would need to create a Phoenix view of both of those HBase tables. Native HBase views in Phoenix, currently, do not automatically update if they are updated outside of Phoenix, which most native HBase tables would be. This effectively renders views of native HBase tables in Phoenix snapshots instead of views; you will need to rebuild any indexes on a regular basis (and potentially stats as well) in order to capture any updates to the underlying HBase tables.
There is a JIRA open to enhance this behavior so that it would auto update, but the ETA of such a feature is unknown at this time.
What I would recommend, unless you have very specific real-time needs (in which case Phoenix, if you could live with the view limitations, may be the better choice), is to use Pig.
Within the Pig script, you can join the two HBase tables and then perform various transformations.
Hive would be another option, but in that case, you would need to sqoop both tables from HBase into Hive, and then proceed from there within Hive.
Im trying to get a clear understanding on HBASE.
Hive:- It just create a Tabular Structure for the Underlying Files in
HDFS. So that we can enable the user to have Querying Abilities on the
HDFS file. Correct me if im wrong here?
Hbase- Again, we have create a Similar table Structure, But bit more
in Structured way( Column Oriented) again over HDFS File system.
aren't they both Same considering the type of job they does. except that Hive runs on Mapredeuce.
Also is that true that we cant create a Hbase table over an Already existing HDFS file?
Hive shares a very similar structures to traditional RDBMS (But Not all), HQL syntax is almost similar to SQL which is good for Database Programmer from learning perspective where as HBase is completely diffrent in the sense that it can be queried only on the basis of its Row Key.
If you want to design a table in RDBMS, you will be following a structured approach in defining columns concentrating more on attributes, while in Hbase the complete design is concentrated around the data, So depending on the type of query to be used we can design a table in Hbase also the columns will be dynamic and will be changing at Runtime (core feature of NoSQL)
You said aren't they both Same considering the type of job they does. except that Hive runs on Mapredeuce .This is not a simple thinking.Because when a hive query is executed, a mapreduce job will be created and triggered.Depending upon data size and complexity it may consume time, since for each mapreduce job, there are some number of steps to do by JobTracker, initializing tasks like maps,combine,shufflesort, reduce etc.
But in case we access HBase, it directly lookup the data they indexed based on specified Scan or Get parameters. Means it just act as a database.
Hive and HBase are completely different things
Hive is a way to create map/reduce jobs for data that resides on HDFS (can be files or HBase)
HBase is an OLTP oriented key-value store that resides on HDFS and can be used in Map/Reduce jobs
In order for Hive to work it holds metadata that maps the HDFS data into tabular data (since SQL works on tables).
I guess it is also important to note that in recent versions Hive is evolving to go beyond a SQL way to write map/reduce jobs and with what HortonWorks calls the "stinger initiative" they have added a dedicated file format (Orc) and import Hive's performance (e.g. with the upcoming Tez execution engine) to deliver SQL on Hadoop (i.e. relatively fast way to run analytics queries for data stored on Hadoop)
Hive:
It's just create a Tabular Structure for the Underlying Files in HDFS. So that we can enable the user to have SQL-like Querying Abilities on existing HDFS files - with typical latency up to minutes.
However, for best performance it's recommended to ETL data into Hive's ORC format.
HBase:
Unlike Hive, HBase is NOT about running SQL queries over existing data in HDFS.
HBase is a strictly-consistent, distributed, low-latency KEY-VALUE STORE.
From The HBase Definitive Guide:
The canonical use case of Bigtable and HBase is the webtable, that is, the web pages
stored while crawling the Internet.
The row key is the reversed URL of the page—for example, org.hbase.www. There is a
column family storing the actual HTML code, the contents family, as well as others
like anchor, which is used to store outgoing links, another one to store inbound links,
and yet another for metadata like language.
Using multiple versions for the contents family allows you to store a few older copies
of the HTML, and is helpful when you want to analyze how often a page changes, for
example. The timestamps used are the actual times when they were fetched from the
crawled website.
The fact that HBase uses HDFS is just an implementation detail: it allows to run HBase on an existing Hadoop cluster, it guarantees redundant storage of data; but it is not a feature in any other sense.
Also is that true that we cant create a Hbase table over an already
existing HDFS file?
No, it's NOT true. Internally HBase stores data in its HFile format.
Background :
I have a Hive Table "log" which contains log information. This table is loaded with new log data every hour. I want to do some quick analytics on logs for past 2 days, so i want to extract last 48 hours of data into my relational database.
To solve the above problem I have created a staging hive table which is loaded by a HIVE SQL query. After loading the new data into the staging table, i load the new logs into relational database using sqoop Query.
Problem is that sqoop is loading data into relational database in BATCH. So at any particular time i have only partial logs for a particular hour.
This is leading to erroneous analytics output.
Questions:
1). How to make this Sqoop data load transactional, i.e either all records are exported or none are exported.
2). What is best way to build this data pipeline where this whole process of Hive Table -> Staging Table -> Relational Table.
Technical Details:
Hadoop version 1.0.4
Hive- 0.9.0
Sqoop - 1.4.2
You should be able to do this with sqoop by using the option called --staging-table. What this does is basically act as an auxiliary table that is used to stage exported data. The staged data is finally moved to the destination table in a single transaction. So by doing this, you shouldn't have consistency issues with partial data.
(source: Sqoop documentation)
Hive and Hadoop are such great technologies that can allow your analytics to run inside MapReduce tasks, performing the analytics very fast by utilizing multiple nodes.
Use that to your benefit. First of all partition your Hive table.
I guess that you store all logs in a single Hive table. Thus when you run your queries and you have a
SQL .... WHERE LOG_DATA > '17/10/2013 00:00:00'
Then you effictivelly query all the data that you have collected so far.
Instead if you use partitions - let's say one per day you can define in your query
WHERE p_date=20131017 OR p_date=20131016
Hive is partitioned and now knows to read only those two files
So let's say you got 10 GB of logs per day - then a HIVE QUERY should succeed in a few seconds in a decent Hadoop cluster
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