So I have a collection of files archived in a HDFS with a unique key in the file-name. I have a table of records in a HIVE table with the same unique key.
How would I provide access to the files to other users? I may need to restrict access to certain users.
I was thinking of providing a reference to the files in the hive table.
I could also look at some sort of web interface for searching for an downloading files.
Hive kicks off a MapReduce job (or several) every time you execute a query. A latency introduced by setting up and tearing down a MapReduce job(s) exceeds any acceptable standards for a responsivness expected from a web interface.
I recommend you keep the metadata for the files in a relational database. You would have to have a relational database, like PostgreSQL, to store Hive metadata. I sure hope you are not using default Derby for that!
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I have started learning Hadoop.I understood that HDFS provides distributed storage system and Mapreduce is for data processing.Now i ma reading Hadoop ecosystem.
From the definition of Hive, it is a data ware house built on hadoop for providing SQL like interface.
My question is when hadoop provides HDFS which is falut tolerant , distributed then why hive? Does hive replaces HDFS?.
Does hive provide only sql interface or storage also?
Hive does not replace HDFS. Hive provides sql type interface to data that is stored in HDFS. Its basically used for querying and analysis of data that is stored. Hive in a sense actually eliminates a lot of boiler plate code, that you would have to write if you were using mapreduce. for example just think of how you are going to create different types of joins(left, right, bucketed) or group by clause or any other sql clause in mapreduce and you will get your answer (you lines of code will easily scale to 100's ). Hive provides them out-of-the-box. You dont need to write those lengthy programs in mapreduce. Hive already does that for you.
One thing to note is, Hive itself uses Mapreduce behind the scenes. So any group by, count, join is converted to mapreduce jobs only. You can change this though to Tez/Spark.
for your second question, hive does not provide any storage, it just uses a database (derby as default, MySQL would be a good choice if you want to use a different db) as a metastore just to store the metadata related to the tables, partitions, views, buckets etc.. (metadata is like location of tables, type of data stored in tables, partitions info of the tables, created date, modified date etc..) you create with hive.
To answer your question in comment...
Hive can process structured (csv,txt etc) data & semi-structured(xml,json,parquet etc). It cannot process unstructured data like audio, video etc.
Note: Semi structured data can be handled in DDLs and also through spark to be put into Hive.
I encourage you to learn what is external and managed tables in hive too.
Happy learning.
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
Is Cassandra a good alternative for Hadoop as a data warehouse where data is append only and all updates in source databases should not overwrite the existing rows in the data warehouse but get appended. Is Cassandra really ment to act as a data warehouse or just as a database to store the results of batch / stream queries?
Cassandra can be used both as a data warehouse(raw data storage) and as a database (for final data storage). It depends more on the cases you want to do with the data.
You even may need to have both Hadoop and Cassandra for different purposes.
Assume, you need to gather and process data from multiple mobile devices and provide some complex aggregation report to the user.
So at first, you need to save data as fast as possible (as new portions appear very often) so you use Cassandra here. As Cassandra is limited in aggregation features, you load data into HDFS and do some processing via HQL scripts (assume, you're not very good at coding but great in complicated SQLs). And then you move the report results from HDFS to Cassandra in a dedicated reports table partitioned by user id.
So when the user wants to have some aggregation report about his activity in the last month, the application takes the id of active user and returns the aggregated result from Cassandra (as it is simple key-value search).
So for your question, yes, it could be an alternative, but the selection strategy depends on the data types and your application business cases.
You can read more information about usage of Cassandra
here
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.
In Hadoop, I can easily create Map/Reduce apps which access and process data in huge text files and csv files. My question is can Hbase do the same and access such huge files, or HBase has other uses?
Hbase runs queries just as relational databases; so, I kind of have a hard time to understand the advantage of HBase, unless it can access huge text and csv files just as Hadoop does.
First of all Hbase is just a store. And a store never accesses anything. Rather you access the store to fetch or put the data. Like any other datastore Hbase has only one job to do, store your data and make it available to you whenever you need it. You can write MapReduce jobs or sequential Java programs etc etc to put data into Hbase or fetch data from it. It's totally upto you which path you prefer.
Coming to the second part of your question, Hbase never ever works like traditional relational databases. Everything, starting from storing the data to accessing the data, is totally different. The advantage of using Hbase is that you can store really really huge amount of data into it and have random read/write access. The data can be of any type viz. text, csv, tsv, binary etc etc. But, before going ahead, you must think well whether Hbase is a suitable choice for you or not, as one size doesn't fit all.
HTH