I am dealing with a problem: I want to make a datavizualisation & prediction infrastructure.
I thought about Kibana+Elasticsearch on Hdfs (with ES-Hadoop), & Spark (Python) on Hdfs for modelisation.
My question is: can I properly index data in Hdfs with ES, or should I use Hive or Spark between Elasticsearch & Hdfs ?
I don't know which architecture is the best way to go.
ES-Hadoop will allow you to index data in HDFS directly with Elasticsearch. If you need to manipulate the data on its way from HDFS to ES, for example, performing lookups or filtering out data based on some criteria, you could use a tool like StreamSets Data Collector - see the blog post for a bit more detail.
Full disclosure - I'm the community champion at StreamSets.
if your question is regarding the performance difference with indexing in hive and hadoop .... There will not be any difference . Even in the case of hive data is stored in HDFS and can be accessed thorough external tables in hive.... the way you want to use the indexes will determine your choice.... Hive will provide a structure on the data and you can apply many inbuilt functions to operate on data...
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In a Cloudera Cluster with Kerberos enabled, I want to index data from a Hive table having Parquet data format, to Cloudera Search(Solr). What is the best way to achieve this? Data may be approx 10-20 Mil.
I found 2 ways so far-
1. Using Map reduce indexing tool and morphlines for Parquet (it would be great if I get some help here)
2. using a custom hive serde, https://github.com/lucidworks/hive-solr, not sure if this will work on higher hive versions.
Are there any other mechanisms to index this data.
The 1.) approach seems to be good for me and according Cloudera Search Guide - MapReduce Indexing.
Are there any other mechanisms to index this data.
Not sure if it would be possible to use ORC's file native-indexes.
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 I am new to Big Data and the related technologies my question is, as the title implies:
When would you use Hadoop and when would you use some kind of NoSQL-Databases to store and analyse massive amounts of data?
I know that Hadoop is a Framework and that Hadoop and NoSQL differs.
But you can save lots of data with Hadoop on HDFS and also with NoSQL-DBs like MongoDB, Neo4j...
So maybe the use of Hadoop or of a NoSQL-Database depends if you just want to analyse data or if you just want to store data?
Or is it just that HDFS can save lets say RAW data and a NoSQL-DB is more structured (more structured than raw data and less structured than a RDBMS)?
Hadoop in an entire framework of which one of the components can be NOSQL.
Hadoop generally refers to cluster of systems working together to analyze data. You can take data from NOSQL and parallel process them using Hadoop.
HBase is a NOSQL that is part of Hadoop ecosystem. You can use other different NOSQL too.
Your question is missleading you are comparing Hadoop, which is a framework, to a database ...
Hadoop is containing a lot of features (including NoSQL database named HBase) in order to provide you a big data environment. If you're having a massive quantity of data you will probably use Hadoop (for the MapReduce functionalities or the datawarehouse capabilities) but it's not sure, depending on what you're processing and how you want to process it. If you're just storing a lot of data and don't need other feature (batch data processing or data transformations ...) a simple NoSQL database is enough.
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.
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.