Sunil S. Ranka's Weblog

Superior Data Analytics is the antidote to Business Failure

Posts Tagged ‘sunil s ranka’

What is Oracle Business Intelligence Cloud Service ( BICS )

Posted by sranka on August 11, 2016

Recently we have been getting lots of traction on BICS , existing OBIEE customers been asking for BICS . In nutshell :

BI Cloud Service enables organisations of all sizes to quickly and cost effectively deploy business intelligence with the simplicity of the cloud..

Silent features of BICS :

  • No need of software installation
  • No need of software maintenance
  • No upfront costs, low monthly subscription
  • Customers can get started in hours
  • 100% cloud based
  • Robust reporting with interactive visuals, auto-suggestions, detailed formatting, export, and more
  • Powerful analytics platform with advanced calculations and analytic functions
  • Easy self-serve data loading
  • Rich data integration options
  • Mobile access with no extra programming required
  • Comprehensive sharing framework
  • Role-based fine grain security
  • Simple self-service administration

Key Benefits :

  • Fast access and low cost speed time to value
  • Quick start means users are productive quickly
  • A single BI platform for all users helps consolidate analytic investments
  • Timely access to data means greater impact
  • Streamlined operations and reduced burden on IT

Summary :

Based on my past experience of working on OBIEE on premise, BICS is a good alternative for any IT organisation, BICS gives all the needed feature of On-Premise installation and flexibility of operation, management , and most importantly low cost solution. In next few post, I will describe more about BICS tool and features.

Hope this helps

Sunil S Ranka

“Superior Data Analytics is the antidote to Business Failure”

 

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Cloud Allergy – Clouds Security and Changing Notion

Posted by sranka on June 30, 2016

With my recent role as CTO/Advisor with www.analytos.com, during most of my conversation with Analytics leaders within the company, all are concern over security. At a recent conversation with another entrepreneur friend, one of his solution was stalled due to SQL injection issue on the cloud ( a valid concern , but is it valid ?) .

During my recent startup sting, cloud Allergy word was coined, and it did make sense, because allergies do exist and you need to go past them , and need to worry about the only life threating ones.

My Early Internet Days

I remember the year 1996 , when I had created my 1st email address — coolguy123@yahoo.com –,  20 years back we were apprehensive about using our real name as part of the email address, now past 20 years, only hackers and late night chat rooms create fake ids. In the year 2001, when I got my 1st credit card ( $500 credit limit ), using it for online shopping was a taboo, in fact till mid of 2005 I had paid my PG&E bill in person at the authorized facility . With the mindset, The fear was not to use personal or financial information over the public internet.

Changing Notion

Come the year 2013 ( within 15 years ) , using a credit card is a norm, giving credit card number to a Comcast agent seating overseas is a trivial and nonissue.  With the notion of facebook, whatsApps, SnapChat and many more social Apps, we take pride and  effort to share personal and important moments with our — extended Social Families — (Yes, just coined a new word ).  With google search data retention capability, I tell my customer — Google Knows you more than your Wife or partner — Most of us take backup of most important documents by sending via email to yourself.

Most importantly kp.org (Kaiser Permanente, a leading national HMO) has all the personal information about your recent visits, vaccination and secured messaging through their enhanced portal .

With the mobile banking capability taking a photo of cheque and depositing is just another norm.

With the changing notion, we will go past the – cloud allergy — behaviour and some of the security questions and concerns will be trivial or non-issue.

Giant Cloud Providers and Security Capabilities

At times if you look at the public clouds, AWS, Google, and MS Azure, these giants are able to attract more talented individuals than most of the companies small to mid-size companies. With cloud being their core focus, they have hundreds of brilliant minds dedicated to security. A company with a modest budget can not match the level of expertise prominent cloud providers can spend on security. Unlike earlier, Fast Deployment, Lower Costs, and Rapid Time to Value have assumed advantages of cloud, security will/is achieving the same level of confidence.

Public clouds at times are much safer than the internal network ( Sony and Target hacking were the best example we all can use )

Trust in and adoption of cloud computing continues to grow despite persistent cloud-related security and compliance concerns. Such is the overarching takeaway of Intel Security’s recent report, “Blue Skies Ahead? The State of Cloud Adoption.” – See more at http://www.baselinemag.com/cloud-computing/slideshows/cloud-deployments-grow-despite-security-concerns.html#sthash.nXNytNaT.dpuf

Different Cloud Service Models :  

With the evolving nature of the cloud, Understanding the relationships and dependencies between different cloud servicing models are critical to understanding cloud computing security risks. IaaS is the foundation of all cloud services, with PaaS building upon IaaS, and SaaS, in turn, building upon PaaS as described in the Cloud.

** Infrastructure as a Service (IaaS), delivers computer infrastructure (typically a platform virtualization environment) as a service, along with raw storage and networking. Rather than purchasing servers, software, data-center space, or network equipment, clients instead buy those resources as a fully outsourced service.

** Software as a service (SaaS), sometimes referred to as “on-demand software,” is a software delivery model in which software and its associated data are hosted centrally (typically in the (Internet) cloud) and are typically accessed by users using a thin client, normally using a web browser over the Internet.

** Platform as a service (PaaS), is the delivery of a computing platform and solution stack as a service. PaaS offerings facilitate deployment of applications without the cost and complexity of buying and managing the underlying hardware and software and provisioning hosting capabilities. This provides all of the facilities required to support the complete life cycle of building and delivering web applications and services entirely available from the Internet.

** Definitions are taken from the internet.

** The figure below shows an example of how a cloud service mapping can be compared against a catalogue of compensating controls to determine which controls exist and which do not — as provided by the consumer, the cloud service provider, or a third party. This can, in turn, be compared to a compliance framework or set of requirements such as PCI DSS, as shown.

Picture1

** Mapping the Cloud Model to the Security Control & Compliance

 

** Text and Figure Taken from CSA (Cloud Security Alliance).

 

Conclusion:

Customer needs to be made aware of what they are considering moving to the cloud. Not every dataset moved to the cloud, needs the same level of security. For low critical dataset, lower security can be used. For a high-value dataset with audit, compliance requirement might entail audit and data retention requirements, for high-value dataset with no regularity compliance restrictions, there could me need for more technical security than the data retention. In short, there would be always a place for all type of dataset in the cloud.

 

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More Animals in Big Data Zoo – Big Data Landscape for 2016

Posted by sranka on March 26, 2016

Hi All

While surfing net stumbled upon Big Data Landscape for 2016 image and it was very impressive to see many more new Animals in Big Data Zoo.

 

New Animals

Hope This Helps

Sunil S Ranka

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Big Data – Tez, MR, Spark Execution Engine : Performance Comparison

Posted by sranka on February 25, 2016

There is no question that massive data is being generated in greater volumes than ever before. Along with the traditional data set, new data sources as sensors, application logs, IOT devices, and social networks are adding to data growth. Unlike traditional ETL platforms like Informatica, ODI, DataStage that are largely proprietary commercial products, the majority of Big ETL platforms are powered by open source.

With many execution engines, customers are always curious about their usage and performance.

To put it into perspective, In this post I am running set of query against 3 key Query Engines namely Tez, MapReduce, Spark (MapReduce) to compare the query execution timings.

create external table sensordata_csv
(
ts string,
deviceid int,
sensorid int,
val double
)
row format delimited
fields terminated by '|'
stored as textfile
location '/user/sranka/MachineData/sensordata'
;

drop table sensordata_part;

create table sensordata_part
(
deviceid int,
sensorid int,
val double
)
partitioned by (ts string)
clustered by (deviceid) sorted by (deviceid) into 10 buckets
stored as orc
;

"**********************************************"
"** 1) Baseline: Read a csv without Tez"
" set hive.execution.engine=mr"
" select count(*) from sensordata_csv where ts = '2014-01-01'"
"**********************************************"
2016-02-25 02:57:27,444 Stage-1 map = 0%,  reduce = 0%
2016-02-25 02:57:35,880 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.84 sec
2016-02-25 02:57:44,420 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 4.99 sec
MapReduce Total cumulative CPU time: 4 seconds 990 msec
Ended Job = job_1456183816302_0046
MapReduce Jobs Launched:
Job 0: Map: 1  Reduce: 1   Cumulative CPU: 4.99 sec   HDFS Read: 3499156 HDFS Write: 6 SUCCESS
Total MapReduce CPU Time Spent: 4 seconds 990 msec
OK
16733
Time taken: 32.524 seconds, Fetched: 1 row(s)

"**********************************************"
"** 2) Read a csv with Tez"
" set hive.execution.engine=tez"
" select count(*) from sensordata_csv where ts = '2014-01-01'"
"**********************************************"
Total jobs = 1
Launching Job 1 out of 1

Status: Running (application id: application_1456183816302_0047)

Map 1: -/-    Reducer 2: 0/1
Map 1: 0/1    Reducer 2: 0/1
Map 1: 0/1    Reducer 2: 0/1
Map 1: 0/1    Reducer 2: 0/1
Map 1: 1/1    Reducer 2: 0/1
Map 1: 1/1    Reducer 2: 1/1
Status: Finished successfully
OK
16733
Time taken: 16.905 seconds, Fetched: 1 row(s)

"**********************************************"
"** 3) Read a partition with Tez"
" select count(*) from sensordata_part where ts = '2014-01-01'"
"**********************************************"
Total jobs = 1
Launching Job 1 out of 1
Status: Running (application id: application_1456183816302_0047)

Map 1: -/-    Reducer 2: 0/1
Map 1: 0/2    Reducer 2: 0/1
Map 1: 1/2    Reducer 2: 0/1
Map 1: 2/2    Reducer 2: 0/1
Map 1: 2/2    Reducer 2: 1/1
Status: Finished successfully
OK
16733
Time taken: 6.503 seconds, Fetched: 1 row(s)

"**********************************************"
"** 4) Read a partition with Spark"
" select count(*) from sensordata_part where ts = '2014-01-01'"
"**********************************************"

Time taken: took 5.8 seconds

"**********************************************"
"** 5) Read a csv with Spark"
" select count(*) from sensordata_csv where ts = '2014-01-01'"
"**********************************************"
Time taken: took 4.5 seconds

Query 1select count(*) from sensordata_csv where ts = ‘2014-01-01’

Query 2select count(*) from sensordata_part where ts = ‘2014-01-01’

Below tables shows the execution timings :
Screen Shot 2016-02-24 at 11.07.03 PM

Conclusion Which Engine is right :

Spark being In memory execution engine comes out to be a clear winner, but in certain scenario especially in the current scenario of running query on partition table TEZ execution engines comes closer to spark.

With this you can not conclude that you Spark will solve your — World Hunger Problem — of Big ETL, being continuously growing product Spark has its own challenges when it comes to productization of the Spark workload, same holds True with TEZ. In all MR engine has been around for the most time and its been the core of HDFS framework, for mission critical workloads which are not time bound, MR could be the best choice.

Hope This Helps

Sunil S Ranka

About Spark : http://spark.apache.org/

About MapReduce : https://en.wikipedia.org/wiki/MapReduce

About Tez : https://tez.apache.org/

Posted in Hadoop | Tagged: , , , , , , , , , , | 1 Comment »

Permissions for both HDFS and local fileSystem paths

Posted by sranka on July 18, 2014

Hi All,

Permission issues is one of the key error , while setting up Hadoop Cluster, while debugging some error found below table on http://hadoop.apache.org/ . It’s a good scorecard to keep handy.

 

Permissions for both HDFS and local fileSystem paths

The following table lists various paths on HDFS and local filesystems (on all nodes) and recommended permissions:

Filesystem Path User:Group Permissions
local dfs.namenode.name.dir hdfs:hadoop drwx——
local dfs.datanode.data.dir hdfs:hadoop drwx——
local $HADOOP_LOG_DIR hdfs:hadoop drwxrwxr-x
local $YARN_LOG_DIR yarn:hadoop drwxrwxr-x
local yarn.nodemanager.local-dirs yarn:hadoop drwxr-xr-x
local yarn.nodemanager.log-dirs yarn:hadoop drwxr-xr-x
local container-executor root:hadoop –Sr-s—
local conf/container-executor.cfg root:hadoop r——–
hdfs / hdfs:hadoop drwxr-xr-x
hdfs /tmp hdfs:hadoop drwxrwxrwxt
hdfs /user hdfs:hadoop drwxr-xr-x
hdfs yarn.nodemanager.remote-app-log-dir yarn:hadoop drwxrwxrwxt
hdfs mapreduce.jobhistory.intermediate-done-dir mapred:hadoop drwxrwxrwxt
hdfs mapreduce.jobhistory.done-dir mapred:hadoop drwxr-x—

Hope this helps

Sunil S Ranka

“Superior BI is the antidote to Business Failure”

This table was taken directly from http://hadoop.apache.org/docs/r2.3.0/hadoop-project-dist/hadoop-common/SecureMode.html

 

Posted in 11g, Big Data | Tagged: , , , , , , , | 1 Comment »

Need for Defining Reference Architecture For Big Data

Posted by sranka on May 7, 2014

Hi Fellow Big Data Admirers ,

With big data and analytics playing an influential role helping organizations achieve a competitive advantage, IT managers are advised not to deploy big data in silos but instead to take a holistic approach toward it and define a base reference architecture even before contemplating positioning the necessary tools. 

My latest print media article (5th in the series) for CIO magazine (ITNEXT) talks extensively about need of reference architecture in Big Data

Click Here For : Need For Defining Big Data Reference Architecture

 

Hope you Enjoy Reading this.

Hope this helps

Sunil S Ranka

“Superior BI is the antidote to Business Failure”

For copy of May 2014 IT Next Magazine please visit http://www.itnext.in/digital_assets/330/IT-NEXT-Vol-05-Issue-04-May-2014.pdf ( My Article is on Page 37 )

 

 

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How to find out a table type in Hive Metastore.

Posted by sranka on April 10, 2014

Hi All

As Hive metastore is getting into the center of nervous system for the different type of  SQL engines like Shark and Impala. It getting equally difficult to distinguish type of table created in Hive metastore. Eg. if we create a impala table using impala shell you will see the same table on hive prompt and vice versa. See the below example

 

Step 1 : “Create Table” in Impala Shell and “Show Table” On HIVE Shell


[samvi.saarth.dev.com:21000] > create table impala_table ( id bigint);

[samvi.saarth.dev.com:21000] > show tables 'impala_table';

Query: show tables 'impala_table'
Query finished, fetching results ...
+--------------+
| name             |
+--------------+
| impala_table |
+--------------+
Returned 1 row(s) in 0.01s

hive> show tables 'impala_table';
OK
impala_table
Time taken: 0.073 seconds

Step 2 : “Create Table” in Hive Shell and “Show Table” On Impala Shell

hive> create table hive_table ( id bigint);
OK
Time taken: 0.058 seconds

Step 3 : Invalidate Metadata on Impala Shell ( This may not be needed always )


[samvi.saarth.dev.com:21000] > invalidate metadata;
Query: invalidate metadata
Query finished, fetching results ...

Returned 0 row(s) in 5.11s

Step 4 : “Show Table” On Impala Shell

 

[samvi.saarth.dev.com:21000] > show tables 'hive_table';
Query: show tables 'hive_table'
Query finished, fetching results ...
+------------+
| name       |
+------------+
| hive_table |
+------------+
Returned 1 row(s) in 0.01s

In short this proves that tables are visible in both shells. Use describe formatted <table name>  command to find out the details. Storage Desc Params will show a value “serialization.format” for hive table, where in for Impala table, we will not have any value.

 

hive> describe formatted hive_table;
OK
# col_name              data_type               comment

id                      bigint                  None

# Detailed Table Information
Database:               default
Owner:                  rsunil
CreateTime:             Thu Apr 10 13:13:09 PDT 2014
LastAccessTime:         UNKNOWN
Protect Mode:           None
Retention:              0
Location:               hdfs://samvi.saarth.dev.com:8020/app/hadoop/hive/warehouse/hive_table
Table Type:             MANAGED_TABLE
Table Parameters:
transient_lastDdlTime   1397160789

# Storage Information
SerDe Library:          org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat:            org.apache.hadoop.mapred.TextInputFormat
OutputFormat:           org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
Compressed:             No
Num Buckets:            -1
Bucket Columns:         []
Sort Columns:           []
Storage Desc Params:

serialization.format    1

Time taken: 0.115 seconds

 




hive> describe formatted impala_table;
OK
# col_name data_type comment

id bigint None

# Detailed Table Information
Database: default
Owner: rsunil
CreateTime: Thu Apr 10 13:10:30 PDT 2014
LastAccessTime: UNKNOWN
Protect Mode: None
Retention: 0
Location: hdfs://samvi.saarth.dev.com:8020/app/hadoop/hive/warehouse/impala_table
Table Type: MANAGED_TABLE
Table Parameters:
transient_lastDdlTime 1397160630

# Storage Information
SerDe Library: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat: org.apache.hadoop.mapred.TextInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
Compressed: No
Num Buckets: 0
Bucket Columns: []
Sort Columns: []
Time taken: 0.185 seconds

 

 

For tables created in impala with Parquet format will give below class exception.

hive> describe formatted parquet_ob_mdm_et28;
FAILED: RuntimeException java.lang.ClassNotFoundException: com.cloudera.impala.hive.serde.ParquetInputFormat</pre>
<pre>

Hope this helps

Sunil S Ranka

“Superior BI is the antidote to Business Failure”

Posted in Big Data | Tagged: , , , , , , , , | 1 Comment »

How To Create External Hive Table on HBase

Posted by sranka on March 28, 2014

Hi All,

While building a data flow for replacing one of the EDW’ workflow using Big Data technology stack , came across some interesting findings and issues.  Due to  UPSERT ( INSERT new records or UPDATE existing records depending) nature of data we had to use Hbase, but to expose the outbound feed we need to do some calculation on HBase and publish that to Hive as external. Even though conceptually , its easy to create an external hive table on HBase is simple, but I had to go through some hoop.

 


Table Creation in hbase
hbase(main):002:0> create 'mytable', 'cf'
hbase(main):004:0> put 'mytable', 'first', 'cf:message', 'hello HBase'
hbase(main):005:0> put 'mytable', 'second', 'cf:foo', 0x0
0 row(s) in 0.0130 seconds
hbase(main):006:0> put 'mytable', 'third', 'cf:bar', 3.14159
0 row(s) in 0.0080 second

hbase(main):002:0> describe 'mytable'
DESCRIPTION ENABLED
'mytable', {NAME => 'cf', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'NONE', REPLICA true
TION_SCOPE => '0', VERSIONS => '3', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL =>
'2147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false'
, ENCODE_ON_DISK => 'true', BLOCKCACHE => 'true'}
1 row(s) in 0.9610 seconds

hbase(main):003:0> scan 'mytable'
ROW COLUMN+CELL
first column=cf:foo, timestamp=1395167684857, value=0
first column=cf:message, timestamp=1395167407496, value=hello HBase
second column=cf:foo, timestamp=1395167483988, value=0
third column=cf:bar, timestamp=1395167493639, value=3.14159
3 row(s) in 0.0760 seconds

Table Creation in Hive


Hive >  CREATE EXTERNAL TABLE hbase_table_3(key string, value string,value1 string) STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key,cf:foo,cf:message") TBLPROPERTIES ("hbase.table.name" = "mytable");

Table Access in Hive

hive> set hbase.client.scanner.caching=50000;
hive> desc hbase_table_3;
OK
key     string  from deserializer
value   string  from deserializer
value1  string  from deserializer
Time taken: 0.428 seconds
hive>

Zookeeper ,  Aux Path and hbase.client.scanner.caching 

Zookeeper is an important part of  Hadoop ecosystem, it works as a Resource Management service. You would need to make sure that it has a quorum with odd numbers (1,3,5) of instances. For accessing external table you need to have zookeeper services up and running.  Along with zookeeper you will need to make few changes.

If you have big HBase table, you will need to set higher hbase.client.scanner.caching property before running the Hbase query. In our case we use 50000.

See below changes needed to hive-site.xml and hbase-site.xml.

Changes in hive-site.xml

<property>
  <name>hive.zookeeper.quorum</name>
   <value>devapphdp08.samvi.com,devapphdp09.samvi.com,devapphdp07.samvi.com</value>
</property>
<property>
 <name>hive.aux.jars.path</name>
 <value>file:///usr/lib/hive/lib/hive-hbase-handler-0.10.0-cdh4.6.0.jar,file:///usr/lib/hive/lib/hbase.jar,file:///usr/lib/zookeeper/zookeeper.jar</value>
</property>

 

Changes in hbase-site.xml

<property>
  <name>hive.zookeeper.quorum</name>
   <value>devapphdp08.samvi.com,devapphdp09.samvi.com,devapphdp07.samvi.com</value>
</property>

 Linux Performance Tuning

Some of the following commands have helped enhancing performance.

echo 1 > /proc/sys/vm/drop_caches
echo 2 > /proc/sys/vm/drop_caches
echo 3 > /proc/sys/vm/drop_caches

 

Special Thanks to Aditi Hedge, Rathinavel Sivaswamy and Anurag Gupta for their inputs.

Hope this helps

Sunil S Ranka

“Superior BI is the antidote to Business Failure”

Posted in Big Data, HBase | Tagged: , , , , , , , , | Leave a Comment »

Hbase : Co-relation between RegionServer and Region

Posted by sranka on March 21, 2014

Hi All

While looking into HBase performance issue, one of the suggestion was to have more region for a larger table. There was some confusion around, “Region” vs “RegionServer” . While doing some digging, found a simple text written below.

The basic unit of scalability and load balancing in HBase is called a region. Regions are essentially contiguous ranges of rows stored together. They are dynamically split by the system when they become too large. Alternatively, they may also be merged to reduce their number and required storage files.*

The HBase regions are equivalent to range partitions as used in database sharding. They can be spread across many physical servers, thus distributing the load, and therefore providing scalability

Initially there is only one region for a table, and as you start adding data to it, the system is monitoring it to ensure that you do not exceed a configured maximum size. If you exceed the limit, the region is split into two at the middle key—the row key in the middle of the region—creating two roughly equal halves.

Each region is served by exactly one region server, and each of these servers can serve many regions at any time. The logical view of a table is actually a set of regions hosted by many region servers.

The default split policy for HBase 0.94 and trunk is IncreasingToUpperBoundRegionSplitPolicy, which does more aggressive splitting based on the number of regions hosted in the same region server. The split policy uses the max store file size based on Min (R^2 * “hbase.hregion.memstore.flush.size”, “hbase.hregion.max.filesize”), where R is the number of regions of the same table hosted on the same regionserver. So for example, with the default memstore flush size of 128MB and the default max store size of 10GB, the first region on the region server will be split just after the first flush at 128MB. As number of regions hosted in the region server increases, it will use increasing split sizes: 512MB, 1152MB, 2GB, 3.2GB, 4.6GB, 6.2GB, etc. After reaching 9 regions, the split size will go beyond the configured “hbase.hregion.max.filesize”, at which point, 10GB split size will be used from then on. For both of these algorithms, regardless of when splitting occurs, the split point used is the rowkey that corresponds to the mid point in the “block index” for the largest store file in the largest store.

  The above text has been taken from Chapter 1 – Introduction, section – Building Blocks of “HBase The Definitive Guide” book and “HortonWorks Blog “.

Hope This Helps

Sunil S Ranka

“Superior BI is the antidote to Business Failure

Posted in Uncategorized | Tagged: , , , , , , , | Leave a Comment »

HDFS Free Space Command

Posted by sranka on March 17, 2014

Hi All

With increasing data  volume , in HDFS space could be continued challenge. While running into some space related issue, following command came very handy, hence thought of sharing with extended virtual community.

At times it gets challenging to know how much of actual space a directory or a file is using.  Having a command which can give you human readable format of size is always useful.  Below command shows how to get actual human readable file size on HDFS

hdfs dfs -du -h /

241.3 G  /app
9.8 G    /benchmarks
309.6 G  /hbase
0        /system
59.6 G   /tmp
20.0 G   /user
[sranka@devHadoopSrvr06 ~]$

 

hadoop dfsadmin -report

Post running the command, below is the result, it takes all the nodes in the cluster and gives the detail break-up based on the space availability and spaces used.


Configured Capacity: 13965170479105 (12.70 TB)
Present Capacity: 4208469598208 (3.83 TB)
DFS Remaining: 2120881930240 (1.93 TB)
DFS Used: 2087587667968 (1.90 TB)
DFS Used%: 49.60%
Under replicated blocks: 0
Blocks with corrupt replicas: 0
Missing blocks: 0

-------------------------------------------------
Datanodes available: 5 (5 total, 0 dead)

Live datanodes:
Name: 160.33.148.202:50010 (devHadoopSrvr08.ps.am.mycompany.com)
Hostname: devHadoopSrvr08.ps.am.mycompany.com
Rack: /default
Decommission Status : Normal
Configured Capacity: 2793034095821 (2.54 TB)
DFS Used: 381953257472 (355.72 GB)
Non DFS Used: 1986904386765 (1.81 TB)
DFS Remaining: 424176451584 (395.05 GB)
DFS Used%: 13.68%
DFS Remaining%: 15.19%
Last contact: Mon Mar 17 12:43:05 PDT 2014

Name: 160.33.148.204:50010 (devHadoopSrvr10.ps.am.mycompany.com)
Hostname: devHadoopSrvr10.ps.am.mycompany.com
Rack: /default
Decommission Status : Normal
Configured Capacity: 2793034095821 (2.54 TB)
DFS Used: 402465816576 (374.83 GB)
Non DFS Used: 1966391827661 (1.79 TB)
DFS Remaining: 424176451584 (395.05 GB)
DFS Used%: 14.41%
DFS Remaining%: 15.19%
Last contact: Mon Mar 17 12:43:05 PDT 2014

Name: 160.33.148.203:50010 (devHadoopSrvr09.ps.am.mycompany.com)
Hostname: devHadoopSrvr09.ps.am.mycompany.com
Rack: /default
Decommission Status : Normal
Configured Capacity: 2793034095821 (2.54 TB)
DFS Used: 391020421120 (364.17 GB)
Non DFS Used: 1977837223117 (1.80 TB)
DFS Remaining: 424176451584 (395.05 GB)
DFS Used%: 14.00%
DFS Remaining%: 15.19%
Last contact: Mon Mar 17 12:43:06 PDT 2014

Name: 160.33.148.201:50010 (devHadoopSrvr07.ps.am.mycompany.com)
Hostname: devHadoopSrvr07.ps.am.mycompany.com
Rack: /default
Decommission Status : Normal
Configured Capacity: 2793034095821 (2.54 TB)
DFS Used: 389182472192 (362.45 GB)
Non DFS Used: 1979675172045 (1.80 TB)
DFS Remaining: 424176451584 (395.05 GB)
DFS Used%: 13.93%
DFS Remaining%: 15.19%
Last contact: Mon Mar 17 12:43:04 PDT 2014

Name: 160.33.148.59:50010 (devHadoopSrvr06.ps.am.mycompany.com)
Hostname: devHadoopSrvr06.ps.am.mycompany.com
Rack: /default
Decommission Status : Normal
Configured Capacity: 2793034095821 (2.54 TB)
DFS Used: 522965700608 (487.05 GB)
Non DFS Used: 1845892140237 (1.68 TB)
DFS Remaining: 424176254976 (395.04 GB)
DFS Used%: 18.72%
DFS Remaining%: 15.19%
Last contact: Mon Mar 17 12:43:05 PDT 2014

Hope This Helps

Sunil S Ranka

“Superior BI is the antidote to Business Failure”

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