Sunday 30 December 2012

SSRS Reporting for months

In SSRS,

For reporting ,formatting can b changed for a text box by right clicking a text box and going to format.Go to 3 ellipses near format code and choose currency to display $ before costs.

By default ,if you generate monthly reports, it will generate report in alphabetical order eg april,august,december like that.To order it in proper format,right click the text box
go to properties turn on the interactive sort option.
Now right click again and go to edit group,In sorting give the column datepart(mm,date)  and display would be datename(mm,date) .



Posted by at No comments:

Tuesday 18 December 2012

Shared network drive and SSIS

When we copy a big file from a server to a remote location (NAS drive or something) through ssis,there comes a network connection unavailable error.
To fix this go to my computer and map a network drive .Add that remote drive.Now the job willl execute properly through SSIS.

Executing a package is easy but running a job from sql server gives error.To resolve that
Use the UNC path when specifying the destination-- the SQL Agent doesn't have a concept of "mapped" "drives".
Also, SQL Agent typically runs as "Local Service" or "Local System" and, as such, doesn't have rights to remote shares on other computers.
You have a couple of choices:
  • Run SQL Agent as a role account in the domain. Grant that account permission to write to the directory / share where you'd like the backups stored.
  • Run SQL Agent as "Network Service". It will authenticate to the sharing server with the domain computer account of the machine the service is running on. Grant that account permission to write to the directory / share where you'd like the backup stored.
  • If you don't have a domain, create an account with the same username and password on both the machine hosting SQL Agent and the machine hosting the backup files. Change SQL Agent to run as this "role" account, and grant that account permission to write to the directory / share where you'd like the backup stored. (The "poor man's domain"...)
  •  
  •  
  •  or else create a batch file pkgexec.bat having "C:\Program Files (x86)\Microsoft SQL Server\100\DTS\Binn\dtexec.exe" /file c:\SceneVisitReports.dtsx  and schedule in windows task manager c:\>pkgexec.bat
Posted by at No comments:

Monday 17 December 2012

How to copy reports from sql server to a remote disk drive

For SSIS package to work with remote disk drive go to my computer and map a network drive to a local drive

Open a new notepad and write a command
copy c:\*.txt \\kp\nas\report and save it as "filename.bat"

In SSIS package
execute process task ,put this bat file in executables and run it.


Posted by at No comments:

The Excel Connection Manager is not supported in the 64-bit version of SSIS

The Excel Connection Manager is not supported in the 64-bit version of SSIS


When i am using SSIS Excel connection manager in my 64bit dev environment it is giving me the following error..
[Connection manager "Excel Connection Manager"] Error: SSIS Error Code DTS_E_OLEDB_EXCEL_NOT_SUPPORTED: The Excel Connection Manager is not supported in the 64-bit version of SSIS, as no OLE DB provider is available.
To fix this:
Go to Project –> Project Properties –> then Set Run64BitRuntime = False.
thats it…
Posted by at No comments:

Tuesday 11 December 2012

Increasing java heap space

While running jasper reports i got error of insufficient java heap space.
Here is the solution for same.

In linux systems,set this

export JVM_ARGS="-Xms1024m -Xmx1024m"

In windows

Running Java applications in computers takes some memory during the process which is known as Java memory (Java heap). Frequently, it is necessary to increase that heap to prevent throttling the performance of the application.
  1. Go to Control Panel. Click on "Start" button. Then click on "Control Panel."

  2. 2
    Find Programs
    Find Programs
    Select Programs. In the left side of Control Panel click on "Programs." Pleas click on the "Programs" written with green color, not the "Uninstall a program," which is in blue color.
  3. 3
    Find Java
    Find Java
    Go to Java settings. In the next dialog click on "Java," usually at the bottom of the other Programs; "Java Control Panel" dialog pop-up opens.
  4. 4
    Find Java Tab
    Find Java Tab
    Select "Java" tab. Inside the Java tab, click on "View" button. It opens the "Java Runtime Environment Settings"
  5. 5
    View Java Runtime Environment
    View Java Runtime Environment
    Change amount of heap. In the "Runtime Parameters" column change the value, or if it is blank decide for the new value, of the Java memory.
  6. 6
    Modify Runtime Parameter
    Modify Runtime Parameter
    Modify the parameter. To modify the parameter, double click in the "Runtime Parameters" column and,
    • type -Xmx512m that assigns 512MB memory for the Java.
    • type -Xmx1024m that assigns 1GB memory for the Java.
    • type -Xmx2048m that assigns 2GB memory for the Java.
    • type -Xmx3072m that assigns 3GB memory for the Java, and so on.
    • Please note, it begins with a minus sign and ends to an m.
    • Also note, there is no blank space between characters.
  7. 7
    Close the dialogue box. Click on "OK" button on the "Java Runtime Environment Settings" to close it.
  8. 8
    Close Java dialogue box. "Apply" button in the "Java Control Panel" has been enabled now. You should click on "Apply" to finalise the new Java memory. Then click on the "OK" button.
  9. 9
    Close the Windows 7 Control Panel.
Posted by at No comments:

Monday 10 December 2012

More about HDFS

HDFS Permissions and Security

Starting with Hadoop 0.16.1, HDFS has included a rudimentary file permissions system. This permission system is based on the POSIX model, but does not provide strong security for HDFS files. The HDFS permissions system is designed to prevent accidental corruption of data or casual misuse of information within a group of users who share access to a cluster. It is not a strong security model that guarantees denial of access to unauthorized parties.
HDFS security is based on the POSIX model of users and groups. Each file or directory has 3 permissions (read, write and execute) associated with it at three different granularities: the file's owner, users in the same group as the owner, and all other users in the system. As the HDFS does not provide the full POSIX spectrum of activity, some combinations of bits will be meaningless. For example, no file can be executed; the +x bits cannot be set on files (only directories). Nor can an existing file be written to, although the +w bits may still be set.
Security permissions and ownership can be modified using the bin/hadoop dfs -chmod, -chown, and -chgrp operations described earlier in this document; they work in a similar fashion to the POSIX/Linux tools of the same name.
Determining identity - Identity is not authenticated formally with HDFS; it is taken from an extrinsic source. The Hadoop system is programmed to use the user's current login as their Hadoop username (i.e., the equivalent of whoami). The user's current working group list (i.e, the output of groups) is used as the group list in Hadoop. HDFS itself does not verify that this username is genuine to the actual operator.
Superuser status - The username which was used to start the Hadoop process (i.e., the username who actually ran bin/start-all.sh or bin/start-dfs.sh) is acknowledged to be the superuser for HDFS. If this user interacts with HDFS, he does so with a special username superuser. This user's operations on HDFS never fail, regardless of permission bits set on the particular files he manipulates. If Hadoop is shutdown and restarted under a different username, that username is then bound to the superuser account.
Supergroup - There is also a special group named supergroup, whose membership is controlled by the configuration parameter dfs.permissions.supergroup.
Disabling permissions - By default, permissions are enabled on HDFS. The permission system can be disabled by setting the configuration option dfs.permissions to false. The owner, group, and permissions bits associated with each file and directory will still be preserved, but the HDFS process does not enforce them, except when using permissions-related operations such as -chmod.

Additional HDFS Tasks

Rebalancing Blocks

New nodes can be added to a cluster in a straightforward manner. On the new node, the same Hadoop version and configuration (conf/hadoop-site.xml) as on the rest of the cluster should be installed. Starting the DataNode daemon on the machine will cause it to contact the NameNode and join the cluster. (The new node should be added to the slaves file on the master server as well, to inform the master how to invoke script-based commands on the new node.)
But the new DataNode will have no data on board initially; it is therefore not alleviating space concerns on the existing nodes. New files will be stored on the new DataNode in addition to the existing ones, but for optimum usage, storage should be evenly balanced across all nodes.
This can be achieved with the automatic balancer tool included with Hadoop. The Balancer class will intelligently balance blocks across the nodes to achieve an even distribution of blocks within a given threshold, expressed as a percentage. (The default is 10%.) Smaller percentages make nodes more evenly balanced, but may require more time to achieve this state. Perfect balancing (0%) is unlikely to actually be achieved.
The balancer script can be run by starting bin/start-balancer.sh in the Hadoop directory. The script can be provided a balancing threshold percentage with the -threshold parameter; e.g., bin/start-balancer.sh -threshold 5. The balancer will automatically terminate when it achieves its goal, or when an error occurs, or it cannot find more candidate blocks to move to achieve better balance. The balancer can always be terminated safely by the administrator by running bin/stop-balancer.sh.
The balancing script can be run either when nobody else is using the cluster (e.g., overnight), but can also be run in an "online" fashion while many other jobs are on-going. To prevent the rebalancing process from consuming large amounts of bandwidth and significantly degrading the performance of other processes on the cluster, the dfs.balance.bandwidthPerSec configuration parameter can be used to limit the number of bytes/sec each node may devote to rebalancing its data store.

Copying Large Sets of Files

When migrating a large number of files from one location to another (either from one HDFS cluster to another, from S3 into HDFS or vice versa, etc), the task should be divided between multiple nodes to allow them all to share in the bandwidth required for the process. Hadoop includes a tool called distcp for this purpose.
By invoking bin/hadoop distcp src dest, Hadoop will start a MapReduce task to distribute the burden of copying a large number of files from src to dest. These two parameters may specify a full URL for the the path to copy. e.g., "hdfs://SomeNameNode:9000/foo/bar/" and "hdfs://OtherNameNode:2000/baz/quux/" will copy the children of /foo/bar on one cluster to the directory tree rooted at /baz/quux on the other. The paths are assumed to be directories, and are copied recursively. S3 URLs can be specified with s3://bucket-name/key.

Decommissioning Nodes

In addition to allowing nodes to be added to the cluster on the fly, nodes can also be removed from a cluster while it is running, without data loss. But if nodes are simply shut down "hard," data loss may occur as they may hold the sole copy of one or more file blocks.
Nodes must be retired on a schedule that allows HDFS to ensure that no blocks are entirely replicated within the to-be-retired set of DataNodes.
HDFS provides a decommissioning feature which ensures that this process is performed safely. To use it, follow the steps below:
Step 1: Cluster configuration. If it is assumed that nodes may be retired in your cluster, then before it is started, an excludes file must be configured. Add a key named dfs.hosts.exclude to your conf/hadoop-site.xml file. The value associated with this key provides the full path to a file on the NameNode's local file system which contains a list of machines which are not permitted to connect to HDFS.
Step 2: Determine hosts to decommission. Each machine to be decommissioned should be added to the file identified by dfs.hosts.exclude, one per line. This will prevent them from connecting to the NameNode.
Step 3: Force configuration reload. Run the command bin/hadoop dfsadmin -refreshNodes. This will force the NameNode to reread its configuration, including the newly-updated excludes file. It will decommission the nodes over a period of time, allowing time for each node's blocks to be replicated onto machines which are scheduled to remain active.
Step 4: Shutdown nodes. After the decommission process has completed, the decommissioned hardware can be safely shutdown for maintenance, etc. The bin/hadoop dfsadmin -report command will describe which nodes are connected to the cluster.
Step 5: Edit excludes file again. Once the machines have been decommissioned, they can be removed from the excludes file. Running bin/hadoop dfsadmin -refreshNodes again will read the excludes file back into the NameNode, allowing the DataNodes to rejoin the cluster after maintenance has been completed, or additional capacity is needed in the cluster again, etc.

Verifying File System Health

After decommissioning nodes, restarting a cluster, or periodically during its lifetime, you may want to ensure that the file system is healthy--that files are not corrupted or under-replicated, and that blocks are not missing.
Hadoop provides an fsck command to do exactly this. It can be launched at the command line like so: 
  bin/hadoop fsck [path] [options]
If run with no arguments, it will print usage information and exit. If run with the argument /, it will check the health of the entire file system and print a report. If provided with a path to a particular directory or file, it will only check files under that path. If an option argument is given but no path, it will start from the file system root (/). The options may include two different types of options:
Action options specify what action should be taken when corrupted files are found. This can be -move, which moves corrupt files to /lost+found, or -delete, which deletes corrupted files.
Information options specify how verbose the tool should be in its report. The -files option will list all files it checks as it encounters them. This information can be further expanded by adding the -blocks option, which prints the list of blocks for each file. Adding -locations to these two options will then print the addresses of the DataNodes holding these blocks. Still more information can be retrieved by adding -racks to the end of this list, which then prints the rack topology information for each location. (See the next subsection for more information on configuring network rack awareness.) Note that the later options do not imply the former; you must use them in conjunction with one another. Also, note that the Hadoop program uses -files in a "common argument parser" shared by the different commands such as dfsadmin, fsck, dfs, etc. This means that if you omit a path argument to fsck, it will not receive the -files option that you intend. You can separate common options from fsck-specific options by using -- as an argument, like so:
  bin/hadoop fsck -- -files -blocks
The -- is not required if you provide a path to start the check from, or if you specify another argument first such as -move.
By default, fsck will not operate on files still open for write by another client. A list of such files can be produced with the -openforwrite option.
Posted by at 1 comment:

Sunday 9 December 2012

Some tips for big data cluster

To disable the iptables in linux box

service iptables status
service iptables save
service iptables stop
chkconfig iptables off
service network restart

To leave the safe mode
hadoop dfsadmin -safemode leave

To stop a daemon of hbase
hbase-daemon.sh stop master

To kill a job
hadoop job -kill job_201209271339_0006

install java jdk rpm

rpm -Uvh jdk1.6.0_33-linux-i586.rpm

Uninstall java
rpm -qa ! grep jdk
rpm -e jdk1.6.0_33-fcs

To view task log url ,replace taskid by attemptid in the log url.

Common errors
UserPriviledgedAction:Give chown rights to hduser
session 0*0 for server null :network issue
Clock sync error:set same time in master n all slaves to synchronise the clocks
Unable to read additional data from clientsessionid:Error comes when slaves are removed and data is not replicated properly.Add the slaves back to recover data.

Create Auxlib in hive and copy these jar files in that
Zookeeper
hive contrib
hbase jar
hbase hive jar
guava jar


For talend opensource many jar files are missing and they can be taken from talend data integration tool.

For talend to connect to hive hive-site.xml and hive-defaullt.xml should be on classpath.For talend or jaspersoft to work,start the thrift server using command

hive --service hiveserver

Add these lines in bashrc to set the classpath

for i in hivehome/lib/*.jar;do
classpath=$classpath:$i
done
classpath=hadoophome/hadoopcore jar
classpath=hivehome/conf

In talend mysql java connector was missing and it requires 5.0 version only to be added to plugins.Go to modules add it and refresh.

HBase connection to talend was noot happening coz localhost could not b resolved.Go to c:/windows/system32/drivers/etc/hosts and add these lines

127.0.0.1 localhost
IP  master
IP1 slave1
IP2 slave2

similarly add hosts entry of ur system in master and slaves

hbase connection is case sensitive and will throw null ptr exception if case is not taken into consideration.

namenode goes for safe mode even before jobtracker so add
dfs.namenode.threshold.percent=0 so that namenode doesnt go for safe mode.

while writing a hive load statement use fields terminated by clause to avoid adding equal no. of null columns.

copying data from eventlog to hbase hive integration creates reducer size to zero as some rowkeys comes a s null.

delete temp files to remove any data that might cause hindrance in starting the cluster.

To ssh without using password do this in root
$ chmod go-w $HOME $HOME/.ssh
$ chmod 600 $HOME/.ssh/authorized_keys

and go to hduser
$ chown `whoami` $HOME/.ssh/authorized_keys

change machine name to slave1,slave2 by
echo slave1 > /proc/sys/kernel/hostname


The most common problem that causes public keys to fail are permissions in the $HOME directory. Your $HOME directory cannot be writable by any user except the owner. Additionally, the .ssh directory and the authorized_keys file cannot be writable except by the owner. The ssh protocol will not report the problem but will silently ignore the authorized_keys file if any permissions are wrong.
To fix the destination public key handshake, you can do this (logged in as the remote user):
    chmod 755 $HOME $HOME/.ssh
    chmod 600 $HOME/.ssh/*

Alternatively, you can just remove the write capability with:
chmod go-w $HOME $HOME/.ssh
chmod go-w $HOME/.ssh/*
Also, the $HOME and $HOME/.ssh directories must be owned by the user and all the files in .ssh owned by the user. A common error is to create the .ssh directory and files as root and forget to assign the proper permissions and ownership. A better way is to login as the user, then run ssh-keygen -t to create not only the ssh keys but the .ssh directory with correct permissions and ownership.
Posted by at 10 comments:

Tuesday 4 December 2012

Reporting on hadoop

Talend - an open source provider of tools for managing Big data, provides a tool called Talend Open Studio for Big Data. Its a GUI based data integration tool like SSIS. Behind the scenes this tool generates code for Hadoop Distributed File System (HDFS), Pig, Hbase, Sqoop and Hive. This kind of tools really take Hadoop and Big Data to a extremely wide user-base.

After you have the ways to build a high-way to a mountain of data-source, the immediate need is to make meaning of these data. One of the front-runners of data visualization and analytics, Tableau, provides way to create ad-hoc visualizations from extracts of data from Hadoop clusters or straight live from the Hadoop clusters. Creating visualization from in-memory data and staging extract of data from Hadoop clusters into relational databases and creating visualizations from the same; both are facilitated by Tableau.
Other analytics vendor like Snaplogic and Pentaho also provides tools for operating with Hadoop clusters, which does not require developers to write code. Microsoft has an integrated platform for integration, reporting and analytics (in-memory/olap) and an IDE like SSDS (formerly BIDS).
If tools similar to Talend and Tableau are integrated into SSIS, SSAS, SSRS, DB Engine and SSDT, then Microsoft is one of the best positioned leaders to take Hadoop to a wide audience in their main-stream business. When platforms like Azure Data Market, Data Quality Services, Master Data Management, StreamInsight, Sharepoint etc join hands with tool and technology support integrated with SQL Sever, it would be an unmatched way to extract intelligence out of Hadoop. Connectors for Hadoop has been the first baby step towards this area. Still lot of maturity in this area is awaited.
Till then look out for existing leaders in this area like Cloudera, MapR, Hortonworks, Apache and GreenPlum for Hadoop distributions and implementation. And for Hadoop tools, software vendors like Talend, Tableau, SnapLogic and Pentaho can provide the required toolset.
Posted by at No comments:

Wednesday 28 November 2012

hive udf

Relational Operators

The following operators compare the passed operands and generate a TRUE or FALSE value depending on whether the comparison between the operands holds.
Operator Operand types Description
A = B All primitive types TRUE if expression A is equal to expression B otherwise FALSE
A <=> B All primitive types Returns same result with EQUAL(=) operator for non-null operands, but returns TRUE if both are NULL, FALSE if one of the them is NULL (as of version 0.9.0)
A == B None! Fails because of invalid syntax. SQL uses =, not ==
A <> B All primitive types NULL if A or B is NULL, TRUE if expression A is NOT equal to expression B otherwise FALSE
A < B All primitive types NULL if A or B is NULL, TRUE if expression A is less than expression B otherwise FALSE
A <= B All primitive types NULL if A or B is NULL, TRUE if expression A is less than or equal to expression B otherwise FALSE
A > B All primitive types NULL if A or B is NULL, TRUE if expression A is greater than expression B otherwise FALSE
A >= B All primitive types NULL if A or B is NULL, TRUE if expression A is greater than or equal to expression B otherwise FALSE
A [NOT] BETWEEN B AND C All primitive types NULL if A, B or C is NULL, TRUE if A is greater than or equal to B AND A less than or equal to C otherwise FALSE. This can be inverted by using the NOT keyword. (as of version 0.9.0)
A IS NULL all types TRUE if expression A evaluates to NULL otherwise FALSE
A IS NOT NULL All types FALSE if expression A evaluates to NULL otherwise TRUE
A LIKE B strings NULL if A or B is NULL, TRUE if string A matches the SQL simple regular expression B, otherwise FALSE. The comparison is done character by character. The _ character in B matches any character in A(similar to . in posix regular expressions) while the % character in B matches an arbitrary number of characters in A(similar to .* in posix regular expressions) e.g. 'foobar' like 'foo' evaluates to FALSE where as 'foobar' like 'foo_ _ _' evaluates to TRUE and so does 'foobar' like 'foo%'
A RLIKE B strings NULL if A or B is NULL, TRUE if string A matches the Java regular expression B(See Java regular expressions syntax), otherwise FALSE e.g. 'foobar' rlike 'foo' evaluates to FALSE where as 'foobar' rlike '^f.*r$' evaluates to TRUE
A REGEXP B strings Same as RLIKE

Arithmetic Operators

The following operators support various common arithmetic operations on the operands. All return number types; if any of the operands are NULL, then the result is also NULL.
Operator Operand types Description
A + B All number types Gives the result of adding A and B. The type of the result is the same as the common parent(in the type hierarchy) of the types of the operands. e.g. since every integer is a float, therefore float is a containing type of integer so the + operator on a float and an int will result in a float.
A - B All number types Gives the result of subtracting B from A. The type of the result is the same as the common parent(in the type hierarchy) of the types of the operands.
A * B All number types Gives the result of multiplying A and B. The type of the result is the same as the common parent(in the type hierarchy) of the types of the operands. Note that if the multiplication causing overflow, you will have to cast one of the operators to a type higher in the type hierarchy.
A / B All number types Gives the result of dividing B from A. The result is a double type.
A % B All number types Gives the reminder resulting from dividing A by B. The type of the result is the same as the common parent(in the type hierarchy) of the types of the operands.
A & B All number types Gives the result of bitwise AND of A and B. The type of the result is the same as the common parent(in the type hierarchy) of the types of the operands.
A | B All number types Gives the result of bitwise OR of A and B. The type of the result is the same as the common parent(in the type hierarchy) of the types of the operands.
A ^ B All number types Gives the result of bitwise XOR of A and B. The type of the result is the same as the common parent(in the type hierarchy) of the types of the operands.
~A All number types Gives the result of bitwise NOT of A. The type of the result is the same as the type of A.

Logical Operators

The following operators provide support for creating logical expressions. All of them return boolean TRUE, FALSE, or NULL depending upon the boolean values of the operands. NULL behaves as an "unknown" flag, so if the result depends on the state of an unknown, the result itself is unknown.
Operator Operand types Description
A AND B boolean TRUE if both A and B are TRUE, otherwise FALSE. NULL if A or B is NULL
A && B boolean Same as A AND B
A OR B boolean TRUE if either A or B or both are TRUE; FALSE OR NULL is NULL; otherwise FALSE
A || B boolean Same as A OR B
NOT A boolean TRUE if A is FALSE or NULL if A is NULL. Otherwise FALSE.
! A boolean Same as NOT A

Complex Type Constructors

The following functions construct instances of complex types.
Constructor Function Operands Description
map (key1, value1, key2, value2, ...) Creates a map with the given key/value pairs
struct (val1, val2, val3, ...) Creates a struct with the given field values. Struct field names will be col1, col2, ...
named_struct (name1, val1, name2, val2, ...) Creates a struct with the given field names and values.
array (val1, val2, ...) Creates an array with the given elements
create_union (tag, val1, val2, ...) Creates a union type with the value that is being pointed to by the tag parameter

Operators on Complex Types

The following operators provide mechanisms to access elements in Complex Types
Operator Operand types Description
A[n] A is an Array and n is an int Returns the nth element in the array A. The first element has index 0 e.g. if A is an array comprising of ['foo', 'bar'] then A[0] returns 'foo' and A[1] returns 'bar'
M[key] M is a Map and key has type K Returns the value corresponding to the key in the map e.g. if M is a map comprising of {'f' -> 'foo', 'b' -> 'bar', 'all' -> 'foobar'} then M['all'] returns 'foobar'
S.x S is a struct Returns the x field of S. e.g for struct foobar {int foo, int bar} foobar.foo returns the integer stored in the foo field of the struct.

Built-in Functions

Mathematical Functions

The following built-in mathematical functions are supported in hive; most return NULL when the argument(s) are NULL:
Return Type Name(Signature) Description
BIGINT round(double a) Returns the rounded BIGINT value of the double
DOUBLE round(double a, int d) Returns the double rounded to d decimal places
BIGINT floor(double a) Returns the maximum BIGINT value that is equal or less than the double
BIGINT ceil(double a), ceiling(double a) Returns the minimum BIGINT value that is equal or greater than the double
double rand(), rand(int seed) Returns a random number (that changes from row to row) that is distributed uniformly from 0 to 1. Specifiying the seed will make sure the generated random number sequence is deterministic.
double exp(double a) Returns ea where e is the base of the natural logarithm
double ln(double a) Returns the natural logarithm of the argument
double log10(double a) Returns the base-10 logarithm of the argument
double log2(double a) Returns the base-2 logarithm of the argument
double log(double base, double a) Return the base "base" logarithm of the argument
double pow(double a, double p) power(double a, double p) Return ap
double sqrt(double a) Returns the square root of a
string bin(BIGINT a) Returns the number in binary format (see [http://dev.mysql.com/doc/refman/5.0/en/string-functions.html#function_bin])
string hex(BIGINT a) hex(string a) If the argument is an int, hex returns the number as a string in hex format. Otherwise if the number is a string, it converts each character into its hex representation and returns the resulting string. (see [http://dev.mysql.com/doc/refman/5.0/en/string-functions.html#function_hex])
string unhex(string a) Inverse of hex. Interprets each pair of characters as a hexidecimal number and converts to the character represented by the number.
string conv(BIGINT num, int from_base, int to_base) Converts a number from a given base to another (see [http://dev.mysql.com/doc/refman/5.0/en/mathematical-functions.html#function_conv])
double abs(double a) Returns the absolute value
int double pmod(int a, int b) pmod(double a, double b) Returns the positive value of a mod b
double sin(double a) Returns the sine of a (a is in radians)
double asin(double a) Returns the arc sin of x if -1<=a<=1 or null otherwise
double cos(double a) Returns the cosine of a (a is in radians)
double acos(double a) Returns the arc cosine of x if -1<=a<=1 or null otherwise
double tan(double a) Returns the tangent of a (a is in radians)
double atan(double a) Returns the arctangent of a
double degrees(double a) Converts value of a from radians to degrees
double radians(double a) Converts value of a from degrees to radians
int double positive(int a) positive(double a) Returns a
int double negative(int a) negative(double a) Returns -a
float sign(double a) Returns the sign of a as '1.0' or '-1.0'
double e() Returns the value of e
double pi() Returns the value of pi

Collection Functions

The following built-in collection functions are supported in hive:
Return Type Name(Signature) Description
int size(Map) Returns the number of elements in the map type
int size(Array) Returns the number of elements in the array type
array map_keys(Map) Returns an unordered array containing the keys of the input map
array map_values(Map) Returns an unordered array containing the values of the input map
boolean array_contains(Array, value) Returns TRUE if the array contains value
array sort_array(Array) Sorts the input array in ascending order according to the natural ordering of the array elements and returns it (as of version 0.9.0)

Type Conversion Functions

The following type conversion functions are supported in hive:
Return Type Name(Signature) Description
binary binary(string|binary) Casts the parameter into a binary
Expected "=" to follow "type" cast(expr as ) Converts the results of the expression expr to e.g. cast('1' as BIGINT) will convert the string '1' to it integral representation. A null is returned if the conversion does not succeed.

Date Functions

The following built-in date functions are supported in hive:
Return Type Name(Signature) Description
string from_unixtime(bigint unixtime[, string format]) Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string representing the timestamp of that moment in the current system time zone in the format of "1970-01-01 00:00:00"
bigint unix_timestamp() Gets current time stamp using the default time zone.
bigint unix_timestamp(string date) Converts time string in format yyyy-MM-dd HH:mm:ss to Unix time stamp, return 0 if fail: unix_timestamp('2009-03-20 11:30:01') = 1237573801
bigint unix_timestamp(string date, string pattern) Convert time string with given pattern (see [http://java.sun.com/j2se/1.4.2/docs/api/java/text/SimpleDateFormat.html]) to Unix time stamp, return 0 if fail: unix_timestamp('2009-03-20', 'yyyy-MM-dd') = 1237532400
string to_date(string timestamp) Returns the date part of a timestamp string: to_date("1970-01-01 00:00:00") = "1970-01-01"
int year(string date) Returns the year part of a date or a timestamp string: year("1970-01-01 00:00:00") = 1970, year("1970-01-01") = 1970
int month(string date) Returns the month part of a date or a timestamp string: month("1970-11-01 00:00:00") = 11, month("1970-11-01") = 11
int day(string date) dayofmonth(date) Return the day part of a date or a timestamp string: day("1970-11-01 00:00:00") = 1, day("1970-11-01") = 1
int hour(string date) Returns the hour of the timestamp: hour('2009-07-30 12:58:59') = 12, hour('12:58:59') = 12
int minute(string date) Returns the minute of the timestamp
int second(string date) Returns the second of the timestamp
int weekofyear(string date) Return the week number of a timestamp string: weekofyear("1970-11-01 00:00:00") = 44, weekofyear("1970-11-01") = 44
int datediff(string enddate, string startdate) Return the number of days from startdate to enddate: datediff('2009-03-01', '2009-02-27') = 2
string date_add(string startdate, int days) Add a number of days to startdate: date_add('2008-12-31', 1) = '2009-01-01'
string date_sub(string startdate, int days) Subtract a number of days to startdate: date_sub('2008-12-31', 1) = '2008-12-30'
timestamp from_utc_timestamp(timestamp, string timezone) Assumes given timestamp ist UTC and converts to given timezone (as of Hive 0.8.0)
timestamp to_utc_timestamp(timestamp, string timezone) Assumes given timestamp is in given timezone and converts to UTC (as of Hive 0.8.0)

Conditional Functions

Return Type Name(Signature) Description
T if(boolean testCondition, T valueTrue, T valueFalseOrNull) Return valueTrue when testCondition is true, returns valueFalseOrNull otherwise
T COALESCE(T v1, T v2, ...) Return the first v that is not NULL, or NULL if all v's are NULL
T CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END When a = b, returns c; when a = d, return e; else return f
T CASE WHEN a THEN b [WHEN c THEN d]* [ELSE e] END When a = true, returns b; when c = true, return d; else return e

String Functions

The following are built-in String functions are supported in hive:
Return Type Name(Signature) Description
int ascii(string str) Returns the numeric value of the first character of str
string concat(string|binary A, string|binary B...) Returns the string or bytes resulting from concatenating the strings or bytes passed in as parameters in order. e.g. concat('foo', 'bar') results in 'foobar'. Note that this function can take any number of input strings.
array> context_ngrams(array>, array, int K, int pf) Returns the top-k contextual N-grams from a set of tokenized sentences, given a string of "context". See StatisticsAndDataMining for more information.
string concat_ws(string SEP, string A, string B...) Like concat() above, but with custom separator SEP.
string concat_ws(string SEP, array) Like concat_ws() above, but taking an array of strings. (as of Hive 0.9.0)
int find_in_set(string str, string strList) Returns the first occurance of str in strList where strList is a comma-delimited string. Returns null if either argument is null. Returns 0 if the first argument contains any commas. e.g. find_in_set('ab', 'abc,b,ab,c,def') returns 3
string format_number(number x, int d) Formats the number X to a format like '#,###,###.##', rounded to D decimal places, and returns the result as a string. If D is 0, the result has no decimal point or fractional part. (as of Hive 0.10.0)
string get_json_object(string json_string, string path) Extract json object from a json string based on json path specified, and return json string of the extracted json object. It will return null if the input json string is invalid. NOTE: The json path can only have the characters [0-9a-z_], i.e., no upper-case or special characters. Also, the keys *cannot start with numbers.* This is due to restrictions on Hive column names.
boolean in_file(string str, string filename) Returns true if the string str appears as an entire line in filename.
int instr(string str, string substr) Returns the position of the first occurence of substr in str
int length(string A) Returns the length of the string
int locate(string substr, string str[, int pos]) Returns the position of the first occurrence of substr in str after position pos
string lower(string A) lcase(string A) Returns the string resulting from converting all characters of B to lower case e.g. lower('fOoBaR') results in 'foobar'
string lpad(string str, int len, string pad) Returns str, left-padded with pad to a length of len
string ltrim(string A) Returns the string resulting from trimming spaces from the beginning(left hand side) of A e.g. ltrim(' foobar ') results in 'foobar '
array> ngrams(array>, int N, int K, int pf) Returns the top-k N-grams from a set of tokenized sentences, such as those returned by the sentences() UDAF. See StatisticsAndDataMining for more information.
string parse_url(string urlString, string partToExtract [, string keyToExtract]) Returns the specified part from the URL. Valid values for partToExtract include HOST, PATH, QUERY, REF, PROTOCOL, AUTHORITY, FILE, and USERINFO. e.g. parse_url('http://facebook.com/path1/p.php?k1=v1&k2=v2#Ref1', 'HOST') returns 'facebook.com'. Also a value of a particular key in QUERY can be extracted by providing the key as the third argument, e.g. parse_url('http://facebook.com/path1/p.php?k1=v1&k2=v2#Ref1', 'QUERY', 'k1') returns 'v1'.
string printf(String format, Obj... args) Returns the input formatted according do printf-style format strings (as of Hive 0.9.0)
string regexp_extract(string subject, string pattern, int index) Returns the string extracted using the pattern. e.g. regexp_extract('foothebar', 'foo(.*?)(bar)', 2) returns 'bar.' Note that some care is necessary in using predefined character classes: using '\s' as the second argument will match the letter s; '
s' is necessary to match whitespace, etc. The 'index' parameter is the Java regex Matcher group() method index. See docs/api/java/util/regex/Matcher.html for more information on the 'index' or Java regex group() method.
string regexp_replace(string INITIAL_STRING, string PATTERN, string REPLACEMENT) Returns the string resulting from replacing all substrings in INITIAL_STRING that match the java regular expression syntax defined in PATTERN with instances of REPLACEMENT, e.g. regexp_replace("foobar", "oo|ar", "") returns 'fb.' Note that some care is necessary in using predefined character classes: using '\s' as the second argument will match the letter s; '
s' is necessary to match whitespace, etc.
string repeat(string str, int n) Repeat str n times
string reverse(string A) Returns the reversed string
string rpad(string str, int len, string pad) Returns str, right-padded with pad to a length of len
string rtrim(string A) Returns the string resulting from trimming spaces from the end(right hand side) of A e.g. rtrim(' foobar ') results in ' foobar'
array> sentences(string str, string lang, string locale) Tokenizes a string of natural language text into words and sentences, where each sentence is broken at the appropriate sentence boundary and returned as an array of words. The 'lang' and 'locale' are optional arguments. e.g. sentences('Hello there! How are you?') returns ( ("Hello", "there"), ("How", "are", "you") )
string space(int n) Return a string of n spaces
array split(string str, string pat) Split str around pat (pat is a regular expression)
map str_to_map(text[, delimiter1, delimiter2]) Splits text into key-value pairs using two delimiters. Delimiter1 separates text into K-V pairs, and Delimiter2 splits each K-V pair. Default delimiters are ',' for delimiter1 and '=' for delimiter2.
string substr(string|binary A, int start) substring(string|binary A, int start) Returns the substring or slice of the byte array of A starting from start position till the end of string A e.g. substr('foobar', 4) results in 'bar' (see [http://dev.mysql.com/doc/refman/5.0/en/string-functions.html#function_substr])
string substr(string|binary A, int start, int len) substring(string|binary A, int start, int len) Returns the substring or slice of the byte array of A starting from start position with length len e.g. substr('foobar', 4, 1) results in 'b' (see [http://dev.mysql.com/doc/refman/5.0/en/string-functions.html#function_substr])
string translate(string input, string from, string to) Translates the input string by replacing the characters present in the from string with the corresponding characters in the to string. This is similar to the translate function in PostgreSQL. If any of the parameters to this UDF are NULL, the result is NULL as well (available as of Hive 0.10.0)
string trim(string A) Returns the string resulting from trimming spaces from both ends of A e.g. trim(' foobar ') results in 'foobar'
string upper(string A) ucase(string A) Returns the string resulting from converting all characters of A to upper case e.g. upper('fOoBaR') results in 'FOOBAR'

Misc. Functions

Return Type Name(Signature) Description
varies java_method(class, method[, arg1[, arg2..]]) Synonym for reflect (as of Hive 0.9.0)
varies reflect(class, method[, arg1[, arg2..]]) Use this UDF to call Java methods by matching the argument signature (uses reflection). (as of Hive 0.7.0)

xpath

Posted by at No comments:

Tuesday 27 November 2012

Pig Basics

Pig raises the level of abstraction for processing large datasets. MapReduce allows you, as the programmer, to specify a map function followed by a reduce function, but working out how to fit your data processing into this pattern, which often requires multiple MapReduce stages, can be a challenge. With Pig, the data structures are much richer, typically being multivalued and nested, and the set of transformations you can apply to the data are much more powerful.


Pig is made up of two pieces:
• The language used to express data flows, called Pig Latin.
• The execution environment to run Pig Latin programs. There are currently two environments: local execution in a single JVM and distributed execution on a Hadoop cluster.
A Pig Latin program is made up of a series of operations, or transformations, that are applied to the input data to produce output. Taken as a whole, the operations describe a data flow, which the Pig execution environment translates into an executable representation and then runs. Under the covers, Pig turns the transformations into a series of MapReduce jobs

Installing and Running Pig

Download latest version of Pig from the following link (Pig Installation).
$ tar xzf pig-0.7.0.tar.gz
set pig environment variables
$ export PIG_INSTALL=/home/user1/pig-0.7.0.tar.gz
$ export PATH=$PATH:$PIG_INSTALL/bin
You also need to set the JAVA_HOME environment variable to point to a suitable Java installation.

Pig has two execution types or modes: 

1) local mode : Pig runs in a single JVM and accesses the local filesystem. This mode is suitable only for small datasets.

$ pig -x local

grunt>
This starts Grunt, the Pig interactive shell

2) MapReduce mode : In MapReduce mode, Pig translates queries into MapReduce jobs and runs them on a Hadoop cluster. The cluster may be a pseudo- or fully distributed cluster.
set the HADOOP_HOME environment variable for finding which Hadoop client to run.

$ pig  or $ pig -x mapreduce , runs pig in MapReduce mode
Running Pig Programs
There are three ways of executing Pig programs, all of which work in both local and MapReduce mode
Script : Pig can run a script file that contains Pig commands. For example, pig
script.pig runs the commands in the local file script.pig
$ pig script.pig
Grunt : Grunt is an interactive shell for running Pig commands.It is also possible to run Pig scripts from within Grunt using run and exec.


Embedded :
You can run Pig programs from Java using the PigServer class, much like you can use JDBC to run SQL programs from Java.

PigPen is an Eclipse plug-in that provides an environment for developing Pig programs.

PigTools and EditorPlugins for pig can be downloaded from PigTools

Example of Pig in Interactive Mode (Grunt)

max_cgpa.pig


-- max_cgpa.pig: Finds the maximum cgpa of a user

records = LOAD 'pigsample.txt'
AS (name:chararray, spl:chararray, cgpa:float);
filtered_records = FILTER records BY cgpa > 0 AND cgpa < 10;
grouped_records = GROUP filtered_records BY spl;
max_cgpa = FOREACH grouped_records GENERATE group, MAX(filtered_records.cgpa);
STORE max_cgpa INTO 'output/cgpa_out';
Above pig script finds the maximum cgpa of a specialization.
pigsample.txt  ( Input to the pig )

raghu     ece     9
kumar    cse      8.5
biju       ece      8
mukul    cse      8.6
ashish   ece      7.0
subha    cse      8.3
ramu     ece     -8.3
rahul     cse      11.4
budania ece      5.4
first column represents name , second column specialization and third column is cgpa, by default each column is separated by tab space.

$ pig max_cgpa.pig
Output : 
(cse,8.6F)
(ece,9.0F)
Analysis : 
Statement : 1
records = LOAD 'pigsample.txt'AS (name:chararray, spl:chararray, cgpa:float);

Load input file in to memory from the file system (HDFS or local or Amazon S3). name:chararray notation describes the field’s
name and type; chararray is like a Java string, and an float is like a Java float.
grunt> DUMP records;
(raghu,ece,9.0F)
(kumar,cse,8.5F)
(biju,ece,8.0F)
(mukul,cse,8.6F)
(ashish,ece,7.0F)
(subha,cse,8.3F)
(ramu,ece,-8.3F)
(rahul,cse,11.4F)
(budania,ece,5.4F)
Input is converted in to a tuple , and each column is separated by ,
grunt> DESCRIBE records;
records: {name: chararray,spl: chararray,cgpa: float}
Statement : 2
filtered_records = FILTER records BY cgpa > 0 AND cgpa < 10;

grunt> DUMP filtered_records;
filter all the records whose cgpa <0 and="and" negative="negative">10 
(raghu,ece,9.0F)
(kumar,cse,8.5F)
(biju,ece,8.0F)
(mukul,cse,8.6F)
(ashish,ece,7.0F)
(subha,cse,8.3F)
(budania,ece,5.4F)
grunt> DESCRIBE filtered_records;
filtered_records: {name: chararray,spl: chararray,cgpa: float}
Statement : 3

The third statement uses the GROUP function to group the records relation by the specialization field.

grouped_records = GROUP filtered_records BY spl;
grunt> DUMP  grouped_records ;
(cse,{(kumar,cse,8.5F),(mukul,cse,8.6F),(subha,cse,8.3F)})
(ece,{(raghu,ece,9.0F),(biju,ece,8.0F),(ashish,ece,7.0F),(budania,ece,5.4F)})
grunt> DESCRIBE  grouped_records;
grouped_records: {group: chararray,filtered_records: {name: chararray,spl: chararray,cgpa: float}}
We now have two rows, or tuples, one for each specialization in the input data. The first field in each tuple is the field being grouped by (the specialization), and the second field is a bag of tuples
for that  specialization. A bag is just an unordered collection of tuples, which in Pig Latin is represented using curly braces.
By grouping the data in this way, we have created a row per  specialization , so now all that remains is to find the maximum cgpa for the tuples in each bag.

Statement : 4


max_cgpa = FOREACH grouped_records GENERATE group,
MAX(filtered_records.cgpa);
FOREACH processes every row to generate a derived set of rows, using a GENERATE clause to define the fields in each derived row. In this example, the first field is group, which is just the specialization. The second field is a little more complex.

The filtered_records.cgpa reference is to the cgpa field of the
filtered_records bag in the grouped_records relation. MAX is a built-in function for calculating the maximum value of fields in a bag. In this case, it calculates the maximum cgpa for the fields in each filtered_records bag.

grunt> DUMP    max_cgpa  ;
(cse,8.6F)
(ece,9.0F)
grunt> DESCRIBE    max_cgpa  ;
max_cgpa : {group: chararray,float}
Statement : 5

STORE max_cgpa INTO 'output/cgpa_out'

This command redirects the output of the script to a file (Local or HDFS) instead of printing the output on the console .
we’ve successfully calculated the maximum cgpa for each specialization.
With the ILLUSTRATE operator, Pig provides a tool for generating a reasonably complete and concise sample dataset.


--------------------------------------------------------------------
| records     | name: bytearray | spl: bytearray | cgpa: bytearray | 
--------------------------------------------------------------------
|             | kumar           | cse            | 8.5             | 
|             | mukul           | cse            | 8.6             | 
|             | ramu            | ece            | -8.3            | 
--------------------------------------------------------------------
----------------------------------------------------------------
| records     | name: chararray | spl: chararray | cgpa: float | 
----------------------------------------------------------------
|             | kumar           | cse            | 8.5         | 
|             | mukul           | cse            | 8.6         | 
|             | ramu            | ece            | -8.3        | 
----------------------------------------------------------------
-------------------------------------------------------------------------
| filtered_records     | name: chararray | spl: chararray | cgpa: float | 
-------------------------------------------------------------------------
|                      | kumar           | cse            | 8.5         | 
|                      | mukul           | cse            | 8.6         | 
-------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------
| grouped_records     | group: chararray | filtered_records: bag({name: chararray,spl: chararray,cgpa: float}) | 
----------------------------------------------------------------------------------------------------------------
|                     | cse              | {(kumar, cse, 8.5), (mukul, cse, 8.6)}                              | 
----------------------------------------------------------------------------------------------------------------
-------------------------------------------
|  max_cgpa   | group: chararray | float | 
-------------------------------------------
|              | cse              | 8.6   |
EXPLAIN max_cgpa  
Use the above command to see the logical and physical plans created by Pig.

Posted by at No comments:

Monday 26 November 2012

Delete a temp table in sql server if it already exists 

 
IF OBJECT_ID('tempdb..##tmp') IS NOT NULL 
begin
DROP TABLE ##tmp
end
CREATE TABLE ##tmp  
( TableName varchar(255),DifferenceInCounts int )
Posted by at No comments:

Thursday 15 November 2012

Date Functions in Hive


Date data types do not exist in Hive. In fact the dates are treated as strings in Hive. The date functions are listed below.

UNIX_TIMESTAMP()
This function returns the number of seconds from the Unix epoch (1970-01-01 00:00:00 UTC) using the default time zone.

UNIX_TIMESTAMP( string date )
This function converts the date in format 'yyyy-MM-dd HH:mm:ss' into Unix timestamp. This will return the number of seconds between the specified date and the Unix epoch. If it fails, then it returns 0.
Example: UNIX_TIMESTAMP('2000-01-01 00:00:00') returns 946713600

UNIX_TIMESTAMP( string date, string pattern )
This function converts the date to the specified date format and returns the number of seconds between the specified date and Unix epoch. If it fails, then it returns 0.
Example: UNIX_TIMESTAMP('2000-01-01 10:20:30','yyyy-MM-dd') returns 946713600

FROM_UNIXTIME( bigint number_of_seconds  [, string format] )
The FROM_UNIX function converts the specified number of seconds from Unix epoch and returns the date in the format 'yyyy-MM-dd HH:mm:ss'.
Example: FROM_UNIXTIME( UNIX_TIMESTAMP() ) returns the current date including the time. This is equivalent to the SYSDATE in oracle.

TO_DATE( string timestamp )
The TO_DATE function returns the date part of the timestamp in the format 'yyyy-MM-dd'.
Example: TO_DATE('2000-01-01 10:20:30') returns '2000-01-01'

YEAR( string date )
The YEAR function returns the year part of the date.
Example: YEAR('2000-01-01 10:20:30') returns 2000

MONTH( string date )
The MONTH function returns the month part of the date.
Example: YEAR('2000-03-01 10:20:30') returns 3

DAY( string date ), DAYOFMONTH( date )
The DAY or DAYOFMONTH function returns the day part of the date.
Example: DAY('2000-03-01 10:20:30') returns 1

HOUR( string date )
The HOUR function returns the hour part of the date.
Example: HOUR('2000-03-01 10:20:30') returns 10

MINUTE( string date )
The MINUTE function returns the minute part of the timestamp.
Example: MINUTE('2000-03-01 10:20:30') returns 20

SECOND( string date )
The SECOND function returns the second part of the timestamp.
Example: SECOND('2000-03-01 10:20:30') returns 30

WEEKOFYEAR( string date )
The WEEKOFYEAR function returns the week number of the date.
Example: WEEKOFYEAR('2000-03-01 10:20:30') returns 9

DATEDIFF( string date1, string date2 )
The DATEDIFF function returns the number of days between the two given dates.
Example: DATEDIFF('2000-03-01', '2000-01-10')  returns 51

DATE_ADD( string date, int days )
The DATE_ADD function adds the number of days to the specified date
Example: DATE_ADD('2000-03-01', 5) returns '2000-03-06'

DATE_SUB( string date, int days )
The DATE_SUB function subtracts the number of days to the specified date
Example: DATE_SUB('2000-03-01', 5) returns ‘2000-02-25’
Posted by at 1 comment:
Subscribe to: Posts (Atom)