Apache Sqoop

Apache Sqoop
Sqoop (“SQL-to-Hadoop”) is a tool designed to transfer data between Hadoop and relational databases. Sqoop allows to import data from a relational database management system (RDBMS) such as MySQL into the Hadoop Distributed File System (HDFS) for further processing using MapReduce program and then later export this processed data back into an RDBMS.

Sqoop is helpful in analysing certain behaviour (e.g. could be reading server logs) and wish to view the results of such analysis quite often. Triggering MR program would not be a feasible approach for a quick view of data (plus the fact that Hadoop systems are not good at quick reads and for smaller chunks).

So to overcome we can capture few data, import into HDFS, process it and export back to Hive or other data system for ad-hoc queries.

In this short tutorial, we will see how sqoop can be used to import data from the relational table to hdfs and vice versa.

Environment
Hadoop: hadoop-2.4.0.tar (Assuming the Hadoop is already installed)
Sqoop: sqoop-1.4.5.bin__hadoop-2.0.4-alpha.tar
(http://www.apache.org/dist/sqoop/1.4.5/)
MySQL JAR: mysql-connector-java-5.1.34.jar

Importing data from MySQL table to HDFS
Step 1: Creating database and table in mysql

mysql> create database sqoopdb;
mysql> use sqoopdb;
mysql> create table employee (name varchar(255), salary double(7,2));
mysql> insert into employee values (‘John’, 123456.66);
mysql> insert into employee values (‘Tim’,98544);

mysql> select * from employee;
+——+———-+
| name | salary |
+——+———-+
| John | 54887.00 |
| Tim | 98544.00 |
+——+———-+
2 rows in set (0.00 sec)

Step2: Copying ‘mysql-connector-java-5.1.34.jar’ to $SQOOP_HOME/lib directory

Note: Initially I have copied ‘mysql-connector-java-5.0.5.jar’, but due to this, I was getting the following exception (trimmed for verbosity):

“INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `employee` AS t LIMIT 1
ERROR manager.SqlManager: Error reading from database: java.sql.SQLException: Streaming result set com.mysql.jdbc.RowDataDynamic@4d3c7378 is still active. No statements may be issued when any streaming result sets are open and in use on a given connection. Ensure that you have called .close() on any active streaming result sets before attempting more queries. java.sql.SQLException: Streaming result set com.mysql.jdbc.RowDataDynamic@4d3c7378 is still active. No statements may be issued when any streaming result sets are open and in use on a given connection. Ensure that you have called .close() on any active streaming result sets before attempting more queries.”

“ERROR tool.ImportTool: Encountered IOException running import job: java.io.IOException: No columns to generate for ClassWriter”

Step3: Importing a table into HDFS
$ sqoop import –connect jdbc:mysql://localhost:3306/sqoopdb –username root –password root –table employee -m 1

INFO sqoop.Sqoop: Running Sqoop version: 1.4.5
WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
INFO tool.CodeGenTool: Beginning code generation
INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `employee` AS t LIMIT 1
INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `employee` AS t LIMIT 1
INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /usr/local/hadoop240
Note: /tmp/sqoop-hduser/compile/8b0c322a9f8c2420e9bbd2dd079dea4d/employee.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hduser/compile/8b0c322a9f8c2420e9bbd2dd079dea4d/employee.jar
WARN manager.MySQLManager: It looks like you are importing from mysql.
WARN manager.MySQLManager: This transfer can be faster! Use the –direct
WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
INFO mapreduce.ImportJobBase: Beginning import of employee
WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable
INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
INFO db.DBInputFormat: Using read commited transaction isolation
INFO mapreduce.JobSubmitter: number of splits:1

INFO mapreduce.Job: Job job_1427265867092_0001 running in uber mode : false
INFO mapreduce.Job: map 0% reduce 0%
INFO mapreduce.Job: map 100% reduce 0%
INFO mapreduce.Job: Job job_1427265867092_0001 completed successfully
INFO mapreduce.Job: Counters: 30

INFO mapreduce.ImportJobBase: Transferred 25 bytes in 47.9508 seconds (0.5214 bytes/sec)
INFO mapreduce.ImportJobBase: Retrieved 2 records.

Step4: Listing datafile content
$ hadoop dfs -ls -R employee
$ hadoop dfs -cat /user/hduser/employee/part-m-00000
John,54887.0
Tim,98544.0

Importing data from HDFS to table
Step1: Creating an empty table in mysql
mysql> use sqoopdb;
mysql> create table employee_export (name varchar(255), salary double(7,2));
Query OK, 0 rows affected (0.06 sec)

mysql> select * from employee_export;
Empty set (0.00 sec)

Step2: Creating directory and copy csv to HDFS
$ cat employee.csv
Jack,9878.21
Mark,8754.65

hadoop fs -mkdir -p /user/hduser/export
hadoop fs -copyFromLocal employee.csv /user/hduser/export/employee.csv

Step3: Running export command
$ sqoop export –connect jdbc:mysql://localhost/sqoopdb –username root –password root –table employee_export –export-dir ‘/user/hduser/export’ -m 1

15/03/25 18:55:23 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
15/03/25 18:55:25 INFO input.FileInputFormat: Total input paths to process : 1
15/03/25 18:55:25 INFO input.FileInputFormat: Total input paths to process : 1
15/03/25 18:55:25 INFO mapreduce.JobSubmitter: number of splits:1
15/03/25 18:55:25 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative
15/03/25 18:55:25 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1427265867092_0006
15/03/25 18:55:26 INFO impl.YarnClientImpl: Submitted application application_1427265867092_0006

15/03/25 18:55:34 INFO mapreduce.Job: map 0% reduce 0%
15/03/25 18:55:41 INFO mapreduce.Job: map 100% reduce 0%
15/03/25 18:55:41 INFO mapreduce.Job: Job job_1427265867092_0006 completed successfully
15/03/25 18:55:42 INFO mapreduce.Job: Counters: 30
15/03/25 18:55:42 INFO mapreduce.ExportJobBase: Transferred 163 bytes in 18.4814 seconds (8.8197 bytes/sec)
15/03/25 18:55:42 INFO mapreduce.ExportJobBase: Exported 2 records.
———————-
mysql> select * from sqoopdb.employee_export;
+——+———+
| name | salary |
+——+———+
| Jack | 9878.21 |
| Mark | 8754.65 |
+——+———+
2 rows in set (0.00 sec)

Apache Storm

What is Storm
Apache Storm is a distributed realtime computation system which process unbounded streams of data, doing for realtime processing. Storm is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate. Realtime analytics, online machine learning, continuous computation, distributed RPC, ETL are namely few primary use cases that can be addressed via Storm.

STORMs logical overview
At the highest level, Storm is comprised of topologies. A topology is a graph of computations — each node contains processing logic and each path between nodes indicates how data should be passed between nodes. A topology comprises of network of streams which is unbounded sequence of tuples. In short:

Tuple: Ordered list of elements. Eg: (“orange”,”tweet-123″,..,..,..).
Valid type: String, Integer, byte-array, or you can also define your own serializers so that custom types can be used natively within tuples.
Streams: Unbounded sequence of tuples: Tuple1, Tuple2, Tuple3

Storm uses SPOUTS, which takes an continuous input stream from a source viz. twitter and pass this chunk of data (or emit this stream) to another component called as BOLTS to consume. An emitted tuple can go from a “Spout” to “Bolt” or/and from “Bolt” to another “Bolt”. A Storm topology may have one or more Spouts and Bolts. As an implementer/programmer, multiple spouts/bolts can be configured as per the business logic.

(The above image is from Apache Storm’s website)

STORMs Architecturial overview
Storm run in a clustered environment. Similar to Hadoop, it has two types of nodes:

Master node: This node runs a daemon process called ‘Nimbus’. Nimbus is responsible for distributing code or the toplogy (spouts+bolts) across the cluster, assigning tasks to worker nodes, and monitoring the success and failure of units of work.
Worker node: Worker node has a node called as daemon process called the ‘Supervisor’. A Supervisor is responsible to starts and stops worker processes. Each worker process executes a subset of a topology, so that the execution of a topology is spread across a different worker processes that are running on cluster.

Storm leverages ZooKeeper to maintain the communication between Nimbus and Supervisor. Nimbus communicates to Supervisor by passing messages to Zookeeper. Zookeeper maintain the complete state of toplogy so that Nimbus and Supervisors to be fail-fast and stateless.

Storm mode
1. Local mode: In local mode, Storm executes topologies completely in-process by simulating worker nodes using threads.
2. Distributed mode: Runs across the cluster of machines.

Map Reduce without jargons

Map Reduce
In simple terms, a list of <key, value> pairs mapped into another list of <key, value> pairs which gets grouped by the key and reduced into a list of values.

E.g:

[A] dataset1.txt: Hadoop was created by Doug Cutting and Mike Cafarella
[B] dataset2.txt: Cutting, who was working at Yahoo! at the time, named it after his son’s toy elephant

This two data-sets [A & B] as an input will get divided into splits. Each split will have a key, value pair. In this case the key will be the offset/line number and the value will be the content of the respective split for that offset/line number. The map function discards the line number and produces a per-line (word, count) pair for each word in the input line.

So the mapper output (from (line number, text) as an input ——> (word, count) pair —-> as an input to map phase):

[(“Hadoop”, 1),(“was”, 1),(“created”, 1),(“by”, 1),(“Dough”,1),(“and”, 1),(“Mike”, 1),(“Cafarella”, 1),(“who”, 1),(“was”, 1),(“working”, 1),(“at”, 1),(“Yahoo”, 1),(“at”, 1),(“the”, 1),(“time”, 1),(“named”, 1),(“it”, 1),(“after”, 1),(“his”, 1),(“son’s”,1),(“toy”,1),(“elephant”,1)]

The output of mapper contains multiple key-value pairs with the same key. So before entering into reducer phase, the map-reduce framework will consolidate all the values for similar key. So the input to the reducer is actually (key, value) pairs. Below is the output from the shuffle phase:

{“Hadoop”: [1] ,”was”: [1,1], “created”: [1], “by”: [1], “Dough”:[1] , “and”: [1], “Mike”: [1], “Cafarella”: [1] ,
“who”: [1], “working”: [1], “at”: [1,1], “Yahoo”: [1], “the”: [1] , “time”: [1], “named”: [1], “it”: [1], “after”: [1], “his”: [1], “son’s”:[1], “toy”:[1], “elephant”:[1] }

Post shuffle, the reducer will take the above consolidate key-value pair input and simply sums up the list of intermediate values and produce the intermediate key and the sum as output:

[(“Hadoop”, 1),(“was”, 2),(“created”, 1),(“by”, 1),(“Dough”,1),(“and”, 1),(“Mike”, 1),(“Cafarella”, 1),(“who”, 1),(“was”, 1),(“working”, 1),(“at”, 2),(“Yahoo”, 1),(“at”, 1),(“the”, 1),(“time”, 1),(“named”, 1),(“it”, 1),(“after”, 1),(“his”, 1),(“son’s”,1),(“toy”,1),(“elephant”,1)]

Apache Pig Quick Tutorial

The following tutorial is about Apache Pig. This is a beginners tutorial which came as a part of self learning.

# Prequisite on Ubuntu:
1. hadoop-1.0.3
2. pig-0.12.0
3. DataSet: Sample youtube dataset (http://netsg.cs.sfu.ca/youtubedata/)

From https://pig.apache.org/:
“Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.”

1. Modes in Apache Pig:
a. Local Mode
– Require single machine;
– To start in local mode you need to specify -x flag (pig -x local).
– Suitable for learning and testing small dataset.

b. MapReduce Mode
– Default Mode (Not required to specify pig -x mapreduce)
– Require access to a Hadoop cluster and HDFS installation.

# Start the Pig in local mode:
hduser@localhost:~$ pig -x local

#Loading dataset into Pig:
grunt> youTube = LOAD ‘/home/hduser/pig_data/utube_mod1.csv’ USING PigStorage(‘,’) as (video_id,uploader,age,category,length,views,rate,ratings,comments);

Here, above the ‘youTube’ is a relation. The ‘PigStorage’ is one of the Load/Store utility. It parses input records based on a delimiter and the fields thereafter can be referenced positionally or via alias.

#describe: describe operator returns the schema for the above relation.
grunt> describe youTube;
youTube: {video_id: bytearray,uploader: bytearray,age: bytearray,category: bytearray,length: bytearray,views: bytearray,rate: bytearray,ratings: bytearray,comments: bytearray}

#dump: dump operator to run (execute) Pig Latin statements interactively and display the results to your screen. Generally dump operator is used for debugging purpose.
grunt> DUMP youTube;

2014-02-19 15:18:45,550 [main] INFO  org.apache.pig.tools.pigstats.ScriptState - Pig features used in the script: UNKNOWN
2014-02-19 15:18:45,718 [main] INFO  org.apache.pig.newplan.logical.optimizer.LogicalPlanOptimizer - {RULES_ENABLED=[AddForEach, ColumnMapKeyPrune, DuplicateForEachColumnRewrite, GroupByConstParallelSetter, ImplicitSplitInserter, LimitOptimizer, LoadTypeCastInserter, MergeFilter, MergeForEach, NewPartitionFilterOptimizer, PartitionFilterOptimizer, PushDownForEachFlatten, PushUpFilter, SplitFilter, StreamTypeCastInserter], RULES_DISABLED=[FilterLogicExpressionSimplifier]}
2014-02-19 15:18:46,091 [main] INFO  org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MRCompiler - File concatenation threshold: 100 optimistic? false
2014-02-19 15:18:46,241 [main] INFO  org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer - MR plan size before optimization: 1
2014-02-19 15:18:46,243 [main] INFO  org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer - MR plan size after optimization: 1
2014-02-19 15:18:46,422 [main] INFO  org.apache.pig.tools.pigstats.ScriptState - Pig script settings are added to the job
2014-02-19 15:18:46,567 [main] INFO  org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler - mapred.job.reduce.markreset.buffer.percent is not set, set to default 0.3
2014-02-19 15:18:46,683 [main] INFO  org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler - Setting up single store job
2014-02-19 15:18:46,691 [main] INFO  org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler - Map only job, skipping reducer estimation
2014-02-19 15:18:46,819 [main] INFO  org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MapReduceLauncher - 1 map-reduce job(s) waiting for submission.
........................
........................

2014-02-19 15:18:51,518 [Thread-4] INFO  org.apache.hadoop.mapred.Task - Task 'attempt_local_0001_m_000000_0' done.
2014-02-19 15:18:51,953 [main] WARN  org.apache.pig.tools.pigstats.PigStatsUtil - Failed to get RunningJob for job job_local_0001
2014-02-19 15:18:51,974 [main] INFO  org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MapReduceLauncher - 100% complete
2014-02-19 15:18:51,974 [main] INFO  org.apache.pig.tools.pigstats.SimplePigStats - Detected Local mode. Stats reported below may be incomplete
2014-02-19 15:18:52,002 [main] INFO  org.apache.pig.tools.pigstats.SimplePigStats - Script Statistics: 

HadoopVersion	PigVersion	UserId	StartedAt	FinishedAt	Features
1.0.3	0.12.0	hduser	2014-02-19 15:18:46	2014-02-19 15:18:51	UNKNOWN

Success!

Job Stats (time in seconds):
JobId	Alias	Feature	Outputs
job_local_0001	youTube	MAP_ONLY	file:/tmp/temp950919397/tmp-2040316079,

Input(s):
Successfully read records from: "/home/hduser/pig_data/utube_mod1.csv"

Output(s):
Successfully stored records in: "file:/tmp/temp950919397/tmp-2040316079"

Job DAG:
job_local_0001

2014-02-19 15:18:52,029 [main] INFO  org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MapReduceLauncher - Success!
2014-02-19 15:18:52,043 [main] WARN  org.apache.pig.data.SchemaTupleBackend - SchemaTupleBackend has already been initialized
2014-02-19 15:18:52,059 [main] INFO  org.apache.hadoop.mapreduce.lib.input.FileInputFormat - Total input paths to process : 1
2014-02-19 15:18:52,062 [main] INFO  org.apache.pig.backend.hadoop.executionengine.util.MapRedUtil - Total input paths to process : 1
(D6frFp-VwHs,yetube,821,Entertainment,30,554455,3.54,2813,422)
(0Lg4i2C6zws,TNAwrestling,821,Sports,573,191461,4.46,217,111)
(UJpgxqYGws4,jrc0803,820,Sports,55,160852,4.09,486,423)
(BAPwg5nCKxE,milanoss,820,Film & Animation,578,170536,4.06,82,91)
(vVJ06ixj19Q,PimpimusPrime,820,Film & Animation,29,95950,4.61,134,185)
(UbhEunreGwQ,milanoss,820,Film & Animation,525,113422,4.06,48,44)
(sR2n3_fg-bY,koushibom,821,News & Politics,94,70136,3.79,38,36)
(ZB-MtI2sgP4,hotelcalifornians,820,Autos & Vehicles,77,77178,4.13,142,148)
(n-cLsNrL6W8,ganggeneral,820,Music,200,72386,3.55,279,279)
(KsL1F4HFxv0,deej240z,821,Entertainment,17,39270,4.51,41,60)
..........

# List the youtube videos having ‘rate’ greater than 3:
grunt> rate_more_than_three = FILTER youTube BY (float) rate>3.0;
2014-02-19 15:31:22,714 [main] WARN org.apache.pig.PigServer – Encountered Warning IMPLICIT_CAST_TO_DOUBLE 1 time(s).

grunt> DUMP rate_more_than_three;

grunt> DUMP rate_more_than_three;
................
(D6frFp-VwHs,yetube,821,Entertainment,30,554455,3.54,2813,422)
(0Lg4i2C6zws,TNAwrestling,821,Sports,573,191461,4.46,217,111)
(UJpgxqYGws4,jrc0803,820,Sports,55,160852,4.09,486,423)
(BAPwg5nCKxE,milanoss,820,Film & Animation,578,170536,4.06,82,91)
(vVJ06ixj19Q,PimpimusPrime,820,Film & Animation,29,95950,4.61,134,185)
(UbhEunreGwQ,milanoss,820,Film & Animation,525,113422,4.06,48,44)
(sR2n3_fg-bY,koushibom,821,News & Politics,94,70136,3.79,38,36)
(ZB-MtI2sgP4,hotelcalifornians,820,Autos & Vehicles,77,77178,4.13,142,148)
..........................

#Storing output to file:

grunt> store rate_more_than_three into '/home/hduser/rate_more_than_three';
..............
Success!

Job Stats (time in seconds):
JobId	Alias	Feature	Outputs
job_local_0003	rate_more_than_three,youTube	MAP_ONLY	/home/hduser/rate_more_than_three,

Input(s):
Successfully read records from: "/home/hduser/pig_data/utube_mod1.csv"

Output(s):
Successfully stored records in: "/home/hduser/rate_more_than_three"

#Youtube video length >=500 AND <=1000
grunt> length_between_500_1000 = FILTER youTube by length >=500 AND length <=1000;

0Lg4i2C6zws,TNAwrestling,821,Sports,573,191461,4.46,217,111)
(BAPwg5nCKxE,milanoss,820,Film & Animation,578,170536,4.06,82,91)
(UbhEunreGwQ,milanoss,820,Film & Animation,525,113422,4.06,48,44)
(V__TtNHKXLU,Orkunyk,821,Film & Animation,599,27399,4.87,104,31)
(_IDfKKWBEZk,pundital,820,News & Politics,573,40935,4.76,155,215)
(hOgvS9c5Kz0,Orkunyk,821,Film & Animation,535,20141,5,74,16)
(doKkOSMaTk4,berkeleyguy0,821,News & Politics,585,20025,4.85,229,315)
(H23vitezN2E,vinnicamara,821,Entertainment,542,19689,4.7,74,85)
(fCDZnp4Pv4g,SoftAnime,820,Film & Animation,544,18110,4.44,50,30)
(koI4vN8Qosw,vinnicamara,821,Entertainment,533,17071,4.77,62,31)
(KY7MdPMyuhA,chriseliterbd,820,Entertainment,558,14845,4.99,130,27)
(RoFq0Be-6q0,DiziTube,821,Entertainment,596,10532,4.96,142,16)
(frvIaUVPN6I,TNAwrestling,820,Sports,894,123172,4.5,159,69)

#ILLUSTRATE: The ILLUSTRATE operator is used to examine how data is transformed through provided Pig Latin statements. ILLUSTRATE allows you to test your programs on small datasets and get faster turnaround times.
grunt> illustrate length_between_500_1000;

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
| youTube     | video_id:bytearray    | uploader:bytearray    | age:bytearray    | category:bytearray    | length:bytearray    | views:bytearray    | rate:bytearray    | ratings:bytearray    | comments:bytearray    | 
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|             | M2aZoFm4RVI           | brettkeane            | 821              | People & Blogs        | 693                 | 951                | 4.07              | 183                  | 75                    | 
|             | 53JV_3QR8f4           | itslate2              | 820              | Entertainment         | 369                 | 9549               | 4.85              | 130                  | 111                   | 
|             | hxHjWYA50Ds           | Politicstv            | 819              | News & Politics       | 1204                | 36725              | 4.99              | 290                  | 133                   | 
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
| length_between_500_1000     | video_id:bytearray    | uploader:bytearray    | age:bytearray    | category:bytearray    | length:bytearray    | views:bytearray    | rate:bytearray    | ratings:bytearray    | comments:bytearray    | 
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|                             | M2aZoFm4RVI           | brettkeane            | 821              | People & Blogs        | 693                 | 951                | 4.07              | 183                  | 75                    | 
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

#GROUP BY:
grunt> grouped_by_category = group youTube by category;
grunt> dump grouped_by_category

( UNA ,{(O7pBhfJnoYw,oodashi,0, UNA ,276,69303,4.06,52,36)})
(Music,{(Cvu373qjj7A,NeedToBe2007,820,Music,304,14330,4,21,21),(i5QF8-JmVm8,TIinstoresjuly3rd,820,Music,307,27636,4.71,163,134),(HXCWXLvJumo,TheKlasix,820,Music,164,20410,4.69,13,25),(jdkyugXj9Pg,brandt4797,820,Music,200,31671,4.54,172,141),(EKXR3zmpnvY,TIvsTIP,821,Music,32,11827,4.82,11,15),(gbDD5HLwifY,myhumpsfergie123456,819,Music,304,384435,3.6,1227,962),(pZnWxzFMCHg,AvrilLavigneSucks,819,Music,304,226893,4.51,579,465),(b2N27e79EdY,VeGaS1004,821,Music,564,7348,4.71,35,119),(uZtjiITa8FE,AllHipHopcom,820,Music,191,55069,3.34,119,127),(NnSk9zv6M6w,BuyCurtisJune26th,819,Music,189,88931,4.06,272,374),(lyfkVooQnmo,alanyukfoo,820,Music,197,11571,5,21,12),(h8y1qL_HLQE,Tam1r,820,Music,201,122334,4.68,344,324),(SKovbXL7xSI,edenmaze,821,Music,265,207,4.32,19,79),(T_SDHfswB30,davidchoimusic,821,Music,139,2694,4.77,192,105),(n-cLsNrL6W8,ganggeneral,820,Music,200,72386,3.55,279,279),(amH3qAu9RA4,blink182this,820,Music,135,17376,4.74,115,179),(X0E6Sf2vOwI,toprank144,821,Music,249,649,4.89,112,8),(oIfEw4e-0Yg,sleepingbeautywood,820,Music,223,57706,4.68,579,359),(IBM8VOpC6Mg,Brookers,820,Music,140,17018,4.31,548,348),(_7KUs1TBkCc,hollywoodrecords,820,Music,199,28640,4.55,65,31),(Gp8WFljRHsQ,ysabellabrave,821,Music,223,17005,4.58,697,344)})
(Comedy,{(RGc-F_0Y7tM,infrastructurdeep,820,Comedy,254,313693,4.38,1571,1626),(6l7P_dZQtac,jarts,820,Comedy,17,287000,4.67,466,304),(JqOeDW5wu_E,earthbrightfuture,819,Comedy,15,235198,3.59,343,572),(Ryeyee-4tw4,nalts,822,Comedy,316,222,4.66,148,133),(yDJAIT6Y5dI,correoyahoo,821,Comedy,34,33895,3.93,44,34),(Y69y0LSgMxk,mrpregnant,822,Comedy,183,1342,4.27,75,346),(8NPwJu42aUo,moonkeyz,819,Comedy,32,174327,1.99,1561,2243),(RzkEsSHOxQU,marcmaron101,820,Comedy,280,73232,4.52,607,101),(mY5qJHZCz2I,DraX360,818,Comedy,88,160177,4.79,358,174),(QBqkPAtKQN8,THANOS43,822,Comedy,246,681,4.62,8,81),(D3Lr70lwaVg,rawful,820,Comedy,25,13313,4.83,103,42),(AVnvjE9ixZs,potlot14,821,Comedy,583,1377,4.91,100,30),(HkRUHugg8vU,jrc0803,819,Comedy,107,131531,3.72,990,1148),(Y9NgXIkyiwk,AgentXPQ,822,Comedy,108,1802,4.73,139,95),(ZcTFgT3Ufw8,nalts,820,Comedy,229,14892,4.76,691,407),(H2XgxvuB2zY,brettkeane,822,Comedy,220,430,3.08,105,82),(bOYsI7f90R0,galipoka,821,Comedy,77,2389,4.54,161,160),(WFqqPK7kN0c,morbeck,821,Comedy,210,624,4.27,62,84),(lx9WAc29kHA,MasakoX,822,Comedy,251,3496,4.9,383,379),(Ub9JGlr9ok4,KnoxsKorner1,820,Comedy,379,1134,4.81,90,96),(2shafTX1KF8,TheRealParis,819,Comedy,135,19030,4.31,487,428),(cMKvJRRgAVY,MMASK,821,Comedy,110,566,4.94,100,6),(o7zDjdsOasg,brettkeane,821,Comedy,675,712,4.25,157,57),(5ml65ZRBVVc,selectscreen,821,Comedy,77,975,4.89,224,9),(P0TCzE4HTZQ,HappySlip,822,Comedy,150,8911,4.74,663,416),(Dni7fBgm_iA,swiftkaratechop,820,Comedy,170,1135,4.89,64,128),(rmeMGbJvu9E,JamesNintendoNerd,822,Comedy,54,6735,4.81,274,135),(Rl3rEL8AlbI,Brookers,820,Comedy,70,15119,4.05,465,347),(E22gUUTG2VI,MarkDayComedy,821,Comedy,246,6799,4.84,946,148),(N4yfFAIR9kc,beebee890,820,Comedy,22,8209,2.07,69,97),(jf8zyO8UrLc,khayav,821,Comedy,34,1121,4.63,87,115),(R1lyKwlvkns,freemovies125,820,Comedy,98,28624,4.54,82,20),(4kHKSZvJscE,lonelygirll15,822,Comedy,186,202,4.98,112,6),(i8K4WFTzjtE,potlot14,821,Comedy,474,1290,4.92,113,37),(0Cbzow5FsKQ,TheWoodcreekFaction,821,Comedy,106,1365,4.45,121,90),(XAs68-oHKhA,guywiththeglasses,821,Comedy,22,2518,4.56,151,145),(73om6gY8XSA,nogoodtv,820,Comedy,442,644674,2.55,793,311),(pZiQBoG8K8E,nalts,821,Comedy,210,56230,3.84,887,469)})
(Sports,{(UJpgxqYGws4,jrc0803,820,Sports,55,160852,4.09,486,423),(pzgEJh6stMA,NBA,821,Sports,118,34627,4.34,61,57),(dh7Xxxhhr_8,NBA,821,Sports,126,25750,4.56,62,53),(UoAXg2pOG5s,jukimol,820,Sports,230,9010,3.7,70,91),(kaAdnLOuoAY,europeansportservice,820,Sports,333,10301,4.8,25,13),(LBBPoyXEOhM,maxpower1453,821,Sports,54,8927,4.76,504,169),(0Lg4i2C6zws,TNAwrestling,821,Sports,573,191461,4.46,217,111),(s7ZMRZCW9Xc,HockeyPacific,820,Sports,44,49214,2.98,141,591),(BgtJwf4dK1A,empiricalred,815,Sports,600,27387,4.88,283,127),(foquonPZSN8,BgirL5,819,Sports,45,48810,3.23,183,1097),(frvIaUVPN6I,TNAwrestling,820,Sports,894,123172,4.5,159,69),(wWrEEKNjIAY,TNAwrestling,821,Sports,223,11547,4.54,59,42),(mjmbJm7idkI,NBA,821,Sports,24,21217,4.59,32,71),(CgLYZHN78Kk,ProductZero,820,Sports,17,73897,4.6,239,358),(yDbX8I202VU,medakaschool,820,Sports,74,529826,4.53,1347,1145)})
(Howto & DIY,{(0S1nAcs772s,hortononon,819,Howto & DIY,46,119519,1.72,756,617),(TWTyJOsMmio,pigslop,822,Howto & DIY,468,1010,0,0,171),(xGcNpfT2L0s,diethealth,820,Howto & DIY,221,115003,2.74,91,82),(80TAx2mCnNc,oodashi,818,Howto & DIY,481,68664,4.15,26,14),(05oT4ejpYGQ,FreePSP,820,Howto & DIY,36,3533,1.67,18,221),(2IAEu6GtDWM,mikeskehan,821,Howto & DIY,140,2858,3.01,142,473)})
(Entertainment,{(IEWBO8xZzpo,DiziTube,821,Entertainment,596,3953,4.94,87,9),(5OaFXetu46Q,NBC,821,Entertainment,266,2452,4.68,92,19),(Sc33LPbqdl8,R3NDI3R,821,Entertainment,337,1782,3.15,140,170),(o_sz0NvQfLc,jakedeherrera,821,Entertainment,111,1004,4.89,81,59),(UbD7MG_j_UI,warren25smash,822,Entertainment,178,2252,3.15,200,202),(ut-RQaJum1c,DoctorArzt,820,Entertainment,29,30109,4.95,38,47),(iXT2E9Ccc8A,kembrew,819,Entertainment,561,46150,4.88,329,143),(MHor7QwhLY0,leorai,821,Entertainment,40,25229,2.75,8,7),(JX7zrbrnaOI,NOWWUT,821,Entertainment,59,19791,3.31,32,69),(s7uXvqfQvNI,TygerClaw,821,Entertainment,152,39929,4.87,180,188),(kxvT_F8GYV4,DoctorArzt,820,Entertainment,29,18794,4.44,9,8),(_idZr95SEAM,lostpromos,820,Entertainment,30,20441,5,24,12),(FXZBLli0_sA,GayGod,820,Entertainment,205,28006,3.53,337,575),(fu_GNJwUnxM,DiziTube,821,Entertainment,393,15614,4.92,354,127),(sYv8vzKKDVs,GWrocks09,820,Entertainment,98,18912,4.87,63,125),(JNiGret7EW4,aat08,821,Entertainment,52,29449,2,50,52),(_Z1X9zpBe_A,DharmaSecrets,820,Entertainment,30,16757,4.81,16,12),(DHDCITa7RyA,GWrocks09,820,Entertainment,153,17171,4.36,36,140),(KsL1F4HFxv0,deej240z,821,Entertainment,17,39270,4.51,41,60),(TJolUxvL3sQ,chriseliterbd,820,Entertainment,369,18302,5,125,18),(tXinnBzRSzg,txvoodoo,820,Entertainment,22,13673,4.5,16,19),(A5d-7KFINqM,vinnicamara,821,Entertainment,459,19187,4.84,74,34),(yZDNwXle154,ootpmovievids,821,Entertainment,30,47213,4.81,58,29),(-4R-4Q7vppg,HockeyCrazyRrazy,820,Entertainment,156,13572,4.45,29,124),(eNQidcorW_g,chriseliterbd,820,Entertainment,424,18506,4.87,124,17),(DE2O9CtuU5o,sundancechannel,821,Entertainment,73,21788,4.41,73,124),(H23vitezN2E,vinnicamara,821,Entertainment,542,19689,4.7,74,85),(G3bz3ZVjcEU,TVGuy88,820,Entertainment,123,11920,3.55,31,185),(koI4vN8Qosw,vinnicamara,821,Entertainment,533,17071,4.77,62,31),(KY7MdPMyuhA,chriseliterbd,820,Entertainment,558,14845,4.99,130,27),(#NAME?,chriseliterbd,820,Entertainment,395,14772,5,118,20),(oJWaEPxfgW8,Danoramma,820,Entertainment,133,11404,3.97,32,152),(785FtaTTezo,daisytree1,821,Entertainment,64,24954,3.15,54,43),(RoFq0Be-6q0,DiziTube,821,Entertainment,596,10532,4.96,142,16),(MxR99EdoZmE,chriseliterbd,820,Entertainment,183,14263,4.97,86,13),(o57wrk4mKvM,DharmaSecrets,820,Entertainment,29,10448,4.6,10,34),(UaxZ63N0rRk,elfactorx,820,Entertainment,140,14235,4.79,14,16),(6J2mr5zKgsk,vinnicamara,821,Entertainment,422,10164,4.88,58,50),(tKKLUIOpvSE,CelebTV,820,Entertainment,45,197144,1.87,587,221),(bOfAPHJTagY,BANGOUTCLIQUECOM,819,Entertainment,17,164822,4.68,638,928),(kMg0gGaMe0Q,tibermedia,819,Entertainment,235,187497,4.73,402,323),(izk3aYS9FIA,truefaithisle,820,Entertainment,61,138296,4.31,552,490),(H85ZaC7utus,sierraforest,816,Entertainment,27,96953,4.44,39,29),(20M8Kf0wqZg,yummyum07,819,Entertainment,304,91578,4.1,224,134),(bWJshfx9bTw,blahinsider,819,Entertainment,138,88262,3.93,190,289),(HiHdJ426_2k,goaltaker1,820,Entertainment,236,84088,4.46,240,291),(eQUAAwNJtg0,YTwatchdog,820,Entertainment,162,2391,4.64,133,568),(D6frFp-VwHs,yetube,821,Entertainment,30,554455,3.54,2813,422),(UAJctmZaLgY,tibermedia,818,Entertainment,407,87416,4.6,113,100),(KuYiFwTsFiw,DiziTube,821,Entertainment,599,10153,4.91,232,15),(UoubyKe9Xa0,DiziTube,821,Entertainment,599,9466,4.96,213,54),(_h-DUe3o4j4,peron75,820,Entertainment,280,5721,4.8,228,192),(RJIxWE2qDsk,DiziTube,821,Entertainment,599,9032,4.96,201,21),(mEDsATTpgGk,DiziTube,821,Entertainment,598,8823,4.99,182,20),(IuGyQRdPP5c,DiziTube,821,Entertainment,594,10151,4.95,183,16),(ZOQU1YP4SsE,DiziTube,821,Entertainment,597,10008,4.92,180,14),(boSrjupyOlw,peron75,821,Entertainment,345,1836,4.87,159,85),(53JV_3QR8f4,itslate2,820,Entertainment,369,9549,4.85,130,111),(scm9nRm8tMY,DeltaDJ2006,821,Entertainment,79,260,5,120,0),(IXFjHlaz1J0,DiGiTiLsOuL,821,Entertainment,303,1937,4.84,128,119),(rlom5CERakI,chriseliterbd,820,Entertainment,272,11151,4.97,110,11),(GwtkWMZbQHk,hoiitsroi,821,Entertainment,192,5451,4.56,126,156),(IT1rSaGmUDQ,chriseliterbd,820,Entertainment,211,13355,4.96,103,28),(FMgKXKsZN90,WHATTHEBUCKSHOW,821,Entertainment,498,2571,4.78,110,99),(ShbEBBcvtBc,chriseliterbd,820,Entertainment,395,11941,4.91,101,7),(rayY8wvYD08,vinnicamara,821,Entertainment,584,9532,4.81,80,157)})
(People & Blogs,{(i18uFHYsUvo,brettkeane,820,People & Blogs,897,729,4.39,143,74),(icd4MgHPOno,brettkeane,821,People & Blogs,1141,827,4.28,149,72),(ZPoWU65NszY,billybigun64,821,People & Blogs,528,746,4.99,80,64),(#NAME?,nickynik,822,People & Blogs,394,397,4.84,186,102),(ZWFtVnqMFu8,Daxflame,821,People & Blogs,236,24769,3.44,882,1472),(caC_fGJT-SM,Blunty3000,821,People & Blogs,247,2664,3.97,152,143),(hEHMhMjlDek,ren4165,820,People & Blogs,183,410,4.56,9,86),(OHCcVlRsllc,smpfilms,821,People & Blogs,164,7430,4.29,219,342),(QW7pxFBBjaU,rickyste,820,People & Blogs,13,13375,4.59,403,620),(mkoh0eXnnf0,kicesie,821,People & Blogs,44,626,3.91,11,125),(C1vZzxyVhV8,Zipster08,821,People & Blogs,245,2072,4.64,163,132),(SD--8k_IsQo,SoldierInGodsArmy,821,People & Blogs,473,480,4.85,141,71),(NkqbDeuKXNk,goldengun85,821,People & Blogs,372,622,1.94,48,96),(#NAME?,blacktreemedia,821,People & Blogs,129,51705,4.62,99,173),(8TwvvOC8vdU,theboringdispatcher,821,People & Blogs,301,1201,4.28,68,80),(NUqEVO_C5ss,applemilk1988,820,People & Blogs,279,35025,4,430,823),(bp_TNrl8xc4,brettkeane,821,People & Blogs,364,464,4.49,99,37),(iaU9puwzMkE,hoiitsroi,821,People & Blogs,420,4726,4.72,127,230),(FiSddAJNIoc,brettkeane,821,People & Blogs,903,636,4.44,147,41),(P4im8gGPdGM,karpmax,821,People & Blogs,62,19973,4.18,11,15),(c5-TCNHSPkk,YourTubeNEWS,821,People & Blogs,341,687,3.11,114,77),(M2aZoFm4RVI,brettkeane,821,People & Blogs,693,951,4.07,183,75),(HAt8hmTNVbY,biostudentgirl,819,People & Blogs,160,124923,4.05,633,312),(O6_oXxTWHmo,mushcul,817,People & Blogs,83,52239,4.45,106,514),(CN-rHMWlB4w,soccerstar4ever,820,People & Blogs,192,45006,2.95,187,219),(c2wkbdBprDw,ashleytisdale,821,People & Blogs,44,24717,4.55,446,434),(6P1IR84LwI4,hydroax,820,People & Blogs,15,18533,1.8,5,4),(CySrshUMwIw,blacktreemedia,821,People & Blogs,242,78879,4.08,216,349),(IvpaBrX52pM,MissMalena13,821,People & Blogs,177,1707,4.95,387,21),(hoVr6iIKj_c,communitychannel,822,People & Blogs,206,7771,4.78,362,327),(7mMw9TdZLxI,khriskhaos2,821,People & Blogs,0,1043,0,0,114),(ilt0rxr5gQk,dramatubearchive,820,People & Blogs,110,3617,3.64,45,812),(T9FJIYBWdTQ,zakgeorge21,822,People & Blogs,638,590,4.75,83,107),(Gs87lI02tek,renetto,820,People & Blogs,646,8516,4.36,448,650),(TCMpVv87g6E,TheAmazingAtheist,821,People & Blogs,913,2805,3.79,486,168),(ezgk4QXwIhY,xgobobeanx,821,People & Blogs,112,1075,4.61,88,95),(E2YTdBsEnro,spricket24,821,People & Blogs,578,3676,4.59,261,228)})
(Pets & Animals,{(T7NpsCWvjzg,HellionExciter,821,Pets & Animals,289,1670,4.21,103,128),(s2ymS4fmjGQ,Padovarulezcom,821,Pets & Animals,88,10925,0,0,0)})
(Gadgets & Games,{(tOF8-Z7yQ_U,joystiq,820,Gadgets & Games,88,10991,4.47,30,25),(jDdtzpmVb1U,UrinatingTree,821,Gadgets & Games,424,1764,4.75,89,0),(s8hRgb0WTt4,ostekakepstsnet,821,Gadgets & Games,415,1135,4.84,108,39),(w867ePtiaZI,UrinatingTree,821,Gadgets & Games,546,1669,4.79,95,93),(xTkLDGtu36Q,Marriland,821,Gadgets & Games,348,2214,4.93,70,131),(Wq3laSZAT7U,spritefan2,821,Gadgets & Games,180,800,4.64,44,98),(R07C3wfft_4,HotPinkMidNite,820,Gadgets & Games,104,1278,4.92,84,83)})
(News & Politics,{(3UrumrPlFiU,cpotato2004,815,News & Politics,605,2931,3.98,62,409),(sR2n3_fg-bY,koushibom,821,News & Politics,94,70136,3.79,38,36),(xcQQ05XtAQ4,RonPaul2008dotcom,821,News & Politics,295,47309,4.89,1263,736),(CY86R1qjgDc,CBS,821,News & Politics,123,61187,3.8,122,12),(shoyObDet4Q,lbracci,820,News & Politics,1529,24533,4.24,38,67),(_IDfKKWBEZk,pundital,820,News & Politics,573,40935,4.76,155,215),(rF3NtEWj6ws,VoteRonPaul08,820,News & Politics,363,24460,4.9,343,292),(R0HEKTr6wrc,tpmtv,821,News & Politics,67,27474,4.61,84,90),(j_qUvgfzuPM,bgcaplay,820,News & Politics,312,14986,2.57,177,78),(ceIXPrfuGxg,puratrampa,820,News & Politics,110,16138,2.29,7,7),(doKkOSMaTk4,berkeleyguy0,821,News & Politics,585,20025,4.85,229,315),(T_VC8iH7lXQ,serkanserkanserkannn,821,News & Politics,40,13052,3.4,5,2),(YPbr5L4ByfE,koushibom,821,News & Politics,415,15045,3.33,3,11),(3FV7XU-TLMU,hillaryclintondotcom,820,News & Politics,53,277309,2.8,2038,50),(vJRDZE5xW2Y,ecogeeky,816,News & Politics,114,169722,4.71,17,15),(YkAPaEMwyKU,TRUEADONIS,819,News & Politics,292,166576,4.76,1423,1368),(Sy4Eugc0Xls,karlspackler,820,News & Politics,363,87170,4.89,1723,1408),(J8oO_OD3PtI,pumaman1,820,News & Politics,472,7942,4.86,326,177),(D6SfmXigHpE,suntereo,821,News & Politics,601,19941,4.87,285,284),(MvID-e_irz4,warren25smash,821,News & Politics,314,1743,4.52,220,166),(PvrrPCkHKLw,aravoth,821,News & Politics,237,8010,4.93,136,60),(LMO3Cg0frB8,brettkeane,821,News & Politics,808,474,4.2,117,47),(Q5VeaUW12pY,MiddleClassified,820,News & Politics,476,14571,4.98,412,179),(hxHjWYA50Ds,Politicstv,819,News & Politics,1204,36725,4.99,290,133),(DTHOgoGBUkw,michellemalkin,821,News & Politics,302,4655,4.14,74,187),(Y_r8mMCsSHA,onedeaddj,821,News & Politics,312,3663,1.79,136,177),(Yj1wext0CQc,ResurrectionOfCG,821,News & Politics,391,370,3.88,43,94),(AF_frpUoMIg,thereaganite84,820,News & Politics,31,289,5,10,85),(xP_2M5DDAuM,joshallem,820,News & Politics,583,495,0,0,83),(sk334TbliaY,GalacticCabaret,819,News & Politics,169,10138,4.91,141,645),(Hc1ohELwjWo,lzpoint1944,819,News & Politics,60,23672,2.85,177,463),(KNz0pta4PVU,VoteRonPaul08,819,News & Politics,301,24500,4.81,270,435)})
(Travel & Places,{(vPtsS4UTuis,sxephil,821,Travel & Places,260,12642,4.02,119,86)})
(Autos & Vehicles,{(ZB-MtI2sgP4,hotelcalifornians,820,Autos & Vehicles,77,77178,4.13,142,148),(DjPfzKbYDXw,lampesuda,818,Autos & Vehicles,48,83313,4.31,77,117),(9OLYrVcjmzM,casperjello,820,Autos & Vehicles,2,21754,4,37,25),(po15iWrv9zs,tellible,820,Autos & Vehicles,141,30703,4.17,100,621),(c0QQ3mqJEIk,jufanet,820,Autos & Vehicles,156,38698,3.66,110,485),(9GCUEamZRSs,casperjello,820,Autos & Vehicles,2,16866,4.11,27,23),(rCvifZybyLg,autoknipsfix,821,Autos & Vehicles,91,36347,4.2,84,82)})
(Film & Animation,{(#NAME?,ChappiRukia,821,Film & Animation,232,1487,4.87,163,96),(nlWOu9FHm-I,truefaithisle,820,Film & Animation,250,212185,4.62,1154,1713),(QdTZQkfkG0Q,animeswordns14s1,821,Film & Animation,252,18277,4.87,94,13),(fCDZnp4Pv4g,SoftAnime,820,Film & Animation,544,18110,4.44,50,30),(v74fPO89L0U,Orkunyk,821,Film & Animation,398,12588,4.93,55,12),(QsLKiPX9Kn4,Orkunyk,821,Film & Animation,389,13732,4.84,74,12),(6vzQ7fCTqzU,Orkunyk,821,Film & Animation,406,13990,4.92,49,8),(N0p17EQaUsA,Orkunyk,821,Film & Animation,457,20447,4.93,68,12),(QSeKvw7KnN4,Orkunyk,821,Film & Animation,159,14780,4.79,80,52),(vVJ06ixj19Q,PimpimusPrime,820,Film & Animation,29,95950,4.61,134,185),(D2FXCczEYCI,Orkunyk,821,Film & Animation,398,21848,4.91,66,16),(wBbQKSvQ5h4,Orkunyk,821,Film & Animation,380,16006,4.98,61,7),(_S2ODLHD_xk,Orkunyk,821,Film & Animation,359,22643,4.97,68,10),(BAPwg5nCKxE,milanoss,820,Film & Animation,578,170536,4.06,82,91),(MYSuY2UIj9U,Orkunyk,821,Film & Animation,419,16339,4.92,64,6),(CndugLgU02U,Orkunyk,821,Film & Animation,447,24484,4.85,86,14),(8OkkJEt52HM,bountyTR,821,Film & Animation,26,19644,4.86,7,13),(hOgvS9c5Kz0,Orkunyk,821,Film & Animation,535,20141,5,74,16),(V__TtNHKXLU,Orkunyk,821,Film & Animation,599,27399,4.87,104,31),(S84s5OQIKg8,macpulenta,819,Film & Animation,235,24248,4.89,432,164),(UbhEunreGwQ,milanoss,820,Film & Animation,525,113422,4.06,48,44),(XKwaYhOnwRg,cankiriklari,821,Film & Animation,600,4427,4.96,80,14),(UWLYfu04-RM,OneMJDN,821,Film & Animation,600,479,4.94,90,33),(Iy3MkWs1-PI,Samsunlu1,820,Film & Animation,618,4947,4.95,91,13),(PNQwOuTpgLM,Samsunlu1,820,Film & Animation,617,4005,4.9,97,11),(TIs5FiOG2ho,Samsunlu1,820,Film & Animation,615,4551,4.91,104,7)})
grunt>

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