Hi Rohit, I would like to thank you for the unlimited patience and support that you are providing here and behind the scene for all of us.
The tool is amazing and easy to use and understand most of the metrics ... Thinking if we need to run it in cluster mode and all the time, i think we can skip it as one or few runs can give you the large picture of how the job is running with different configuration and it's not too much complicated to run it using spark-submit. I think it will be so helpful if the sparklens can also include how the job is running with different configuration of cores and memory, Spark job with 1 exec and 1 core will run different from spark job with 1 exec and 3 cores and for sure the same compare with different exec memory. Overall, it is so good starting point, but it will be a GAME CHANGER getting these metrics on the tool. @Rohit , Huge THANY YOU On Mon, Mar 26, 2018 at 1:35 PM, Rohit Karlupia <roh...@qubole.com> wrote: > Hi Shmuel, > > In general it is hard to pin point to exact code which is responsible for > a specific stage. For example when using spark sql, depending upon the kind > of joins, aggregations used in the the single line of query, we will have > multiple stages in the spark application. I usually try to split the code > into smaller chunks and also use the spark UI which has special section for > SQL. It can also show specific backtraces, but as I explained earlier they > might not be very helpful. Sparklens does help you ask the right questions, > but is not mature enough to answer all of them. > > Understanding the report: > > *1) The first part of total aggregate metrics for the application.* > > Printing application meterics..... > > AggregateMetrics (Application Metrics) total measurements 1869 > NAME SUM MIN > MAX MEAN > diskBytesSpilled 0.0 KB 0.0 KB > 0.0 KB 0.0 KB > executorRuntime 15.1 hh 3.0 ms > 4.0 mm 29.1 ss > inputBytesRead 26.1 GB 0.0 KB > 43.8 MB 14.3 MB > jvmGCTime 11.0 mm 0.0 ms > 2.1 ss 354.0 ms > memoryBytesSpilled 314.2 GB 0.0 KB > 1.1 GB 172.1 MB > outputBytesWritten 0.0 KB 0.0 KB > 0.0 KB 0.0 KB > peakExecutionMemory 0.0 KB 0.0 KB > 0.0 KB 0.0 KB > resultSize 12.9 MB 2.0 KB > 40.9 KB 7.1 KB > shuffleReadBytesRead 107.7 GB 0.0 KB > 276.0 MB 59.0 MB > shuffleReadFetchWaitTime 2.0 ms 0.0 ms > 0.0 ms 0.0 ms > shuffleReadLocalBlocks 2,318 0 > 68 1 > shuffleReadRecordsRead 3,413,511,099 0 > 8,251,926 1,826,383 > shuffleReadRemoteBlocks 291,126 0 > 824 155 > shuffleWriteBytesWritten 107.6 GB 0.0 KB > 257.6 MB 58.9 MB > shuffleWriteRecordsWritten 3,408,133,175 0 > 7,959,055 1,823,506 > shuffleWriteTime 8.7 mm 0.0 ms > 1.8 ss 278.2 ms > taskDuration 15.4 hh 12.0 ms > 4.1 mm 29.7 ss > > > *2) Here we show number of hosts used and executors per host. I have seen > users set executor memory to 33GB on a 64GB executor. Direct waste of 31GB of > memory.* > > Total Hosts 135 > > > Host server86.cluster.com startTime 02:26:21:081 executors count 3 > Host server164.cluster.com startTime 02:30:12:204 executors count 1 > Host server28.cluster.com startTime 02:31:09:023 executors count 1 > Host server78.cluster.com startTime 02:26:08:844 executors count 5 > Host server124.cluster.com startTime 02:26:10:523 executors count 3 > Host server100.cluster.com startTime 02:30:24:073 executors count 1 > Done printing host timeline > *3) Time at which executers were added. Not all executors are available at > the start of the application. * > > Printing executors timeline.... > Total Hosts 135 > Total Executors 250 > At 02:26 executors added 52 & removed 0 currently available 52 > At 02:27 executors added 10 & removed 0 currently available 62 > At 02:28 executors added 13 & removed 0 currently available 75 > At 02:29 executors added 81 & removed 0 currently available 156 > At 02:30 executors added 48 & removed 0 currently available 204 > At 02:31 executors added 45 & removed 0 currently available 249 > At 02:32 executors added 1 & removed 0 currently available 250 > > > *4) How the stages within the jobs were scheduled. Helps you understand which > stages ran in parallel and which are dependent on others. > * > > Printing Application timeline > 02:26:47:654 Stage 3 ended : maxTaskTime 3117 taskCount 1 > 02:26:47:708 Stage 4 started : duration 00m 02s > 02:26:49:898 Stage 4 ended : maxTaskTime 226 taskCount 200 > 02:26:49:901 JOB 3 ended > 02:26:56:234 JOB 4 started : duration 08m 28s > [ 5 ||||||| > ] > [ 6 ||||||||||||||||||| > ] > [ 9 |||||||| > ] > [ 10 |||||||||||||| > ] > [ 11 > ] > [ 12 || > ] > [ 13 |||| > ] > [ 14 ||||||||||||||| > ] > [ 15 > |||||||||||||||||||||||||||||||||||||| ] > 02:26:58:095 Stage 5 started : duration 00m 44s > 02:27:42:816 Stage 5 ended : maxTaskTime 37214 taskCount 23 > 02:27:03:478 Stage 6 started : duration 02m 04s > 02:29:07:517 Stage 6 ended : maxTaskTime 35578 taskCount 601 > 02:28:56:449 Stage 9 started : duration 00m 46s > 02:29:42:625 Stage 9 ended : maxTaskTime 7196 taskCount 200 > 02:27:22:343 Stage 10 started : duration 01m 33s > 02:28:56:333 Stage 10 ended : maxTaskTime 49203 taskCount 39 > 02:27:23:910 Stage 11 started : duration 00m 00s > 02:27:24:422 Stage 11 ended : maxTaskTime 298 taskCount 2 > 02:29:06:902 Stage 12 started : duration 00m 12s > 02:29:19:350 Stage 12 ended : maxTaskTime 11511 taskCount 200 > 02:29:19:413 Stage 13 started : duration 00m 25s > 02:29:44:444 Stage 13 ended : maxTaskTime 24924 taskCount 200 > 02:29:44:491 Stage 14 started : duration 01m 36s > 02:31:20:873 Stage 14 ended : maxTaskTime 86194 taskCount 200 > 02:31:20:973 Stage 15 started : duration 04m 03s > 02:35:24:346 Stage 15 ended : maxTaskTime 238747 taskCount 200 > 02:35:24:347 JOB 4 ended > 02:35:28:841 app ended > *5) I guess these metrics are well explained * > > > Time spent in Driver vs Executors > Driver WallClock Time 01m 02s 10.66% > Executor WallClock Time 08m 43s 89.34% > Total WallClock Time 09m 46s > > > > Minimum possible time for the app based on the critical path (with infinite > resources) 07m 59s > Minimum possible time for the app with same executors, perfect parallelism > and zero skew 02m 15s > If we were to run this app with single executor and single core > 15h 08m > > > Total cores available to the app 750 > > OneCoreComputeHours: Measure of total compute power available from cluster. > One core in the executor, running > for one hour, counts as one OneCoreComputeHour. > Executors with 4 cores, will have 4 times > the OneCoreComputeHours compared to one with just one > core. Similarly, one core executor > running for 4 hours will OnCoreComputeHours equal to 4 > core executor running for 1 hour. > > Driver Utilization (Cluster idle because of driver) > > Total OneCoreComputeHours available 122h 07m > Total OneCoreComputeHours available (AutoScale Aware) 77h 25m > OneCoreComputeHours wasted by driver 13h 01m > > AutoScale Aware: Most of the calculations by this tool will assume that all > executors are available throughout > the runtime of the application. The number above is printed > to show possible caution to be > taken in interpreting the efficiency metrics. > > Cluster Utilization (Executors idle because of lack of tasks or skew) > > Executor OneCoreComputeHours available 109h 06m > Executor OneCoreComputeHours used 15h 07m 13.86% > OneCoreComputeHours wasted 93h 59m 86.14% > > App Level Wastage Metrics (Driver + Executor) > > OneCoreComputeHours wasted Driver 10.66% > OneCoreComputeHours wasted Executor 76.96% > OneCoreComputeHours wasted Total 87.62% > > > > 6) *Here we use the simulation to provide answers to how the application wall > clock time will vary as we change the number of executors. Goal is to run the > application at 100% cluster utilization and minimum time. Look for ROI in > terms of wall clock time due to additional executors. Also if the application > is not scaling, this is good time to revisit application and look for why it > is not scaling.* > > App completion time and cluster utilization estimates with different > executor counts > > Real App Duration 09m 46s > Model Estimation 08m 01s > Model Error 17% > > NOTE: 1) Model error could be large when auto-scaling is enabled. > 2) Model doesn't handles multiple jobs run via thread-pool. For better > insights into > application scalability, please try such jobs one by one without > thread-pool. > > > Executor count 25 ( 10%) estimated time 17m 07s and estimated cluster > utilization 70.61% > Executor count 50 ( 20%) estimated time 12m 15s and estimated cluster > utilization 49.34% > Executor count 125 ( 50%) estimated time 08m 25s and estimated cluster > utilization 28.72% > Executor count 200 ( 80%) estimated time 08m 15s and estimated cluster > utilization 18.29% > Executor count 250 (100%) estimated time 08m 01s and estimated cluster > utilization 15.06% > Executor count 275 (110%) estimated time 08m 00s and estimated cluster > utilization 13.72% > Executor count 300 (120%) estimated time 07m 59s and estimated cluster > utilization 12.61% > Executor count 375 (150%) estimated time 07m 59s and estimated cluster > utilization 10.09% > Executor count 500 (200%) estimated time 07m 59s and estimated cluster > utilization 7.57% > Executor count 750 (300%) estimated time 07m 59s and estimated cluster > utilization 5.04% > Executor count 1000 (400%) estimated time 07m 59s and estimated cluster > utilization 3.78% > Executor count 1250 (500%) estimated time 07m 59s and estimated cluster > utilization 3.03% > > *7) These two sections are for finding out which stage are taking most of the > wall-clock time and why. It is either not enough parallelism or skew. > Parallelism is easier to fix. Fixing skew will require changing the > application in way that creates more uniform tasks. > * > Total tasks in all stages 1869 > Per Stage Utilization > Stage-ID Wall Task Task IO% Input Output > ----Shuffle----- -WallClockTime- --OneCoreComputeHours--- MaxTaskMem > Clock% Runtime% Count Input | > Output Measured | Ideal Available| Used%|Wasted% > 0 0.00 0.00 1 0.0 64.0 KB 0.0 KB 0.0 KB > 0.0 KB 00m 02s 00m 00s 00h 27m 0.0 100.0 0.0 KB > 1 0.00 0.00 1 0.0 64.0 KB 0.0 KB 0.0 KB > 0.0 KB 00m 02s 00m 00s 00h 30m 0.1 99.9 0.0 KB > 2 0.00 0.00 1 0.0 90.0 KB 0.0 KB 0.0 KB > 0.0 KB 00m 03s 00m 00s 00h 37m 0.1 99.9 0.0 KB > 3 0.00 0.01 1 0.0 867.1 KB 0.0 KB 0.0 KB > 148.4 KB 00m 04s 00m 00s 01h 01m 0.1 99.9 0.0 KB > 4 0.00 0.00 200 0.0 0.0 KB 0.0 KB 148.4 KB > 0.0 KB 00m 02s 00m 00s 00h 27m 0.1 99.9 0.0 KB > 5 6.00 1.15 23 0.2 402.1 MB 0.0 KB 0.0 KB > 1.3 GB 00m 44s 00m 00s 09h 19m 1.9 98.1 0.0 KB > 6 17.00 19.92 601 7.1 17.2 GB 0.0 KB 0.0 KB > 1.8 GB 02m 04s 00m 14s 25h 50m 11.7 88.3 0.0 KB > 9 6.00 0.73 200 2.9 6.9 GB 0.0 KB 409.5 MB > 2.8 GB 00m 46s 00m 00s 09h 37m 1.2 98.8 0.0 KB > 10 13.00 2.27 39 0.3 807.8 MB 0.0 KB 0.0 KB > 2.5 GB 01m 33s 00m 01s 19h 34m 1.7 98.3 0.0 KB > 11 0.00 0.00 2 0.0 31.5 KB 0.0 KB 0.0 KB > 60.0 KB 00m 00s 00m 00s 00h 06m 0.1 99.9 0.0 KB > 12 1.00 2.15 200 0.3 758.7 MB 0.0 KB 2.3 GB > 1.5 GB 00m 12s 00m 01s 02h 35m 12.6 87.4 0.0 KB > 13 3.00 5.91 200 0.0 0.0 KB 0.0 KB 1.5 GB > 47.5 GB 00m 25s 00m 04s 05h 12m 17.1 82.9 0.0 KB > 14 13.00 19.83 200 0.0 0.0 KB 0.0 KB 50.3 GB > 50.3 GB 01m 36s 00m 14s 20h 04m 14.9 85.1 0.0 KB > 15 34.00 48.02 200 0.0 0.0 KB 0.0 KB 53.2 GB > 0.0 KB 04m 03s 00m 34s 50h 42m 14.3 85.7 0.0 KB > > > Stage-ID WallClock OneCore Task PRatio -----Task------ OIRatio > |* ShuffleWrite% ReadFetch% GC% *| > Stage% ComputeHours Count Skew StageSkew > 0 0.32 00h 00m 1 0.00 1.00 0.37 0.00 > |* 0.00 0.00 15.10 *| > 1 0.35 00h 00m 1 0.00 1.00 0.38 0.00 > |* 0.00 0.00 15.56 *| > 2 0.43 00h 00m 1 0.00 1.00 0.45 0.00 > |* 0.00 0.00 8.88 *| > 3 0.70 00h 00m 1 0.00 1.00 0.63 0.17 > |* 4.51 0.00 6.74 *| > 4 0.31 00h 00m 200 0.27 37.67 0.10 0.00 > |* 0.00 0.04 23.79 *| > 5 6.38 00h 10m 23 0.03 1.42 0.83 3.18 > |* 1.08 0.00 2.72 *| > 6 17.68 03h 00m 601 0.80 2.07 0.29 0.10 > |* 0.60 0.00 1.90 *| > 9 6.58 00h 06m 200 0.27 5.20 0.16 0.38 > |* 4.74 13.24 4.04 *| > 10 13.40 00h 20m 39 0.05 1.67 0.52 3.17 > |* 1.10 0.00 1.96 *| > 11 0.07 00h 00m 2 0.00 1.00 0.58 1.91 > |* 13.59 0.00 0.00 *| > 12 1.77 00h 19m 200 0.27 1.99 0.92 0.50 > |* 1.85 19.63 3.09 *| > 13 3.57 00h 53m 200 0.27 1.59 1.00 31.42 > |* 6.06 12.25 1.33 *| > 14 13.74 02h 59m 200 0.27 1.65 0.89 1.00 > |* 1.84 2.38 0.83 *| > 15 34.69 07h 15m 200 0.27 1.88 0.98 0.00 > |* 0.00 4.21 0.88 *| > > PRatio: Number of tasks in stage divided by number of cores. > Represents degree of > parallelism in the stage > TaskSkew: Duration of largest task in stage divided by duration of > median task. > Represents degree of skew in the stage > TaskStageSkew: Duration of largest task in stage divided by total duration of > the stage. > Represents the impact of the largest task on stage time. > OIRatio: Output to input ration. Total output of the stage (results + > shuffle write) > divided by total input (input data + shuffle read) > > These metrics below represent distribution of time within the stage > > ShuffleWrite: Amount of time spent in shuffle writes across all tasks in the > given > stage as a percentage > ReadFetch: Amount of time spent in shuffle read across all tasks in the > given > stage as a percentage > GC: Amount of time spent in GC across all tasks in the given stage > as a > percentage > > If the stage contributes large percentage to overall application time, we > could look into > these metrics to check which part (Shuffle write, read fetch or GC is > responsible) > > thanks, > > rohitk > > > > On Mon, Mar 26, 2018 at 1:38 AM, Shmuel Blitz <shmuel.bl...@similarweb.com > > wrote: > >> Hi Rohit, >> >> Thanks for the analysis. >> >> I can use repartition on the slow task. But how can I tell what part of >> the code is in charge of the slow tasks? >> >> It would be great if you could further explain the rest of the output. >> >> Thanks in advance, >> Shmuel >> >> On Sun, Mar 25, 2018 at 12:46 PM, Rohit Karlupia <roh...@qubole.com> >> wrote: >> >>> Thanks Shamuel for trying out sparklens! >>> >>> Couple of things that I noticed: >>> 1) 250 executors is probably overkill for this job. It would run in same >>> time with around 100. >>> 2) Many of stages that take long time have only 200 tasks where as we >>> have 750 cores available for the job. 200 is the default value for >>> spark.sql.shuffle.partitions. Alternatively you could try increasing >>> the value of spark.sql.shuffle.partitions to latest 750. >>> >>> thanks, >>> rohitk >>> >>> On Sun, Mar 25, 2018 at 1:25 PM, Shmuel Blitz < >>> shmuel.bl...@similarweb.com> wrote: >>> >>>> I ran it on a single job. >>>> SparkLens has an overhead on the job duration. I'm not ready to enable >>>> it by default on all our jobs. >>>> >>>> Attached is the output. >>>> >>>> Still trying to understand what exactly it means. >>>> >>>> On Sun, Mar 25, 2018 at 10:40 AM, Fawze Abujaber <fawz...@gmail.com> >>>> wrote: >>>> >>>>> Nice! >>>>> >>>>> Shmuel, Were you able to run on a cluster level or for a specific job? >>>>> >>>>> Did you configure it on the spark-default.conf? >>>>> >>>>> On Sun, 25 Mar 2018 at 10:34 Shmuel Blitz <shmuel.bl...@similarweb.com> >>>>> wrote: >>>>> >>>>>> Just to let you know, I have managed to run SparkLens on our cluster. >>>>>> >>>>>> I switched to the spark_1.6 branch, and also compiled against the >>>>>> specific image of Spark we are using (cdh5.7.6). >>>>>> >>>>>> Now I need to figure out what the output means... :P >>>>>> >>>>>> Shmuel >>>>>> >>>>>> On Fri, Mar 23, 2018 at 7:24 PM, Fawze Abujaber <fawz...@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> Quick question: >>>>>>> >>>>>>> how to add the --jars /path/to/sparklens_2.11-0.1.0.jar to the >>>>>>> spark-default conf, should it be using: >>>>>>> >>>>>>> spark.driver.extraClassPath /path/to/sparklens_2.11-0.1.0.jar or i >>>>>>> should use spark.jars option? anyone who could give an example how it >>>>>>> should be, and if i the path for the jar should be an hdfs path as i'm >>>>>>> using it in cluster mode. >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> On Fri, Mar 23, 2018 at 6:33 AM, Fawze Abujaber <fawz...@gmail.com> >>>>>>> wrote: >>>>>>> >>>>>>>> Hi Shmuel, >>>>>>>> >>>>>>>> Did you compile the code against the right branch for Spark 1.6. >>>>>>>> >>>>>>>> I tested it and it looks working and now i'm testing the branch for >>>>>>>> a wide tests, Please use the branch for Spark 1.6 >>>>>>>> >>>>>>>> On Fri, Mar 23, 2018 at 12:43 AM, Shmuel Blitz < >>>>>>>> shmuel.bl...@similarweb.com> wrote: >>>>>>>> >>>>>>>>> Hi Rohit, >>>>>>>>> >>>>>>>>> Thanks for sharing this great tool. >>>>>>>>> I tried running a spark job with the tool, but it failed with an >>>>>>>>> *IncompatibleClassChangeError >>>>>>>>> *Exception. >>>>>>>>> >>>>>>>>> I have opened an issue on Github.(https://github.com/qub >>>>>>>>> ole/sparklens/issues/1) >>>>>>>>> >>>>>>>>> Shmuel >>>>>>>>> >>>>>>>>> On Thu, Mar 22, 2018 at 5:05 PM, Shmuel Blitz < >>>>>>>>> shmuel.bl...@similarweb.com> wrote: >>>>>>>>> >>>>>>>>>> Thanks. >>>>>>>>>> >>>>>>>>>> We will give this a try and report back. >>>>>>>>>> >>>>>>>>>> Shmuel >>>>>>>>>> >>>>>>>>>> On Thu, Mar 22, 2018 at 4:22 PM, Rohit Karlupia < >>>>>>>>>> roh...@qubole.com> wrote: >>>>>>>>>> >>>>>>>>>>> Thanks everyone! >>>>>>>>>>> Please share how it works and how it doesn't. Both help. >>>>>>>>>>> >>>>>>>>>>> Fawaze, just made few changes to make this work with spark 1.6. >>>>>>>>>>> Can you please try building from branch *spark_1.6* >>>>>>>>>>> >>>>>>>>>>> thanks, >>>>>>>>>>> rohitk >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> On Thu, Mar 22, 2018 at 10:18 AM, Fawze Abujaber < >>>>>>>>>>> fawz...@gmail.com> wrote: >>>>>>>>>>> >>>>>>>>>>>> It's super amazing .... i see it was tested on spark 2.0.0 and >>>>>>>>>>>> above, what about Spark 1.6 which is still part of Cloudera's main >>>>>>>>>>>> versions? >>>>>>>>>>>> >>>>>>>>>>>> We have a vast Spark applications with version 1.6.0 >>>>>>>>>>>> >>>>>>>>>>>> On Thu, Mar 22, 2018 at 6:38 AM, Holden Karau < >>>>>>>>>>>> hol...@pigscanfly.ca> wrote: >>>>>>>>>>>> >>>>>>>>>>>>> Super exciting! I look forward to digging through it this >>>>>>>>>>>>> weekend. >>>>>>>>>>>>> >>>>>>>>>>>>> On Wed, Mar 21, 2018 at 9:33 PM ☼ R Nair (रविशंकर नायर) < >>>>>>>>>>>>> ravishankar.n...@gmail.com> wrote: >>>>>>>>>>>>> >>>>>>>>>>>>>> Excellent. You filled a missing link. >>>>>>>>>>>>>> >>>>>>>>>>>>>> Best, >>>>>>>>>>>>>> Passion >>>>>>>>>>>>>> >>>>>>>>>>>>>> On Wed, Mar 21, 2018 at 11:36 PM, Rohit Karlupia < >>>>>>>>>>>>>> roh...@qubole.com> wrote: >>>>>>>>>>>>>> >>>>>>>>>>>>>>> Hi, >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Happy to announce the availability of Sparklens as open >>>>>>>>>>>>>>> source project. It helps in understanding the scalability >>>>>>>>>>>>>>> limits of spark >>>>>>>>>>>>>>> applications and can be a useful guide on the path towards >>>>>>>>>>>>>>> tuning >>>>>>>>>>>>>>> applications for lower runtime or cost. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Please clone from here: https://github.com/qubole/sparklens >>>>>>>>>>>>>>> Old blogpost: https://www.qubole.c >>>>>>>>>>>>>>> om/blog/introducing-quboles-spark-tuning-tool/ >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> thanks, >>>>>>>>>>>>>>> rohitk >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> PS: Thanks for the patience. It took couple of months to get >>>>>>>>>>>>>>> back on this. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>> -- >>>>>>>>>>>>> Twitter: https://twitter.com/holdenkarau >>>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> -- >>>>>>>>>> Shmuel Blitz >>>>>>>>>> Big Data Developer >>>>>>>>>> Email: shmuel.bl...@similarweb.com >>>>>>>>>> www.similarweb.com >>>>>>>>>> <https://www.facebook.com/SimilarWeb/> >>>>>>>>>> <https://www.linkedin.com/company/429838/> >>>>>>>>>> <https://twitter.com/similarweb> >>>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>>> Shmuel Blitz >>>>>>>>> Big Data Developer >>>>>>>>> Email: shmuel.bl...@similarweb.com >>>>>>>>> www.similarweb.com >>>>>>>>> <https://www.facebook.com/SimilarWeb/> >>>>>>>>> <https://www.linkedin.com/company/429838/> >>>>>>>>> <https://twitter.com/similarweb> >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> Shmuel Blitz >>>>>> Big Data Developer >>>>>> Email: shmuel.bl...@similarweb.com >>>>>> www.similarweb.com >>>>>> <https://www.facebook.com/SimilarWeb/> >>>>>> <https://www.linkedin.com/company/429838/> >>>>>> <https://twitter.com/similarweb> >>>>>> >>>>> >>>> >>>> >>>> -- >>>> Shmuel Blitz >>>> Big Data Developer >>>> Email: shmuel.bl...@similarweb.com >>>> www.similarweb.com >>>> <https://www.facebook.com/SimilarWeb/> >>>> <https://www.linkedin.com/company/429838/> >>>> <https://twitter.com/similarweb> >>>> >>> >>> >> >> >> -- >> Shmuel Blitz >> Big Data Developer >> Email: shmuel.bl...@similarweb.com >> www.similarweb.com >> <https://www.facebook.com/SimilarWeb/> >> <https://www.linkedin.com/company/429838/> >> <https://twitter.com/similarweb> >> > > -- Take Care Fawze Abujaber
