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>
>
