Let me share the Ipython notebook. On Tue, Jun 30, 2020 at 11:18 AM Gourav Sengupta <gourav.sengu...@gmail.com> wrote:
> Hi,
>
> I think that the notebook clearly demonstrates that setting the
> inferTimestamp option to False does not really help.
>
> Is it really impossible for you to show how your own data can be loaded?
> It should be simple, just open the notebook and see why the exact code you
> have given does not work, and shows only 11 records.
>
>
> Regards,
> Gourav Sengupta
>
> On Tue, Jun 30, 2020 at 4:15 PM Sanjeev Mishra <sanjeev.mis...@gmail.com>
> wrote:
>
>> Hi Gourav,
>>
>> Please check the comments of the ticket, looks like the performance
>> degradation is attributed to inferTimestamp option that is true by default
>> (I have no idea why) in Spark 3.0. This forces Spark to scan entire text
>> and so the poor performance.
>>
>> Regards
>> Sanjeev
>>
>> On Jun 30, 2020, at 8:12 AM, Gourav Sengupta <gourav.sengu...@gmail.com>
>> wrote:
>>
>> Hi, Sanjeev,
>>
>> I think that I did precisely that, can you please download my ipython
>> notebook and have a look, and let me know where I am going wrong. its
>> attached with the JIRA ticket.
>>
>>
>> Regards,
>> Gourav Sengupta
>>
>> On Tue, Jun 30, 2020 at 1:42 PM Sanjeev Mishra <sanjeev.mis...@gmail.com>
>> wrote:
>>
>>> There are total 11 files as part of tar. You will have to untar it to
>>> get to actual files (.json.gz)
>>>
>>> No, I am getting
>>>
>>> Count: 33447
>>>
>>> spark.time(spark.read.json(“/data/small-anon/"))
>>> Time taken: 431 ms
>>> res73: org.apache.spark.sql.DataFrame = [created: bigint, id: string ...
>>> 2 more fields]
>>>
>>> scala> res73.count()
>>> res74: Long = 33447
>>>
>>> ls -ltr
>>> total 7592
>>> -rw-r--r-- 1 sanjeevmishra staff 132413 Jun 29 08:40
>>> part-00010-2558524a-1a1f-4f14-b027-4276a8143194-c000.json.gz
>>> -rw-r--r-- 1 sanjeevmishra staff 272767 Jun 29 08:40
>>> part-00009-2558524a-1a1f-4f14-b027-4276a8143194-c000.json.gz
>>> -rw-r--r-- 1 sanjeevmishra staff 272314 Jun 29 08:40
>>> part-00008-2558524a-1a1f-4f14-b027-4276a8143194-c000.json.gz
>>> -rw-r--r-- 1 sanjeevmishra staff 277158 Jun 29 08:40
>>> part-00007-2558524a-1a1f-4f14-b027-4276a8143194-c000.json.gz
>>> -rw-r--r-- 1 sanjeevmishra staff 321451 Jun 29 08:40
>>> part-00006-2558524a-1a1f-4f14-b027-4276a8143194-c000.json.gz
>>> -rw-r--r-- 1 sanjeevmishra staff 331419 Jun 29 08:40
>>> part-00005-2558524a-1a1f-4f14-b027-4276a8143194-c000.json.gz
>>> -rw-r--r-- 1 sanjeevmishra staff 337195 Jun 29 08:40
>>> part-00004-2558524a-1a1f-4f14-b027-4276a8143194-c000.json.gz
>>> -rw-r--r-- 1 sanjeevmishra staff 366346 Jun 29 08:40
>>> part-00003-2558524a-1a1f-4f14-b027-4276a8143194-c000.json.gz
>>> -rw-r--r-- 1 sanjeevmishra staff 423154 Jun 29 08:40
>>> part-00002-2558524a-1a1f-4f14-b027-4276a8143194-c000.json.gz
>>> -rw-r--r-- 1 sanjeevmishra staff 458187 Jun 29 08:40
>>> part-00000-2558524a-1a1f-4f14-b027-4276a8143194-c000.json.gz
>>> -rw-r--r-- 1 sanjeevmishra staff 673836 Jun 29 08:40
>>> part-00001-2558524a-1a1f-4f14-b027-4276a8143194-c000.json.gz
>>> -rw-r--r-- 1 sanjeevmishra staff 0 Jun 29 08:40 _SUCCESS
>>>
>>> On Jun 30, 2020, at 5:37 AM, Gourav Sengupta <gourav.sengu...@gmail.com>
>>> wrote:
>>>
>>> Hi Sanjeev,
>>> that just gives 11 records from the sample that you have loaded to the
>>> JIRA tickets is it correct?
>>>
>>>
>>> Regards,
>>> Gourav Sengupta
>>>
>>> On Tue, Jun 30, 2020 at 1:25 PM Sanjeev Mishra <sanjeev.mis...@gmail.com>
>>> wrote:
>>>
>>>> There is not much code, I am just using spark-shell and reading the
>>>> data like so
>>>>
>>>> spark.time(spark.read.json("/data/small-anon/"))
>>>>
>>>> On Jun 30, 2020, at 3:53 AM, Gourav Sengupta <gourav.sengu...@gmail.com>
>>>> wrote:
>>>>
>>>> Hi Sanjeev,
>>>>
>>>> can you share the exact code that you are using to read the JSON files?
>>>> Currently I am getting only 11 records from the tar file that you have
>>>> attached with JIRA.
>>>>
>>>> Regards,
>>>> Gourav Sengupta
>>>>
>>>> On Mon, Jun 29, 2020 at 5:46 PM ArtemisDev <arte...@dtechspace.com>
>>>> wrote:
>>>>
>>>>> According to the spec, in addition to the line breaks, you should also
>>>>> put the nested object values in arrays instead of dictionaries. You may
>>>>> want to give a try and see if this would give you a better performance.
>>>>>
>>>>> Nevertheless, this still doesn't explain why Spark 2.4.6 outperforms
>>>>> 3.0. Hope the Databricks engineers will find an answer or bug fix soon.
>>>>>
>>>>> -- ND
>>>>> On 6/29/20 12:27 PM, Sanjeev Mishra wrote:
>>>>>
>>>>> The tar file that I have attached has bunch of json.zip files and this
>>>>> is the file that is being processed. Each line is self contained JSON as
>>>>> shown below
>>>>>
>>>>> zcat < part-00010-2558524a-1a1f-4f14-b027-4276a8143194-c000.json.gz |
>>>>>>> head -3
>>>>>>
>>>>>> {"id":"954e7819e91a11e981f60050569979b6","created":1570463599492,"properties":{"WANAccessType":"2","deviceClassifiers":["ARRIS
>>>>>>> HNC IGD","Annex F
>>>>>>> Gateway","Supports.Collect.Optimized.Workflow","Fast.Inform","Supports.TR98.Traceroute","InternetGatewayDevice:1.4","Motorola.ServiceType.IP","Supports
>>>>>>> Arris FastPath Speed
>>>>>>> Test","Arris.NVG468MQ.9.3.0h0","Wireless.Common.IGD.DualRadio","001E46.NVG468MQ.Is.WANIP","Device.Supports.HNC","Device.Type.RG","
>>>>>>> Arris.NVG4xx.Missing.CA
>>>>>>> <http://arris.nvg4xx.missing.ca/>","Supports.TR98.IPPing","Arris.NVG468MQ.9.3.0+","Wireless","ARRIS
>>>>>>> HNC IGD
>>>>>>> EUROPA","Arris.NVG.Wireless","WLAN.Radios.Action.Common.TR098","VoiceService:1.0","ConnecticutDeviceTypes","Device.Supports.SpeedTest","Motorola.Device.Supports.VoIP","Arris.NVG468MQ","Motorola.device","CaptivePortal:1","Arris.NVG4xx","All.TR069.RG.Devices","TraceRoute:1","Arris.NVG4xx.9.3.0+","datamodel.igd","Arris.NVG4xxQ","IPPing:1","Device.ServiceType.IP","001E46.NVG468MQ.Is.WANEth","Arris.NVG468MQ.9.2.4+","broken.device.no.notification"],"deviceType":"IGD","firstInform":"1570463619543","groups":["Self-Service
>>>>>>> Diagnostics","SLF-SRVC_DGNSTCS000","TCW - NVG4xx - First
>>>>>>> Contact"],"hardwareVersion":"NVG468MQ_0200240031004E","hncEnable":"0","lastBoot":"1587765844155","lastInform":"1590624062260","lastPeriodic":"1590624062260","manufacturerName":"Motorola","modelName":"NVG468MQ","productClass":"NVG468MQ","protocolVersion":"cwmp10","provisioningCode":"","softwareVersion":"9.3.0h0d55","tags":["default"],"timeZone":"EST+5EDT,M3.2.0/2,M11.1.0/2","wan":{"ethDuplexMode":"Full","ethSyncBitRate":"1000"},"wifi":[{"0":{"Enable":"1","SSID":"Frontier3136","SSIDAdvertisementEnabled":"1"},"1":{"Enable":"0","SSID":"Guest3136","SSIDAdvertisementEnabled":"1"},"2":{"Enable":"0","SSID":"Frontier3136_D2","SSIDAdvertisementEnabled":"1"},"3":{"Enable":"0","SSID":"Frontier3136_D3","SSIDAdvertisementEnabled":"1"},"4":{"Enable":"1","SSID":"Frontier3136_5G","SSIDAdvertisementEnabled":"1"},"5":{"Enable":"0","SSID":"Guest3136_5G","SSIDAdvertisementEnabled":"1"},"6":{"Enable":"1","SSID":"Frontier3136_5G-TV","SSIDAdvertisementEnabled":"0"},"7":{"Enable":"0","SSID":"Frontier3136_5G_D2","SSIDAdvertisementEnabled":"1"}}]},"ts":1590624062260}
>>>>>>
>>>>>> {"id":"bf0448736d09e2e677ea383ef857d5bc","created":1517843609967,"properties":{"WANAccessType":"2","arrisNvgDbCheck":"1:success","deviceClassifiers":["ARRIS
>>>>>>> HNC IGD","Annex F
>>>>>>> Gateway","Supports.Collect.Optimized.Workflow","Fast.Inform","InternetGatewayDevice:1.4","Supports.TR98.Traceroute","Supports
>>>>>>> Arris FastPath Speed
>>>>>>> Test","Motorola.ServiceType.IP","Arris.NVG468MQ.9.3.0h0","Wireless.Common.IGD.DualRadio","001E46.NVG468MQ.Is.WANIP","Device.Supports.HNC","Device.Type.RG","
>>>>>>> Arris.NVG4xx.Missing.CA
>>>>>>> <http://arris.nvg4xx.missing.ca/>","Supports.TR98.IPPing","Arris.NVG468MQ.9.3.0+","Wireless","ARRIS
>>>>>>> HNC IGD
>>>>>>> EUROPA","Arris.NVG.Wireless","VoiceService:1.0","WLAN.Radios.Action.Common.TR098","ConnecticutDeviceTypes","Device.Supports.SpeedTest","Motorola.Device.Supports.VoIP","Arris.NVG468MQ","Motorola.device","CaptivePortal:1","Arris.NVG4xx","All.TR069.RG.Devices","TraceRoute:1","Arris.NVG4xx.9.3.0+","datamodel.igd","Arris.NVG4xxQ","IPPing:1","Device.ServiceType.IP","001E46.NVG468MQ.Is.WANEth","Arris.NVG468MQ.9.2.4+","broken.device.no.notification"],"deviceType":"IGD","firstInform":"1517843629132","groups":["Total
>>>>>>> Control","GPON_100M_100M","Self-Service
>>>>>>> Diagnostics","HSI","SLF-SRVC_DGNSTCS000","HS002","TTL_CNTRL000","GPN_100M_100M001"],"hardwareVersion":"NVG468MQ_0200240031004E","hncEnable":"0","lastBoot":"1590196375084","lastInform":"1590624060253","lastPeriodic":"1590624060253","manufacturerName":"Motorola","modelName":"NVG468MQ","productClass":"NVG468MQ","protocolVersion":"cwmp10","provisioningCode":"","softwareVersion":"9.3.0h0d55","tags":["default"],"timeZone":"EST+5EDT,M3.2.0/2,M11.1.0/2","wan":{"ethDuplexMode":"Full","ethSyncBitRate":"1000"},"wifi":[{"0":{"Enable":"1","SSID":"NE-TB12-GOAT-2G","SSIDAdvertisementEnabled":"1"},"1":{"Enable":"1","SSID":"TP-Link_extender_2.4GHz","SSIDAdvertisementEnabled":"1"},"2":{"Enable":"0","SSID":"Frontier5360_D2","SSIDAdvertisementEnabled":"1"},"3":{"Enable":"0","SSID":"Frontier5360_D3","SSIDAdvertisementEnabled":"1"},"4":{"Enable":"1","SSID":"NE-TB12-GOAT-5G","SSIDAdvertisementEnabled":"1"},"5":{"Enable":"0","SSID":"Guest5360_5G","SSIDAdvertisementEnabled":"1"},"6":{"Enable":"1","SSID":"Frontier5360_5G-TV","SSIDAdvertisementEnabled":"0"},"7":{"Enable":"0","SSID":"Frontier5360_5G_D2","SSIDAdvertisementEnabled":"1"}}]},"ts":1590624060253}
>>>>>>
>>>>>> {"id":"1512b1b8526211e9acf100505699063c","created":1553891682535,"properties":{"WANAccessType":"2","arrisNvgDbCheck":"1:success","deviceClassifiers":["ARRIS
>>>>>>> HNC IGD","Annex F
>>>>>>> Gateway","Supports.Collect.Optimized.Workflow","Fast.Inform","InternetGatewayDevice:1.4","Supports.TR98.Traceroute","Motorola.ServiceType.IP","Supports
>>>>>>> Arris FastPath Speed
>>>>>>> Test","Arris.NVG468MQ.9.3.0h0","Wireless.Common.IGD.DualRadio","001E46.NVG468MQ.Is.WANIP","Device.Supports.HNC","
>>>>>>> Arris.NVG4xx.Missing.CA
>>>>>>> <http://arris.nvg4xx.missing.ca/>","Device.Type.RG","Supports.TR98.IPPing","Arris.NVG468MQ.9.3.0+","Wireless","ARRIS
>>>>>>> HNC IGD
>>>>>>> EUROPA","Arris.NVG.Wireless","WLAN.Radios.Action.Common.TR098","VoiceService:1.0","ConnecticutDeviceTypes","Device.Supports.SpeedTest","Motorola.Device.Supports.VoIP","Arris.NVG468MQ","Motorola.device","Arris.NVG4xx","CaptivePortal:1","All.TR069.RG.Devices","TraceRoute:1","Arris.NVG4xx.9.3.0+","datamodel.igd","Arris.NVG4xxQ","IPPing:1","Device.ServiceType.IP","001E46.NVG468MQ.Is.WANEth","Arris.NVG468MQ.9.2.4+","broken.device.no.notification"],"deviceType":"IGD","firstInform":"1553891708717","groups":["Total
>>>>>>> Control","HSI","Self-Service
>>>>>>> Diagnostics","SLF-SRVC_DGNSTCS000","HS004","TTL_CNTRL000","TCW - NVG4xx
>>>>>>> -
>>>>>>> First Contact","GPON_200M_200M","TCW
>>>>>>> Enabled","GPN_200M_200M000"],"hardwareVersion":"NVG468MQ_0200240031004E","hncEnable":"1","lastBoot":"1590537703734","lastInform":"1590624061415","lastPeriodic":"1590624061415","manufacturerName":"Motorola","modelName":"NVG468MQ","productClass":"NVG468MQ","protocolVersion":"cwmp10","provisioningCode":"","softwareVersion":"9.3.0h0d55","tags":["default"],"timeZone":"EST+5EDT,M3.2.0/2,M11.1.0/2","wan":{"ethDuplexMode":"Full","ethSyncBitRate":"1000"},"wifi":[{"0":{"Enable":"1","SSID":"Frontier7968","SSIDAdvertisementEnabled":"1"},"1":{"Enable":"0","SSID":"Guest7968","SSIDAdvertisementEnabled":"1"},"2":{"Enable":"0","SSID":"Frontier7968_D2","SSIDAdvertisementEnabled":"1"},"3":{"Enable":"0","SSID":"Frontier7968_D3","SSIDAdvertisementEnabled":"1"},"4":{"Enable":"1","SSID":"Frontier7968","SSIDAdvertisementEnabled":"1"},"5":{"Enable":"0","SSID":"Guest7968_5G","SSIDAdvertisementEnabled":"1"},"6":{"Enable":"1","SSID":"Frontier7968_5G-TV","SSIDAdvertisementEnabled":"0"},"7":{"Enable":"0","SSID":"Frontier7968_5G_D2","SSIDAdvertisementEnabled":"1"}}]},"ts":1590624061415}
>>>>>>
>>>>>>
>>>>> On Mon, Jun 29, 2020 at 9:20 AM ArtemisDev <arte...@dtechspace.com>
>>>>> wrote:
>>>>>
>>>>>> Could you please share your input file instead of output files on
>>>>>> that ticket? Not sure if you were following the specific file format
>>>>>> requirement for JSON in Spark. The following is a snippet from the Spark
>>>>>> online doc:
>>>>>>
>>>>>> Note that the file that is offered as *a json file* is not a typical
>>>>>> JSON file. Each line must contain a separate, self-contained valid JSON
>>>>>> object. For more information, please see JSON Lines text format,
>>>>>> also called newline-delimited JSON <http://jsonlines.org/>.
>>>>>>
>>>>>> For a regular multi-line JSON file, set the multiLine option to true.
>>>>>>
>>>>>> -- ND
>>>>>> On 6/29/20 11:55 AM, Sanjeev Mishra wrote:
>>>>>>
>>>>>> Done. https://issues.apache.org/jira/browse/SPARK-32130
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Mon, Jun 29, 2020 at 8:21 AM Maxim Gekk <maxim.g...@databricks.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hello Sanjeev,
>>>>>>>
>>>>>>> It is hard to troubleshoot the issue without input files. Could you
>>>>>>> open an JIRA ticket at https://issues.apache.org/jira/projects/SPARK and
>>>>>>> attach the JSON files there (or samples or code which generates JSON
>>>>>>> files)?
>>>>>>>
>>>>>>> Maxim Gekk
>>>>>>> Software Engineer
>>>>>>> Databricks, Inc.
>>>>>>>
>>>>>>>
>>>>>>> On Mon, Jun 29, 2020 at 6:12 PM Sanjeev Mishra <
>>>>>>> sanjeev.mis...@gmail.com> wrote:
>>>>>>>
>>>>>>>> It has read everything. As you notice the timing of count is still
>>>>>>>> smaller in Spark 2.4
>>>>>>>>
>>>>>>>> Spark 2.4
>>>>>>>>
>>>>>>>> scala> spark.time(spark.read.json("/data/20200528"))
>>>>>>>> Time taken: 19691 ms
>>>>>>>> res61: org.apache.spark.sql.DataFrame = [created: bigint, id:
>>>>>>>> string ... 5 more fields]
>>>>>>>>
>>>>>>>> scala> spark.time(res61.count())
>>>>>>>> Time taken: 7113 ms
>>>>>>>> res64: Long = 2605349
>>>>>>>>
>>>>>>>> Spark 3.0
>>>>>>>> scala> spark.time(spark.read.json("/data/20200528"))
>>>>>>>> 20/06/29 08:06:53 WARN package: Truncated the string representation
>>>>>>>> of a plan since it was too large. This behavior can be adjusted by
>>>>>>>> setting
>>>>>>>> 'spark.sql.debug.maxToStringFields'.
>>>>>>>> Time taken: 849652 ms
>>>>>>>> res0: org.apache.spark.sql.DataFrame = [created: bigint, id: string
>>>>>>>> ... 5 more fields]
>>>>>>>>
>>>>>>>> scala> spark.time(res0.count())
>>>>>>>> Time taken: 8201 ms
>>>>>>>> res2: Long = 2605349
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> On Mon, Jun 29, 2020 at 7:45 AM ArtemisDev <arte...@dtechspace.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Could you share your code? Are you sure you Spark 2.4 cluster had
>>>>>>>>> indeed read anything? Looks like the Input size field is empty under
>>>>>>>>> 2.4.
>>>>>>>>>
>>>>>>>>> -- ND
>>>>>>>>> On 6/27/20 7:58 PM, Sanjeev Mishra wrote:
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> I have large amount of json files that Spark can read in 36
>>>>>>>>> seconds but Spark 3.0 takes almost 33 minutes to read the same. On
>>>>>>>>> closer
>>>>>>>>> analysis, looks like Spark 3.0 is choosing different DAG than Spark
>>>>>>>>> 2.0.
>>>>>>>>> Does anyone have any idea what is going on? Is there any configuration
>>>>>>>>> problem with Spark 3.0.
>>>>>>>>>
>>>>>>>>> Here are the details:
>>>>>>>>>
>>>>>>>>> *Spark 2.4*
>>>>>>>>>
>>>>>>>>> Summary Metrics for 2203 Completed Tasks
>>>>>>>>> <http://10.0.0.8:4040/stages/stage/?id=0&attempt=0#tasksTitle>
>>>>>>>>> Metric Min 25th percentile Median 75th percentile Max
>>>>>>>>> Duration 0.0 ms 0.0 ms 0.0 ms 1.0 ms 62.0 ms
>>>>>>>>> GC Time 0.0 ms 0.0 ms 0.0 ms 0.0 ms 11.0 ms
>>>>>>>>> Showing 1 to 2 of 2 entries
>>>>>>>>> Aggregated Metrics by Executor
>>>>>>>>> Show 20 40 60 100 All entries
>>>>>>>>> Search:
>>>>>>>>> Executor ID Logs Address Task Time Total Tasks Failed Tasks Killed
>>>>>>>>> Tasks Succeeded Tasks Blacklisted
>>>>>>>>> driver
>>>>>>>>> 10.0.0.8:49159 36 s 2203 0 0 2203 false
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> *Spark 3.0*
>>>>>>>>>
>>>>>>>>> Summary Metrics for 8 Completed Tasks
>>>>>>>>> <http://10.0.0.8:4040/stages/stage/?id=1&attempt=0&task.eventTimelinePageNumber=1&task.eventTimelinePageSize=47#tasksTitle>
>>>>>>>>> Metric Min 25th percentile Median 75th percentile Max
>>>>>>>>> Duration 3.8 min 4.0 min 4.1 min 4.4 min 5.0 min
>>>>>>>>> GC Time 3 s 3 s 3 s 4 s 4 s
>>>>>>>>> Input Size / Records 15.6 MiB / 51028 16.2 MiB / 53303 16.8 MiB /
>>>>>>>>> 55259 17.8 MiB / 58148 20.2 MiB / 71624
>>>>>>>>> Showing 1 to 3 of 3 entries
>>>>>>>>> Aggregated Metrics by Executor
>>>>>>>>> Show 20 40 60 100 All entries
>>>>>>>>> Search:
>>>>>>>>> Executor ID Logs Address Task Time Total Tasks Failed Tasks Killed
>>>>>>>>> Tasks Succeeded Tasks Blacklisted Input Size / Records
>>>>>>>>> driver
>>>>>>>>> 10.0.0.8:50224 33 min 8 0 0 8 false 136.1 MiB / 451999
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> The DAG is also different
>>>>>>>>> Spark 2.0 DAG
>>>>>>>>>
>>>>>>>>> <Screenshot 2020-06-27 16.30.26.png>
>>>>>>>>>
>>>>>>>>> Spark 3.0 DAG
>>>>>>>>>
>>>>>>>>> <Screenshot 2020-06-27 16.32.32.png>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>
>>>
>>
load_anon_data.ipynb
Description: Binary data
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