Hi, 
I am testing the Spark 1.5.2 using a 4 nodes cluster, with 64G memory each, and 
one is master and 3 are workers. I am using Standalone mode.
Here is my spark-env.sh for settings:
export 
SPARK_LOCAL_DIRS=/data1/spark/local,/data2/spark/local,/data3/spark/localexport 
SPARK_MASTER_WEBUI_PORT=8081export SPARK_MASTER_IP=hostnameexport 
SPARK_MASTER_OPTS="-Dspark.deploy.defaultCores=3"export 
SPARK_WORKER_MEMORY=24gexport SPARK_WORKER_CORES=6export 
SPARK_WORKER_DIR=/tmp/spark/workexport SPARK_DRIVER_MEMORY=4gexport 
SPARK_EXECUTOR_MEMORY=2gexport SPARK_DAEMON_MEMORY=1g
I start my spark-shell in the following command: /opt/spark/bin/spark-shell 
--conf spark-executor_memory=8g --conf total_executor_cores=12
So each executor will have 8G as total heap, and allow up to 12/3=4 concurrence 
running.
Now I have a join between 2 dataframes:
val loadRaw = sqlContext.read.parquet("parquet_file_path")val trialRaw = 
loadRaw.filter(instr(loadRaw("event_list"), "202") > 
0).select("soid_e1","visid_high","visid_low","date_time")
// trialRaw.count is 1106, with 4 columns, very small data
val historyRaw = sqlContext.read.parquet("multi path parquet files")
// historyRaw.count is 13.5M, but with 500 columns
If I run the join this way:
val join1 = trialRaw.join(historyRaw, trialRaw("visid_high") <=> 
historyRaw("visid_high") &&  trialRaw("visid_low") <=> historyRaw("visid_low") 
&& trialRaw("date_time") > historyRaw("date_time"))join1.count
I can get the result after 20 minutes, in the above setting on this cluster. 
But I know the best way is to do the broadcast join, as the trailRaw is so 
small, so I did this:
val join2 = historyRaw.join(broadcast(trialRaw), trialRaw("visid_high") <=> 
historyRaw("visid_high") &&  trialRaw("visid_low") <=> historyRaw("visid_low") 
&& trialRaw("date_time") > historyRaw("date_time"))join2.count
To my surprise, in this case the executors got the java.lang.OutOfMemoryError: 
GC overhead limit exceeded, I know the historyRaw is around 500 columns, which 
is big. My end result does want to include all the 500 columns in the join, as 
long as the join conditions match. But the count of row will be limited. Also 
the trialRaw is very small, only 1106 rows with 4 columns. This is the only 
data should be shipped to all the executors, and they just read the Parquet 
data and match by the join conditions.Why in this case it runs out of memory?
1) I know the OOM is not in Driver, as it has 4G heap, and the OOM error comes 
from the executor stderr2) I know I can add more heap, but I really want to 
know is that under the same settings, why join1 works, but join2 failed with 
such small data broadcasting?3) Is this related to how the parquet files being 
read by Spark?4) It is my first time trying this broadcast join in Spark 
dataframe, as it is only available since 1.5. So maybe there is some pitfall I 
don't understand how it works.
This is the stdout, it shows the full GC, then OOM:
2016-02-22T11:54:44.014-0500: [Full GC [PSYoungGen: 
1994715K->1943403K(2395136K)] [ParOldGen: 5592413K->5592495K(5592576K)] 
7587129K->7535898K(7987712K) [PSPermGen: 63554K->63548K(64000K)], 3.7543230 
secs] [Times: user=50.72 sys=0.06, real=3.75 secs] 
2016-02-22T11:54:47.803-0500: [Full GC [PSYoungGen: 
1994752K->1960598K(2395136K)] [ParOldGen: 5592495K->5592485K(5592576K)] 
7587247K->7553083K(7987712K) [PSPermGen: 63690K->63587K(64000K)], 30.9218790 
secs] [Times: user=543.07 sys=2.50, real=30.91 secs] 
Heap
 PSYoungGen      total 2395136K, used 1988191K [0x0000000755500000, 
0x0000000800000000, 0x0000000800000000)
  eden space 1994752K, 99% used 
[0x0000000755500000,0x00000007cea97c30,0x00000007cf100000)
  from space 400384K, 0% used 
[0x00000007cf100000,0x00000007cf100000,0x00000007e7800000)
  to   space 390656K, 0% used 
[0x00000007e8280000,0x00000007e8280000,0x0000000800000000)
 ParOldGen       total 5592576K, used 5592485K [0x00000005fff80000, 
0x0000000755500000, 0x0000000755500000)
  object space 5592576K, 99% used 
[0x00000005fff80000,0x00000007554e94e8,0x0000000755500000)
 PSPermGen       total 64000K, used 63710K [0x00000005eff80000, 
0x00000005f3e00000, 0x00000005fff80000)
  object space 64000K, 99% used 
[0x00000005eff80000,0x00000005f3db7ac8,0x00000005f3e00000)
This is the executor log:16/02/22 11:53:41 INFO 
hadoop.InternalParquetRecordReader: RecordReader initialized will read a total 
of 0 records.
16/02/22 11:54:27 INFO parquet.ParquetRelation$$anonfun$buildScan$1$$anon$1: 
Input split: ParquetInputSplit{part: 
hdfs://host:9000/data/event_parquet/2015/08/21/event_parquet-2015-08-21-20150831195822.parquet
 start: 134217728 end: 268435456 length: 134217728 hosts: []}
16/02/22 11:54:42 WARN hadoop.ParquetRecordReader: Can not initialize counter 
due to context is not a instance of TaskInputOutputContext, but is 
org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
16/02/22 11:54:42 ERROR executor.Executor: Managed memory leak detected; size = 
33554432 bytes, TID = 206
16/02/22 11:54:42 ERROR executor.Executor: Exception in task 1.0 in stage 4.0 
(TID 206)
java.lang.OutOfMemoryError: GC overhead limit exceeded
        at sun.nio.cs.StreamEncoder.write(StreamEncoder.java:133)
        at java.io.OutputStreamWriter.write(OutputStreamWriter.java:220)
        at java.io.Writer.write(Writer.java:157)
Below is the join plan:
scala> join2.explain16/02/23 17:11:03 INFO storage.MemoryStore: 
ensureFreeSpace(156640) called with curMem=252597, maxMem=222302306316/02/23 
17:11:03 INFO storage.MemoryStore: Block broadcast_5 stored as values in memory 
(estimated size 153.0 KB, free 2.1 GB)16/02/23 17:11:03 INFO 
storage.MemoryStore: ensureFreeSpace(16125) called with curMem=409237, 
maxMem=222302306316/02/23 17:11:03 INFO storage.MemoryStore: Block 
broadcast_5_piece0 stored as bytes in memory (estimated size 15.7 KB, free 2.1 
GB)16/02/23 17:11:03 INFO storage.BlockManagerInfo: Added broadcast_5_piece0 in 
memory on 10.20.180.71:46082 (size: 15.7 KB, free: 2.1 GB)16/02/23 17:11:03 
INFO spark.SparkContext: Created broadcast 5 from explain at 
<console>:2816/02/23 17:11:03 INFO storage.MemoryStore: ensureFreeSpace(156640) 
called with curMem=425362, maxMem=222302306316/02/23 17:11:03 INFO 
storage.MemoryStore: Block broadcast_6 stored as values in memory (estimated 
size 153.0 KB, free 2.1 GB)16/02/23 17:11:03 INFO storage.MemoryStore: 
ensureFreeSpace(16125) called with curMem=582002, maxMem=222302306316/02/23 
17:11:03 INFO storage.MemoryStore: Block broadcast_6_piece0 stored as bytes in 
memory (estimated size 15.7 KB, free 2.1 GB)16/02/23 17:11:03 INFO 
storage.BlockManagerInfo: Added broadcast_6_piece0 in memory on 
10.20.180.71:46082 (size: 15.7 KB, free: 2.1 GB)16/02/23 17:11:03 INFO 
spark.SparkContext: Created broadcast 6 from explain at <console>:28== Physical 
Plan ==Filter (((visid_high#460L <=> visid_high#948L) && (visid_low#461L <=> 
visid_low#949L)) && (date_time#25L > date_time#513L)) CartesianProduct  Scan 
ParquetRelation[hdfs://xxx/data/event_parquet/2015/08/13,hdfs://xxx/data/event_parquet/2015/08/14,hdfs://xxx/data/event_parquet/2015/08/21][accept_language#488,browser#489L,browser_height#490,browser_width#491,campaign#492,c_color#493,channel#494,click_action#495,click_action_type#496,click_context#497,click_context_type#498,click_sourceId#499L,click_tag#500,code_ver#501,color#502,connection_type#503,cookies#504,country#505,ct_connect_type#506,currency#507,curr_factor#508,curr_rate#509,cust_hit_time_gmt#510L,cust_visid#511,daily_visitor#512,date_time#513L,domain#514,duplicated_from#515,duplicate_events#516,duplicate_purchase#517,soid_e1#518,lpuid_e2#519,customer_status_e3#520,partner_uid_e4#521,ab_split_group_e5#522,application_e6#523,page_name_e7#524,originating_page_e8#525,email_signup_e9#526,survey_signup_e10#527,event_signup_e11#528,email_paying_e12#529,survey_paying_e13#530,event_paying_e14#531,image_hosting_paying_e15#532,archive_paying_e16#533,internal_search_query_e17#534,internal_search_page_e18#535,download_file_name_e19#536,download_file_page_e20#537,campaign_stacking_e21#538,improveit_category_e22#539,improveit_keyword_e23#540,omtr_vi_id_e24#541,aprimo_id_e25#542,link_name_e26#543,user_agent_e27#544,page_type_desc_e28#545,site_region_language_e29#546,social_campaigns_signup_e30#547,session_history_e31#548,external_data_source_type_e32#549,rmc_code_e33#550,video_name_e34#551,video_segment_e35#552,video_content_e36#553,free_user_prod_name_e37#554,web_voc_id_e38#555,ic_code_e39#556,hit_count_e40#557,signup_form_orig_page_e41#558,e42#559,e43#560,e44#561,pyament_plan_type_e45#562,purchased_product_name_e46#563,purchased_product_category_e47#564,link_loc_name_e48#565,link_pg_loc_name_e49#566,email_test_counter_e50#567,email_preview_counter_e51#568,email_send_counter_e52#569,time_emcf_tpicker_e53#570,time_emcf_editor_e54#571,time_emcf_scheduler_e55#572,time_emcf_build_e56#573,time_emcf_send_success_e57#574,timestamp_e58#575,templates_returned_e59#576,email_agent_id_e60#577,template_name_e61#578,template_hit_counter_e62#579,template_taxonomy_e63#580,email_home_taxonomy_e64#581,email_home_hit_counter_e65#582,editor_taxonomy_t66#583,editor_block_detail_e67#584,editor_block_title_e68#585,editor_hit_counter_e69#586,email_insert_detail_e70#587,email_creation_source_e71#588,e72#589,e73#590,e74#591,adlens_ef_id_e75#592,event_list#593,exclude_hit#594,first_hit_pagename#595,first_hit_page_url#596,first_hit_referrer#597,first_hit_time_gmt#598L,geo_city#599,geo_country#600,geo_dma#601,geo_region#602,geo_zip#603,hier1#604,hier2#605,hier3#606,hier4#607,hier5#608,hitid_high#609L,hitid_low#610L,hit_source#611,hit_time_gmt#612L,homepage#613,hourly_visitor#614,ip#615,ip2#616,java_enabled#617,javascript#618,j_jscript#619,language#620,last_hit_time_gmt#621L,last_purchase_num#622,last_purchase_time_gmt#623L,mobile_id#624L,monthly_visitor#625L,mvvar1#626,mvvar2#627,mvvar3#628,namespace#629,new_visit#630,os#631L,page_event#632,page_event_var1#633,page_event_var2#634,page_event_var3#635,page_name#636,page_type#637,page_url#638,paid_search#639,partner_plugins#640,persistent_cookie#641,plugins#642,post_browser_height#643,post_browser_width#644,post_campaign#645,post_channel#646,post_cookies#647,post_currency#648,post_cust_hit_time_gmt#649L,post_cust_visid#650,soid_pe1#651,lpuid_pe2#652,customer_status_pe3#653,partner_uid_pe4#654,ab_split_group_pe5#655,application_pe6#656,page_name_pe7#657,pe8#658,pe9#659,pe10#660,pe11#661,pe12#662,pe13#663,pe14#664,pe15#665,pe16#666,pe17#667,pe18#668,pe19#669,pe20#670,campaign_stacking_pe21#671,category_name_pe22#672,improvedit_keyword_pe23#673,omtr_vi_id_pe24#674,aprimo_id_pe25#675,link_name_pe26#676,pe27#677,pe28#678,site_region_language_pe29#679,pe30#680,session_history_pe31#681,pe32#682,rmc_code_pe33#683,video_name_pe34#684,pe35#685,pe36#686,pe37#687,pe38#688,ic_code_pe39#689,template_hit_crt_pe40#690,pe41#691,pe42#692,pe43#693,pe44#694,payment_plan_type_pe45#695,purchased_product_name_pe46#696,purchased_product_category_pe47#697,link_loc_name_pe48#698,link_pg_loc_name_pe49#699,email_test_ctr_pe50#700,email_preview_ctr_pe51#701,email_send_ctr_pe52#702,time_emcf_tpicker_pe53#703,time_emcf_editor_pe54#704,time_emcf_scheduler_pe55#705,time_emcf_build_pe56#706,time_emcf_send_success_pe57#707,pe58#708,pe59#709,email_agent_id_pe60#710,template_name_pe61#711,template_hit_ctr_pe62#712,template_taxonomy_pe63#713,email_home_taxonomy_pe64#714,email_home_hit_crt_pe65#715,editor_taxonomy_pe66#716,editor_block_detail_pe67#717,editor_block_title_pe68#718,editor_hit_crt_pe69#719,editor_insert_detail_pe70#720,email_creation_source_pe71#721,pe72#722,pe73#723,pe74#724,adlens_ef_id_pe75#725,post_event_list#726,post_hier1#727,post_hier2#728,post_hier3#729,post_hier4#730,post_hier5#731,post_java_enabled#732,post_keywords#733,post_mvvar1#734,post_mvvar2#735,post_mvvar3#736,post_page_event#737,post_page_event_var1#738,post_page_event_var2#739,post_page_event_var3#740,post_pagename#741,post_pagename_no_url#742,post_page_type#743,post_page_url#744,post_partner_plugins#745,post_persistent_cookie#746,post_product_list#747,soid_pp1#748,lpuid_pp2#749,custom_status_pp3#750,internal_external_pp4#751,ab_split_group_pp5#752,application_pp6#753,internal_search_query_pp7#754,pp8#755,pp9#756,pp10#757,pp11#758,sops_pp12#759,pp13#760,pp14#761,pp15#762,pp16#763,pp17#764,pp18#765,signup_form_pp19#766,pp20#767,pp21#768,category_name_pp22#769,improveit_keyword_pp23#770,pp24#771,sops_hero_name_pp25#772,video_name_pp26#773,pp27#774,topnav_link_name_pp28#775,pp29#776,pp30#777,pp31#778,pp32#779,pp33#780,pp34#781,pp35#782,pp36#783,pp37#784,pp38#785,pp39#786,link_name_pp40#787,link_loc_name_pp41#788,link_pg_loc_name_pp42#789,pp43#790,pp44#791,pp45#792,pp46#793,pp47#794,pp48#795,template_taxonomy_pp49#796,template_name_pp50#797,email_home_taxonomy_pp51#798,editor_taxonomy_pp52#799,kb_details_pp53#800,pp54#801,pp55#802,pp56#803,pp57#804,pp58#805,pp59#806,pp60#807,pp61#808,pp62#809,pp63#810,pp64#811,pp65#812,pp66#813,pp67#814,pp68#815,pp69#816,pp70#817,pp71#818,pp72#819,pp73#820,pp74#821,pp75#822,post_purchaseid#823,post_referrer#824,post_search_engine#825,post_state#826,post_survey#827,post_tnt#828,post_transactionid#829,post_t_time_info#830,post_visid_high#831L,post_visid_low#832L,post_visid_type#833,post_zip#834,p_plugins#835,prev_page#836L,product_list#837,product_merchandising#838,soid_p1#839,p2#840,p3#841,p4#842,p5#843,p6#844,p7#845,p8#846,p9#847,p10#848,p11#849,p12#850,p13#851,p14#852,p15#853,p16#854,p17#855,p18#856,signup_form_p19#857,p20#858,p21#859,improveit_category_p22#860,improveit_keyword_p23#861,test_drive_type_p24#862,sops_hero_name_p25#863,video_name_p26#864,previous_page_name_p27#865,topnav_link_name_p28#866,p29#867,marketplace_detail_p30#868,p31#869,p32#870,p33#871,p34#872,p35#873,p36#874,p37#875,p38#876,p39#877,link_name_p40#878,link_loc_name_p41#879,link_pg_loc_name_p42#880,p43#881,p44#882,p45#883,p46#884,p47#885,timestamp_p48#886,template_taxonomy_p49#887,template_name_p50#888,email_home_taxonomy_p51#889,editor_taxonomy_p52#890,kb_detail_p53#891,stock_image_name_p54#892,sent_template_name_p55#893,p56#894,p57#895,p58#896,p59#897,p60#898,p61#899,p62#900,p63#901,p64#902,p65#903,p66#904,p67#905,p68#906,p69#907,p70#908,p71#909,p72#910,p73#911,p74#912,p75#913,purchaseid#914,quarterly_visitor#915L,ref_domain#916,referrer#917,ref_type#918,resolution#919,sampled_hit#920,search_engine#921,search_page_num#922L,secondary_hit#923,service#924,sourceid#925L,s_resolution#926,state#927,stats_server#928,tnt#929,tnt_post_vista#930,transactionid#931,truncated_hit#932,t_time_info#933,ua_color#934,ua_os#935,ua_pixels#936,user_agent#937,user_hash#938L,userid#939L,username#940,user_server#941,va_closer_detail#942,va_closer_id#943L,va_finder_detail#944,va_finder_id#945L,va_instance_event#946,va_new_engagement#947,visid_high#948L,visid_low#949L,visid_new#950,visid_timestamp#951L,visid_type#952,visit_keywords#953,visit_num#954,visit_page_num#955,visit_referrer#956,visit_search_engine#957,visit_start_pagename#958,visit_start_page_url#959,visit_start_time_gmt#960L,weekly_visitor#961,yearly_visitor#962L,zip#963,browser_desc#964,color_desc#965,connection_type_desc#966,country_desc#967,event_list_desc#968,javascript_desc#969,language_desc#970,os_desc#971,plugins_desc#972,ref_type_desc#973,resolution_desc#974,search_engine_desc#975]
  ConvertToSafe   TungstenProject 
[soid_e1#30,visid_high#460L,visid_low#461L,date_time#25L]    Filter 
(instr(event_list#105,202) > 0)     Scan 
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