Effect of garbage collection in iterative algorithms on Spark: an experimental analysis

Abstract Spark is one of the most widely used systems for the distributed processing of big data. Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on...
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Autor*in:	Kang, Minseo [verfasserIn] Lee, Jae-Gil [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Spark Garbage collection Iterative algorithms Distributed processing Storage level

Übergeordnetes Werk:	Enthalten in: The journal of supercomputing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 76(2020), 9 vom: 16. Jan., Seite 7204-7218
Übergeordnetes Werk:	volume:76 ; year:2020 ; number:9 ; day:16 ; month:01 ; pages:7204-7218

Links:	Volltext

DOI / URN:	10.1007/s11227-020-03150-z

Katalog-ID:	SPR040477894

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520			\|a Abstract Spark is one of the most widely used systems for the distributed processing of big data. Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on iterative algorithms. In an iterative algorithm, garbage collection has more performance impact than other workloads because the algorithm repeatedly loads and deletes data in the main memory through multiple iterations. Spark provides three caching mechanisms which are “disk cache,” “memory cache,” and “no cache” to keep the unchanged data across iterations. In this paper, we provide an in-depth experimental analysis of the effect of garbage collection on the overall performance depending on the caching mechanisms of Spark with various combinations of algorithms and datasets. The experimental results show that garbage collection accounts for 16–47% of the total elapsed time of running iterative algorithms on Spark and that the memory cache is no less advantageous in terms of garbage collection than the disk cache. We expect the results of this paper to serve as a guide for the tuning of garbage collection in the running of iterative algorithms on Spark.
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allfields	10.1007/s11227-020-03150-z doi (DE-627)SPR040477894 (SPR)s11227-020-03150-z-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Kang, Minseo verfasserin aut Effect of garbage collection in iterative algorithms on Spark: an experimental analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spark is one of the most widely used systems for the distributed processing of big data. Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on iterative algorithms. In an iterative algorithm, garbage collection has more performance impact than other workloads because the algorithm repeatedly loads and deletes data in the main memory through multiple iterations. Spark provides three caching mechanisms which are “disk cache,” “memory cache,” and “no cache” to keep the unchanged data across iterations. In this paper, we provide an in-depth experimental analysis of the effect of garbage collection on the overall performance depending on the caching mechanisms of Spark with various combinations of algorithms and datasets. The experimental results show that garbage collection accounts for 16–47% of the total elapsed time of running iterative algorithms on Spark and that the memory cache is no less advantageous in terms of garbage collection than the disk cache. We expect the results of this paper to serve as a guide for the tuning of garbage collection in the running of iterative algorithms on Spark. Spark (dpeaa)DE-He213 Garbage collection (dpeaa)DE-He213 Iterative algorithms (dpeaa)DE-He213 Distributed processing (dpeaa)DE-He213 Storage level (dpeaa)DE-He213 Lee, Jae-Gil verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 76(2020), 9 vom: 16. Jan., Seite 7204-7218 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:76 year:2020 number:9 day:16 month:01 pages:7204-7218 https://dx.doi.org/10.1007/s11227-020-03150-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.20 ASE AR 76 2020 9 16 01 7204-7218
spelling	10.1007/s11227-020-03150-z doi (DE-627)SPR040477894 (SPR)s11227-020-03150-z-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Kang, Minseo verfasserin aut Effect of garbage collection in iterative algorithms on Spark: an experimental analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spark is one of the most widely used systems for the distributed processing of big data. Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on iterative algorithms. In an iterative algorithm, garbage collection has more performance impact than other workloads because the algorithm repeatedly loads and deletes data in the main memory through multiple iterations. Spark provides three caching mechanisms which are “disk cache,” “memory cache,” and “no cache” to keep the unchanged data across iterations. In this paper, we provide an in-depth experimental analysis of the effect of garbage collection on the overall performance depending on the caching mechanisms of Spark with various combinations of algorithms and datasets. The experimental results show that garbage collection accounts for 16–47% of the total elapsed time of running iterative algorithms on Spark and that the memory cache is no less advantageous in terms of garbage collection than the disk cache. We expect the results of this paper to serve as a guide for the tuning of garbage collection in the running of iterative algorithms on Spark. Spark (dpeaa)DE-He213 Garbage collection (dpeaa)DE-He213 Iterative algorithms (dpeaa)DE-He213 Distributed processing (dpeaa)DE-He213 Storage level (dpeaa)DE-He213 Lee, Jae-Gil verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 76(2020), 9 vom: 16. Jan., Seite 7204-7218 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:76 year:2020 number:9 day:16 month:01 pages:7204-7218 https://dx.doi.org/10.1007/s11227-020-03150-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.20 ASE AR 76 2020 9 16 01 7204-7218
allfields_unstemmed	10.1007/s11227-020-03150-z doi (DE-627)SPR040477894 (SPR)s11227-020-03150-z-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Kang, Minseo verfasserin aut Effect of garbage collection in iterative algorithms on Spark: an experimental analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spark is one of the most widely used systems for the distributed processing of big data. Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on iterative algorithms. In an iterative algorithm, garbage collection has more performance impact than other workloads because the algorithm repeatedly loads and deletes data in the main memory through multiple iterations. Spark provides three caching mechanisms which are “disk cache,” “memory cache,” and “no cache” to keep the unchanged data across iterations. In this paper, we provide an in-depth experimental analysis of the effect of garbage collection on the overall performance depending on the caching mechanisms of Spark with various combinations of algorithms and datasets. The experimental results show that garbage collection accounts for 16–47% of the total elapsed time of running iterative algorithms on Spark and that the memory cache is no less advantageous in terms of garbage collection than the disk cache. We expect the results of this paper to serve as a guide for the tuning of garbage collection in the running of iterative algorithms on Spark. Spark (dpeaa)DE-He213 Garbage collection (dpeaa)DE-He213 Iterative algorithms (dpeaa)DE-He213 Distributed processing (dpeaa)DE-He213 Storage level (dpeaa)DE-He213 Lee, Jae-Gil verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 76(2020), 9 vom: 16. Jan., Seite 7204-7218 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:76 year:2020 number:9 day:16 month:01 pages:7204-7218 https://dx.doi.org/10.1007/s11227-020-03150-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.20 ASE AR 76 2020 9 16 01 7204-7218
allfieldsGer	10.1007/s11227-020-03150-z doi (DE-627)SPR040477894 (SPR)s11227-020-03150-z-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Kang, Minseo verfasserin aut Effect of garbage collection in iterative algorithms on Spark: an experimental analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spark is one of the most widely used systems for the distributed processing of big data. Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on iterative algorithms. In an iterative algorithm, garbage collection has more performance impact than other workloads because the algorithm repeatedly loads and deletes data in the main memory through multiple iterations. Spark provides three caching mechanisms which are “disk cache,” “memory cache,” and “no cache” to keep the unchanged data across iterations. In this paper, we provide an in-depth experimental analysis of the effect of garbage collection on the overall performance depending on the caching mechanisms of Spark with various combinations of algorithms and datasets. The experimental results show that garbage collection accounts for 16–47% of the total elapsed time of running iterative algorithms on Spark and that the memory cache is no less advantageous in terms of garbage collection than the disk cache. We expect the results of this paper to serve as a guide for the tuning of garbage collection in the running of iterative algorithms on Spark. Spark (dpeaa)DE-He213 Garbage collection (dpeaa)DE-He213 Iterative algorithms (dpeaa)DE-He213 Distributed processing (dpeaa)DE-He213 Storage level (dpeaa)DE-He213 Lee, Jae-Gil verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 76(2020), 9 vom: 16. Jan., Seite 7204-7218 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:76 year:2020 number:9 day:16 month:01 pages:7204-7218 https://dx.doi.org/10.1007/s11227-020-03150-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.20 ASE AR 76 2020 9 16 01 7204-7218
allfieldsSound	10.1007/s11227-020-03150-z doi (DE-627)SPR040477894 (SPR)s11227-020-03150-z-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Kang, Minseo verfasserin aut Effect of garbage collection in iterative algorithms on Spark: an experimental analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spark is one of the most widely used systems for the distributed processing of big data. Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on iterative algorithms. In an iterative algorithm, garbage collection has more performance impact than other workloads because the algorithm repeatedly loads and deletes data in the main memory through multiple iterations. Spark provides three caching mechanisms which are “disk cache,” “memory cache,” and “no cache” to keep the unchanged data across iterations. In this paper, we provide an in-depth experimental analysis of the effect of garbage collection on the overall performance depending on the caching mechanisms of Spark with various combinations of algorithms and datasets. The experimental results show that garbage collection accounts for 16–47% of the total elapsed time of running iterative algorithms on Spark and that the memory cache is no less advantageous in terms of garbage collection than the disk cache. We expect the results of this paper to serve as a guide for the tuning of garbage collection in the running of iterative algorithms on Spark. Spark (dpeaa)DE-He213 Garbage collection (dpeaa)DE-He213 Iterative algorithms (dpeaa)DE-He213 Distributed processing (dpeaa)DE-He213 Storage level (dpeaa)DE-He213 Lee, Jae-Gil verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 76(2020), 9 vom: 16. Jan., Seite 7204-7218 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:76 year:2020 number:9 day:16 month:01 pages:7204-7218 https://dx.doi.org/10.1007/s11227-020-03150-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.20 ASE AR 76 2020 9 16 01 7204-7218
language	English
source	Enthalten in The journal of supercomputing 76(2020), 9 vom: 16. Jan., Seite 7204-7218 volume:76 year:2020 number:9 day:16 month:01 pages:7204-7218
sourceStr	Enthalten in The journal of supercomputing 76(2020), 9 vom: 16. Jan., Seite 7204-7218 volume:76 year:2020 number:9 day:16 month:01 pages:7204-7218
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Spark Garbage collection Iterative algorithms Distributed processing Storage level
dewey-raw	004
isfreeaccess_bool	false
container_title	The journal of supercomputing
authorswithroles_txt_mv	Kang, Minseo @@aut@@ Lee, Jae-Gil @@aut@@
publishDateDaySort_date	2020-01-16T00:00:00Z
hierarchy_top_id	271350202
dewey-sort	14
id	SPR040477894
language_de	englisch
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Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on iterative algorithms. In an iterative algorithm, garbage collection has more performance impact than other workloads because the algorithm repeatedly loads and deletes data in the main memory through multiple iterations. Spark provides three caching mechanisms which are “disk cache,” “memory cache,” and “no cache” to keep the unchanged data across iterations. In this paper, we provide an in-depth experimental analysis of the effect of garbage collection on the overall performance depending on the caching mechanisms of Spark with various combinations of algorithms and datasets. 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author	Kang, Minseo
spellingShingle	Kang, Minseo ddc 004 bkl 54.20 misc Spark misc Garbage collection misc Iterative algorithms misc Distributed processing misc Storage level Effect of garbage collection in iterative algorithms on Spark: an experimental analysis
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title	Effect of garbage collection in iterative algorithms on Spark: an experimental analysis
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title_full	Effect of garbage collection in iterative algorithms on Spark: an experimental analysis
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title_sort	effect of garbage collection in iterative algorithms on spark: an experimental analysis
title_auth	Effect of garbage collection in iterative algorithms on Spark: an experimental analysis
abstract	Abstract Spark is one of the most widely used systems for the distributed processing of big data. Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on iterative algorithms. In an iterative algorithm, garbage collection has more performance impact than other workloads because the algorithm repeatedly loads and deletes data in the main memory through multiple iterations. Spark provides three caching mechanisms which are “disk cache,” “memory cache,” and “no cache” to keep the unchanged data across iterations. In this paper, we provide an in-depth experimental analysis of the effect of garbage collection on the overall performance depending on the caching mechanisms of Spark with various combinations of algorithms and datasets. The experimental results show that garbage collection accounts for 16–47% of the total elapsed time of running iterative algorithms on Spark and that the memory cache is no less advantageous in terms of garbage collection than the disk cache. We expect the results of this paper to serve as a guide for the tuning of garbage collection in the running of iterative algorithms on Spark.
abstractGer	Abstract Spark is one of the most widely used systems for the distributed processing of big data. Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on iterative algorithms. In an iterative algorithm, garbage collection has more performance impact than other workloads because the algorithm repeatedly loads and deletes data in the main memory through multiple iterations. Spark provides three caching mechanisms which are “disk cache,” “memory cache,” and “no cache” to keep the unchanged data across iterations. In this paper, we provide an in-depth experimental analysis of the effect of garbage collection on the overall performance depending on the caching mechanisms of Spark with various combinations of algorithms and datasets. The experimental results show that garbage collection accounts for 16–47% of the total elapsed time of running iterative algorithms on Spark and that the memory cache is no less advantageous in terms of garbage collection than the disk cache. We expect the results of this paper to serve as a guide for the tuning of garbage collection in the running of iterative algorithms on Spark.
abstract_unstemmed	Abstract Spark is one of the most widely used systems for the distributed processing of big data. Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on iterative algorithms. In an iterative algorithm, garbage collection has more performance impact than other workloads because the algorithm repeatedly loads and deletes data in the main memory through multiple iterations. Spark provides three caching mechanisms which are “disk cache,” “memory cache,” and “no cache” to keep the unchanged data across iterations. In this paper, we provide an in-depth experimental analysis of the effect of garbage collection on the overall performance depending on the caching mechanisms of Spark with various combinations of algorithms and datasets. The experimental results show that garbage collection accounts for 16–47% of the total elapsed time of running iterative algorithms on Spark and that the memory cache is no less advantageous in terms of garbage collection than the disk cache. We expect the results of this paper to serve as a guide for the tuning of garbage collection in the running of iterative algorithms on Spark.
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container_issue	9
title_short	Effect of garbage collection in iterative algorithms on Spark: an experimental analysis
url	https://dx.doi.org/10.1007/s11227-020-03150-z
remote_bool	true
author2	Lee, Jae-Gil
author2Str	Lee, Jae-Gil
ppnlink	271350202
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s11227-020-03150-z
up_date	2024-07-03T16:15:56.745Z
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