On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms
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The document discusses the performance of Hadoop under hotspots compared to Big Data replay platforms, highlighting the need for optimization in Hadoop's architecture. It presents a range of research findings on performance measurement and optimization strategies, addressing issues such as rack awareness and bottleneck modeling. Additionally, it illustrates comparative performance metrics and models for evaluating efficiency in managing large-scale data processing.
![The Binary ”Till Contention” Metric
• not a common, but very realistic
way to model performance under load
• note: even more applicable under
hotspot-y input
Rack
Rack
Border
(switch)
Client
Data
Shards
Data
Shards
…
Volume
Contention
Contention -free
to contention -ful
threshold
• example: function of server response
time to load can be expressed as:
T =
1
2
[
(L − n) +
√
(L − n)2 + k
1 − L
]
• ...where T is response time, L is load,
and k is the knee = contention point!
M.Zhanikeev – maratishe@gmail.com On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms – bit.do/170920 9/12
9/12](https://image.slidesharecdn.com/slides-170920033409/85/On-Performance-Under-Hotspots-in-Hadoop-versus-Bigdata-Replay-Platforms-9-320.jpg)
On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms
- 1. Platforms Marat Zhanikeev [email protected] maratishe.github.io Hadoop versus Bigdata Replay Tokyo Univ. of Science O n P e r f o r m a n c e U n d e r H o t s p o t s i n WebDB Forum 2017@お茶の水女子大 PDF → bit.do/170920
- 2. Background on Hadoop • Hadoop performance measurement ◦ creators on performance limits 09 ◦ superlinear effect 08 ◦ various benchmarks on Hadoop vs Spark 07 ◦ inconsistencies in measurements 11 • Hadoop/MapReduce optimization in 14 and a ton of other papers • the ”Do We (actually) Need Hadoop?” argument in 10 and few recent papers 09 K.Shvachko+0 ”HDFS scalability: the limits to growth” Usenix Login (2010) 08 N.Gunther+2 ”Hadoop Superlinear Scalability” ACM Queue (2015) 07 J.Shi+6 ”Clash of the Titans: MapReduce vs. Spark for Large Scale Data Analytics” Very Large Data Bases (2015) 11 M.Xia+3 ”Performance Inconsistency in Large Scale Data Processing Clusters” 10th USENIX ICAC (2013) 14 A.Rasooli+1 ”COSHH: A Classiffication and Optimization based Scheduler for Heterogeneous Hadoop Systems” Future Gen.Comp.Sys. (2014) 10 A.Rowstron+1 ”Nobody ever got fired for using Hadoop on a cluster” 1st HotCDP (2012) M.Zhanikeev – [email protected] On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms – bit.do/170920 2/12 2/12
- 3. Modeling Hadoop Bottlenecks Network (NW) Bulk Storage (BS) Shared Memory (SM) Core Output Big Data Processing HPC, Simulators, Modeling Small Data Bulk Storage (BS) On-Chip Shared Memory (hSM) Numberofparallelaccesses Network (NW) Ability to isolate Bottleneck (pipe width) RAM-based Shared Memory (sSM) Bulk Storage (BS) Network (NW)1 RAM-based Shared Memory (sSM) Parallelaccesses Ability to isolate Core Output Small Data M.Zhanikeev – [email protected] On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms – bit.do/170920 3/12 3/12
- 4. Hadoop’s Answer: Rack Awareness Rack Switch Datanode Datanode Datanode … Rack Switch Datanode Datanode …… Core Switch Client Client Logical Client Own Rack Switch Other Rack Switch Other Rack Switch Other Rack Switch Datanodes • official Hadoop feature (not a bug) 12 • some dynamics, goes off-rack when local nodes have too many jobs • sadly, manual configuration of rack affiliation (much potential here for research on virtual network coordinates – Meridian, Vivaldi...) 12 ”Hadoop: Rack Awareness” https://hadoop.apache.org (2017) M.Zhanikeev – [email protected] On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms – bit.do/170920 4/12 4/12
- 5. Hadoop vs Bigdata Replay Method • basic idea similar to 10 but uses circuits 02 to transfer shards and multicore 01 to parallel-process them Name Node Storage Node (shard) file A file B file C … Hadoop Space Manager Hadoop Job (your code) Hadoop Job (your code) Hadoop Job (your code) MapReduce job (your code) manymany Name Server(s) Client Machine Hadoop Client Your Code You Start Use Deploy FindRead/parse many Internals (DC) Users Storage Node (shard) Time-Aware Sub-Store(s) Manager Client Machine Client Your Sketcher You Start Use Schedule Multicore Replay Replay Node many 10 A.Rowstron+1 ”Nobody ever got fired for using Hadoop on a cluster” 1st HotCDP (2012) 02 myself+0 ”Circuit Emulation for Big Data Transfers in Clouds” Networking for Big Data, CRC (2015) 01 myself+0 ”Streaming Algorithms for Big Data Processing on Multicore” Big Data: Algorithms, Analytics, and Applications, CRC (2015) M.Zhanikeev – [email protected] On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms – bit.do/170920 5/12 5/12
- 6. Replay Environment is Highly Flexible! • replay is time-aligned, so jobs can pick any spot on the timeline • similar to Spark in going beyond key-value datatype but more – the full scope of streaming algorithms 01 • massively multicore environments 04 with 100+ cores, dynamic re-packing of job batches, etc. Core 1 Core 1 Core X Replay Manager Now(replay) …. Time-Aligned Big Data Cursor Time Direction One Sketch One SketchOne Sketch Start End End End Read/prepare Shared Memory Start …. Time Now (buffer head) Manager Job Job Buffer tail pos pos Controller Kill 2 Report Manage in realtime One Replay Batch One Buffer One Buffer One BufferJobs Jobs Jobs Replay at a scale 1 01 myself+0 ”Streaming Algorithms for Big Data Processing on Multicore” Big Data: Algorithms, Analytics, and Applications, CRC (2015) 04 myself+0 ”Volume and Irregularity Effects on Massively Multicore Packet Processors” APNOMS (2016) M.Zhanikeev – [email protected] On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms – bit.do/170920 6/12 6/12
- 7. Performance under hotspots M.Zhanikeev – [email protected] On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms – bit.do/170920 7/12 7/12
- 8. The Hotspot Distribution 0 20 40 60 80 100 Decreasing order 0 0.35 0.7 1.05 1.4 1.75 2.1 2.45 2.8 log(value) Class A Class B Class C Class D Class E • models Flash/Hotspot/ Killerapp/Blackswan events using extreme variance in popularity • generation method: stick-breaking process, Dirichlet distribution with parallel beta sources 05 • final step: classify based on the number of hot/flash items 05 myself+1 ”Popularity-Based Modeling of Flash Events in Synthetic Packet Traces” CQ研 (2012) M.Zhanikeev – [email protected] On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms – bit.do/170920 8/12 8/12
- 9. The Binary ”Till Contention” Metric • not a common, but very realistic way to model performance under load • note: even more applicable under hotspot-y input Rack Rack Border (switch) Client Data Shards Data Shards … Volume Contention Contention -free to contention -ful threshold • example: function of server response time to load can be expressed as: T = 1 2 [ (L − n) + √ (L − n)2 + k 1 − L ] • ...where T is response time, L is load, and k is the knee = contention point! M.Zhanikeev – [email protected] On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms – bit.do/170920 9/12 9/12
- 10. Performance Models • shard size as S and in-job traffic to shard size ratio r ◦ so, Hadoop jobs generate rS versus always strictly S under Replay • contention threshold as C (for both contention and/or capacity) • list of shard hotness (popularity) { h1, h2, h3, ..., hn } and sizes{ S1, S2, S3, ...., Sn } • then we have (job/traffic) volume for Hadoop: Vhadoop = ∑ i=1..n rhiSi • ... and for Replay method: Vreplay = ∑ i=1..n Si (1) M.Zhanikeev – [email protected] On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms – bit.do/170920 10/12 10/12
- 11. Results A 0.001 B 0.001 C 0.001 D 0.001 E 0.001 hadoopreplay A 0.005 B 0.005 C 0.005 D 0.005 E 0.005 A 0.01 B 0.01 C 0.01 D 0.01 E 0.01 A 0.05 B 0.05 C 0.05 D 0.05 E 0.05 A 0.1 B 0.1 C 0.1 D 0.1 E 0.1 A 0.2 B 0.2 C 0.2 D 0.2 E 0.2 10 20 50 100 200 500 1000 2000 5000 10000 0.7 1.4 2.1 2.8 3.5 4.2 log(1+timetillcontention) A 0.5 B 0.5 C 0.5 D 0.5 E 0.5 Replay period (step) is 10 A 0.001 B 0.001 C 0.001 D 0.001 E 0.001 hadoopreplay A 0.005 B 0.005 C 0.005 D 0.005 E 0.005 A 0.01 B 0.01 C 0.01 D 0.01 E 0.01 A 0.05 B 0.05 C 0.05 D 0.05 E 0.05 A 0.1 B 0.1 C 0.1 D 0.1 E 0.1 A 0.2 B 0.2 C 0.2 D 0.2 E 0.2 10 20 50 100 200 500 1000 2000 5000 10000 0.8 1.6 2.4 3.2 4 4.8 log(1+timetillcontention) A 0.5 B 0.5 C 0.5 D 0.5 E 0.5 Replay period (step) is 50 A 0.001 B 0.001 C 0.001 D 0.001 E 0.001 hadoopreplay A 0.005 B 0.005 C 0.005 D 0.005 E 0.005 A 0.01 B 0.01 C 0.01 D 0.01 E 0.01 A 0.05 B 0.05 C 0.05 D 0.05 E 0.05 A 0.1 B 0.1 C 0.1 D 0.1 E 0.1 A 0.2 B 0.2 C 0.2 D 0.2 E 0.2 10 20 50 100 200 500 1000 2000 5000 10000 0.9 1.8 2.7 3.6 4.5 5.4 log(1+timetillcontention) A 0.5 B 0.5 C 0.5 D 0.5 E 0.5 Replay period (step) is 200 M.Zhanikeev – [email protected] On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms – bit.do/170920 11/12 11/12
- 12. That’s all, thank you ... M.Zhanikeev – [email protected] On Performance Under Hotspots in Hadoop versus Bigdata Replay Platforms – bit.do/170920 12/12 12/12