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map_suite_geocoder_performance_guide [2019/03/21 02:42]
tgwikiupdate
map_suite_geocoder_performance_guide [2019/03/21 02:49]
tgwikiupdate
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 The purpose of this guide is to help you to know following things: The purpose of this guide is to help you to know following things:
-  * A simple ​introduction for best practice of Map Suite Geocoder.+  * An introduction for best practice of Map Suite Geocoder.
   * Using multi-thread to improve the performance of batch query.   * Using multi-thread to improve the performance of batch query.
   * The bottleneck of Map Suite Geocoder performance.   * The bottleneck of Map Suite Geocoder performance.
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 To compare the performance between single thread and multi-thread,​ we used the following (Figure 2) machine hardware device to do the benchmark: To compare the performance between single thread and multi-thread,​ we used the following (Figure 2) machine hardware device to do the benchmark:
  
-{{:map_suite_geocoder_performance_guide_001.png}}+{{:map_suite_geocoder_performance_guide_002.png}}
 \\ \\
 //Figure 2. Machine Hardware Device Information.//​ //Figure 2. Machine Hardware Device Information.//​
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 Below Figure 3 shows the benchmark result: Below Figure 3 shows the benchmark result:
  
-{{:map_suite_geocoder_performance_guide_001.png}}+{{:map_suite_geocoder_performance_guide_003.png}}
 \\ \\
 //Figure 3. Single Thread & Multi-Thread Benchmark Result.// //Figure 3. Single Thread & Multi-Thread Benchmark Result.//
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 It shows that we improved about three times speed after using multi-thread. And the below Figure 4 shows the hardware usage when using multi-thread:​ It shows that we improved about three times speed after using multi-thread. And the below Figure 4 shows the hardware usage when using multi-thread:​
  
-{{:map_suite_geocoder_performance_guide_001.png}}+{{:map_suite_geocoder_performance_guide_004.png}}
 \\ \\
 //Figure 4. Hardware Usage Using Multi-Thread.//​ //Figure 4. Hardware Usage Using Multi-Thread.//​
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 To dig the limitation of the Geocoder with multi-thread we also created different [[https://​aws.amazon.com/​ec2/​instance-types|Amazon® instances]] to do the same benchmark. Below Figure 5 is the test result: To dig the limitation of the Geocoder with multi-thread we also created different [[https://​aws.amazon.com/​ec2/​instance-types|Amazon® instances]] to do the same benchmark. Below Figure 5 is the test result:
  
-{{:map_suite_geocoder_performance_guide_001.png}}+{{:map_suite_geocoder_performance_guide_005.png}}
 \\ \\
 //Figure 5. Amazon® Instance Benchmark Result.// //Figure 5. Amazon® Instance Benchmark Result.//
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 Below Figure 6 is the analysis result that did **1,000** queries one by one, the execution time is **4.669** seconds: Below Figure 6 is the analysis result that did **1,000** queries one by one, the execution time is **4.669** seconds:
  
-{{:map_suite_geocoder_performance_guide_001.png}}+{{:map_suite_geocoder_performance_guide_006.png}}
 \\ \\
 //Figure 6. Analysis Result With 1,000 Queries.// //Figure 6. Analysis Result With 1,000 Queries.//
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 And the below Figure 7 is the analysis result that did **10,000** queries one by one, the execution time is **16.588** seconds: And the below Figure 7 is the analysis result that did **10,000** queries one by one, the execution time is **16.588** seconds:
  
-{{:map_suite_geocoder_performance_guide_001.png}}+{{:map_suite_geocoder_performance_guide_007.png}}
 \\ \\
 //Figure 7. Analysis Test With 10,000 Queries.// //Figure 7. Analysis Test With 10,000 Queries.//
  
 It's obvious that the most time spent is on I/O (includes file or cache data read), then normalization. It's obvious that the most time spent is on I/O (includes file or cache data read), then normalization.
map_suite_geocoder_performance_guide.txt · Last modified: 2019/03/21 02:49 by tgwikiupdate