Join Processing for Flash SSDs: Remembering Past Lessons Jaeyoung Do, Jignesh M. Patel Department of Computer Sciences University of Wisconsin-Madison All right. Welcome to my presentation. My name is Jaeyoung, Do. Today I’m going to talk about the performance of join algorithms on flash SSDs.

Flash Solid State Drives (SSDs) ▶ Benefits of Flash SSDs No seek time, small latency ▶ Flash Densities 1995. 16 MB NAND Flash 2005. 16 GB NAND Flash 2010. 1 TB NAND Flash (predicted by Samsung) ▶ Flash Prices Decreasing continuously GB What is flash Solid State Drives. Flash SSDs are data storage devices that use NAND flash memory chips. Currently, the most available and popular storage device is absolutely magnetic hard disk drives. However, since flash SSDs have many benefits compared to magnetic hard disk drives, they are expected to gradually replace hard disks as the primary permanent storage media in large data centers. These two graphs show the change of flash densities and flash prices as time goes by. As its capacity continues to increase and price drops, Jim Gray’s prediction of “Flash is disk, disk is tape, and tape is dead” is coming close to reality in many applications. Then, what are advantages of flash SSDs over magnetic hard disks? $/MB DaMoN 09

Flash SSDs for DBMSs still apply to flash SSDs? ▶ Many previous works with flash SSDs Flash-based DBMS [Gray ACM Queue 08, Graefe DaMoN07] In-Page Logging [Lee VLDB07] Transaction Processing [Lee VLDB08] B+ Tree Index [Li ICDE09] I/O Benchmarks [Bouganim CIDR09] ▶ We focus on Join algorithms New join algorithms [Shah DaMoN08, Tsirogiannis SIGMOD09] So, the use of flash SSDs for DBMSs are being actively studied in various fields including transaction processing and b+ tree index. In this work, we focus on joins which are common, but expensive query processing operations. There are already a few researches in order to develop new join algorithms on flash SSDs. Rather than developing new join algorithm, our aim of this research is to see what lessons join techniques that worked for magnetic hard disk drives continue to work with flash SSDs. So, we wanna see what happen if we just replace magnetic HDDs with flash SSDs, and examine the effects of tuning parameters of join performance. But we already got plenty of lessons that we have learnt over three decases of desinging and turinng join algorihtms for hard disk drive based systems. If joins were done with flash SSDs, would theses lessons be meaningless? As we will see later, the answer is NO. Obviously many lessons learnt for magneis HDDs still work for flash SSDs. The focust of this work Purpose here is to Different XX however, What lessons learnt for magnetic HDDs still apply to flash SSDs? DaMoN 09

Goals 2. Providing better insights for join performance on flash SSDs ▶Demonstrate the importance of recalling past lessons about efficient joins in magnetic HDDs ▶ Explore the effects of various parameters for joins on flash SSDs 1. Not inventing new join algorithms 2. Providing better insights for join performance on flash SSDs Therefore, the first goal of our research is that we want to recall some of the important lessons about efficient join processing in magnetic hard disks determining if these lessons also apply to joins using flash SSDs. In addition, we want to see how tuned parameters based on characteristic of magnetic HDDs works for joins on flash SSDs. Better staring points when designing new join algori Wthms for flash SSDs. We want to see the effects of tuned parameters that are based on characteristics of HDD, and how they work for join on flahs SSDs DaMoN 09

Our Approach ▶ Investigate four popular ad hoc join algorithms Block Nested Loops Join (BNL) Sort-Merge Join (SM) Grace Hash Join (GH) Hybrid Hash Join (HH) ▶ Conduct experiments as varying various parameters Memory buffer pool size Page size I/O unit size To achieve the goals, we have investigated four ad hoc join algorithms which are popular in both literacture and industry. four popular ad hoc join algorithms, namely BNL, SM, GH, HH. Then, we have conducted some experiments to see the effect of tuned parameters such as … DaMoN 09

Roadmap ▶ Introduction / Goals ▶ Ad hoc join algorithms ▶ Experimental Results ▶ Conclusion / Future Work DaMoN 09

Assumptions ▶ Blocked I/O is available ▶ We use the buffer allocation strategy tuned for magnetic HDDs [Haas et al. VLDB97] Before explaining the join algorithms considered in our work, let me give some assumptions. First, Rather than fetching one page in each I/O operation we used blocked I/O to sequentially read and write multiple pages per an I/O operation. In the case of magnetic hard disk drives, blocked I/O is useful because it amortizes the cost of expensive disk seeks and rotational delays. Also, a lot is known about how to optimize joins with magnetic HDDs to use the available memory buffer pool effectively. Specifically, Hass showed that the right buffer pool allocation strategy can have a huge impact – upto 400% improvements in some cases. In this work, we use the same buffer alolcation method for both flash SSDs and magnetic HDDs. While these allocations may not be optimal for flash SSDs, our goal here is to start with the best allocation strategy for magnetic HDDs, and explore what happens if we simply use the same settings when replacing a magnetic HDD with a flash SSD. per a disk I/O. DaMoN 09

Block Nested Loops Join IS= Block Nested Loops Join Input Buffer for R Result Input Buffer for S Buffer Pool R S IS= , IR=B-IS Disk DaMoN 09

Sort-Merge Join Buffer Pool Buffer Pool Result Disk Disk Working Space Input Buffer Input Buffer Result Input Buffer Output Buffer Input Buffer R Optimization is out of concerns why?> S Disk Disk DaMoN 09

Grace Hash Join Buffer Pool Buffer Pool Result Disk Disk Input Bucket 1 Z Input Buffer for R Result Input Buffer for S h Bucket k R S Disk Disk DaMoN 09

Hybrid Hash Join Buffer Pool Buffer Pool Result Result Disk Disk In Memory Bucket Bucket 1 Result Input Buffer for R Result Input Buffer Input Buffer for S h Bucket k R S Disk Disk DaMoN 09

Roadmap ▶ Introduction / Goals ▶ Ad-hoc join algorithms ▶ Experimental Results ▶ Conclusion / Future Work DaMoN 09

Experimental Setup ▶ A single-thread and light-weight database engine ▶ Flash SSD and magnetic HDD OCZ Core Series 2.5” SATA 60 GB TOSHIBA 5400 RPM 320 GB ▶ Data Set: TPC-H Customer: 730 MB Orders: 5 GB ▶ Platform Dual Core 3.2 GHz Intel Pentium, Red Hat Max. DB buffer pool size: 600 MB SSD HDD None Avg. Seek Time 12 ms 0.35 ms Avg. Latency 5.56 ms 120 MB/sec Read Data Transfer Rate 34 MB/sec 80 MB/sec Write Data 230.99 $ (3.85 $/GB) Price 129.99 $ (0.36 $/GB) MAX BUFER pAGE Source: OCZ and TOSHIBA DaMoN 09

Effect of Blocked I/O (HDD) 500 MB Buffer Pool, 8 KB Page Size Non-Blocked I/O Blocked I/O 2.1X CPU time I/O time 2.2X Join Time (sec) 2.0X 2.3X 500 MB, 8 KB BNL SM GH HH BNL SM GH HH HDD DaMoN 09

Effect of Blocked I/O (SSD) 500 MB Buffer Pool, 8 KB Page Size Non-Blocked I/O Blocked I/O CPU time I/O time 1.7X Join Time (sec) 1.6X 1.7X 1.9X 184 346 530 124 155 279 BNL SM GH HH BNL SM GH HH SSD Using blocked I/O is critical DaMoN 09

Joins are I/O Bound? (HDD) 8 KB Page Size, Blocked I/O Buffer Pool Size 200 MB 500 MB CPU time I/O time 0.68 0.65 0.69 Join Time (sec) Join Time (sec) 0.62 0.58 0.34 0.61 0.31 8 KB, Blocked I/O 200 MB 500 MB GH SM is IO bound, 8 KB Page Size, Blocked I/O BNL SM GH HH BNL SM GH HH HDD DaMoN 09

Joins are I/O Bound? (SSD) 8 KB Page Size, Blocked I/O Buffer Pool Size 200 MB 500 MB CPU time I/O time Join Time (sec) 1.78 1.09 0.70 1.35 1.69 0.64 1.79 0.8 8 KB, Blocked I/O 200 MB 500 MB GH SM is IO bound, 8 KB Page Size, Blocked I/O BNL SM GH HH BNL SM GH HH SSD Joins may become CPU-bound sooner DaMoN 09

Effect of Varying the Page Size (SSD) More details in our DaMoN’09 paper Effect of Varying the Page Size (SSD) 500 MB Buffer Pool, Blocked I/O BNL SM GH HH CPU time I/O time Join Time (sec) bigger 2 8 32 2 8 32 2 8 32 2 8 32 Page size (KB) When using Blocked I/O, the page size has a small impact on join performance DaMoN 09

Conclusions / Future Work ▶ Traditional Join optimizations continue to be important with flash SSDs Blocked I/O dramatically improves join performance Buffer allocation strategy has an impact on join performance It is even more critical to consider both CPU and I/O costs ▶ Future Work Expand the range of hardware, and consider other HDD-based configurations Derive detailed cost models for existing join algorithms, and explore the optimal buffer allocations for flash SSDs Provide better insights for join performance on flash SSDs 608-886-7154 Before you go to develop new join algorithms on flash SSDs, m ake sure that you can get large performace improvements with basic join algorithms by using many traditional join optimizations. DaMoN 09