SSD/Flash for Modern Databases. Peter Zaitsev, CEO, Percona November 1, 2014 Highload Moscow,Russia
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1 SSD/Flash for Modern Databases Peter Zaitsev, CEO, Percona November 1, 2014 Highload Moscow,Russia
2 Percona We love Open Source Software Percona Server Percona Xtrabackup Percona XtraDB Cluster Percona Toolkit We want to help you to succeed with MySQL and Beyond Consulting Support Managed Services 2
3 In this Presentation Flash technology overview Review some of the available technology What does this mean for databases? Specific opportunities for MySQL 3
4 Before SSDs 4
5 There were HDDs Good at Sequential Read/Writes RT=Seek Time + Rotation Latency Reads/Write Similar Latency No Specific Write Limits Retain data for a long time One IO Request in Parallel Low cost per GB 5
6 RAID and SAN 6
7 Using Many HDDs together Caching Reads Buffering Writes (Writeback Cache) Better Sequential Read/Write speed Better throughput at high concurrency Higher IO latencies for uncached IO 7
8 Flash Revolution Use Flash chips instead of platters No moving parts No seeks 8
9 NAND Flash Cell Page/Read Block Erase Block Write but no overwrite Wears with writes (erases) 9
10 Writing to the Flash Erase Write Change Zero to one Set all bits to Set some of the bits to 0: Impossible. Do Erase, when Write 10
11 Types of NAND Flash From AnandTech: 11
12 Flash Storage Design Cache Battery/Super Capacitor Controller + Complex Firmware Built-in Parallelism 12
13 Flash Controller Tasks Write wear leveling Garbage collection Error correction Bad block mapping Read scrubbing Read disturb management Encryption 13
14 Flash Properties Lots of IOs per device! (100K+) Less random IO penalty Writes more expensive than reads (but can be faster) Limited by amount of writes Limited retention Concurrent execution on single device Fast write acknowledgement (safe or not) Can burst writes 14
15 Flash Interface Designs DIMM PCI-E SFF-8639 SATA/SAS FC and Network 15
16 Transitioning AHCI NVMe 16
17 AHCI vs NVMe Source: AnandTech.com 17
18 Sandisk ULLtraDIMM 18
19 HGST Virident 19
20 Sandisk FusionIO 20
21 Intel P
22 Intel 730 (SATA) 22
23 msata 23
24 M.2 Interface 24
25 Violin Memory 25
26 Consumer vs Enterprise Performance Endurance Durability Retention Encryption 26
27 Not your HDD All HDDs are the same; All SSDs are different 27
28 Evaluation Performance changes over time Empty Space Matters Complex internals Watch stability carefully 28
29 How Flash Fails Clear write amount defined EOL (but often can handle a lot more) One day it s gone Power Loss Protection Internal ECC and redundancy 29
30 To RAID or not to RAID? More valuable for consumer grade Watch for good Flash support RAID controller logic may slow things down Use a redundant array of inexpensive servers instead? 30
31 Redundancy Device internal redundancy Hardware RAID Software RAID Filesystem RAID 31
32 OS Support Flash support is actively being improved TRIM Sparse Files 32
33 Flash And Databases 33
34 Database History Most have been designed in HDD time Optimize for sequential IO Count on cheap sequential writes RAID, BBU to improve performance 34
35 It s time for Flash Your OLTP Database should live on Flash 35
36 But What Flash? Pick a flash type that is right for your application 36
37 IO vs Memory 37
38 Warmup Much faster warmup times Even if the database fits in memory, SSD might be justified 38
39 Tolerate more IO bound load HDD 5ms Can do 20 IO/s for 100ms response time (non parallel) Flash 0.1ms Can do 1000 IO/s for 100ms response time (non parallel) 39
40 Endurance Might be a top consideration 40
41 Endurance Math HGST FlashMax III 2200GB 4400GB/day over 5 Years 1400MB/sec peak writes 66 days at peak write throughput Crucial M GB 72TB total life time writes 400MB/sec write 52 hours at peak write throughput 41
42 Databases and Flash How do we optimize databases to us Flash best? 42
43 Torn Page problem Flash can avoid this with little cost due to internal design FusionIO NVMFS (Atomic Writes) Copy-on-Write File Systems ZFS BTRFS Filesystem level data journaling less preferred data=journal for EXT4 Skip-Innodb-double-write 43
44 Fast IO Path Bypass Caching O_DIRECT Native Asynchronous IO Efficient Checksuming Innodb_checksum_algorithm=crc32 Innodb_flush_method=O_DIRECT 44
45 IO Cost Accounting Sequential vs Random IO balance IO vs CPU Balance Smaller page sizes might make sense innodb_page_size=4k 45
46 Less Pre-fetching Most pre-fetched data must be used Often best to try It out 46
47 Less merging on flushing Do not assume flushing multiple sequential dirty pages has same cost Innodb_flush_neighbors=0 47
48 Less Space on Disk Innodb Compression (2x typical) TokuDB Compression (5-10x typical) Archiving data off OLTP System 48
49 Less Writes on Flash Hybrid Flash/SSD System Transactional Logs, Other logs on the HDD with RAID and BBU Small Temporary objects on tmpfs Innodb_log_file_size=<LARGE> 49
50 Logs on RAID can be fast 50
51 Single Intel 730 Sysbench 51
52 IOPS 52
53 Consistency Graph by 53
54 Is Flash Too Fast? Multiple instances might scale better 54
55 Other Thoughts Host hardware and OS matter, especially with high end flash Virtualization has higher relative overhead Network higher relative overhead 55
56 Thank You! Peter 56
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