Author: Mark Callaghan

CPU overheads for RocksDB queries

An LSM like RocksDB has much better write and space efficiency than a B-Tree. That means with RocksDB you will use less SSD than with a B-Tree and the SSD will either last longer or you can use lower endurance SSD. But this efficiency comes at a cost. In the worst-case RocksDB might use 2X more CPU/query than a B-Tree, which means that in the worst case QPS with RocksDB might be half of what it is with a B-Tree. In the examples below I show that RocksDB can use ~2X more comparisons per query compared to a B-Tree and it is nice when results in practice can be explained by theory.But first I want to explain the context where this matters and where it doesn’t matter. It matters when the CPU overhead from RocksDB is a significant fraction of the query response time — so the workload needs to be CPU bound (cached working set). It isn’t a problem for workloads that are IO-bound. Many performance results have been published on my blog and more are coming later this year. With fast storage devices available I recommend that database workloads strive to be IO-bound to avoid using too much memory+power.Basic OperationsWhere does the CPU overhead come from? I started to explain this in my review of the original LSM paper. My focus in this post is on SELECT statements in SQL, but note that writes in SQL also do reads from the storage engine (updates have a where clause, searches are done to find matching rows and appropriate leaf pages, queries might be done to determine whether constraints will be violated, etc).With RocksDB data can be found in the memtable and sorted runs from the L0 to Lmax (Lmax is the max/largest level in the LSM tree. There are 3 basic operations that are used to search for data – get, range seek and range next. Get searches for an exact match. Range seek positions iterators at the start of a range scan. Range next gets the next row from a range scan.First a few comments before I explain the CPU overhead:
I will try to be clear when I refer to the keys for a SQL table (SQL key) and the key as used by RocksDB (RocksDB key). Many SQL indexes can be stored in the same LSM tree (column family) and they are distinguished by prepending the index ID as the first bytes in the RocksDB key. The prepended bytes aren’t visible to the user.
Get is used for exact match on a PK index but not used for exact match on a secondary index because with MyRocks the RocksDB key for a secondary index entry is made unique by appending the PK to it. A SQL SELECT that does an exact match on the secondary key searches for a prefix of the RocksDB key and that requires a range scan.
With RocksDB a bloom filter can be on a prefix of the key or the entire key (prefix vs whole key). A prefix bloom filter can be used for range and point queries. A whole key bloom filter will only be used for point queries on a PK. A whole key bloom filter won’t be used for range queries. Nor will it be used for point queries on a secondary index (because secondary index exact match uses a range scan internally).
Today bloom filters are configured per column family. A good use for column families is to put secondary indexes into separate ones that are configured with prefix bloom filters. Eventually we expect to make this easier in RocksDB.

Get

A point query is evaluated for RocksDB by searching sorted runs in order: first the memtable, then runs in the L0, then the L1 and so on until the Lmax is reached. The search stops as soon as the key is found, whether from a tombstone or a key-value pair. The work done to search each sorted run varies. I use comparisons and bloom filter probes as the proxy for work. I ignore memory system behavior (cache misses) but assume that the skiplist isn’t great in that regard:
memtable – this is a skiplist and I assume the search cost is log2(N) when it has N entries. 
L0 – for each sorted run first check the bloom filter and if the key might exist then do binary search on the block index and then do binary search within the data block. The cost is a bloom filter probe and possibly log2(M) comparisons when an L0 sorted run has M entries.
L1 to Ln – each of these levels is range partitioned into many SST files so the first step is to do a binary search to find the SST that might contain the key, then check the bloom filter for that SST and if the key might exist then do binary search on the block index for that SST and then do binary search within the data block. The binary search cost to find the SST gets larger for the larger levels and that cost doesn’t go away when bloom filters are used. For RocksDB with a 1T database, per-level fanout=8, SST size=32M then the number of SSTs per level is 8 in L1, 64 in L2, 512 in L3, 4096 in L4 and 32768 in L5. The number of comparisons before the bloom filter check are 3 for L1, 6 for L2, 9 for L3 and 12 for L4. I assume there is no bloom filter on L5 because it is the max level.
A bloom filter check isn’t free. Fortunately, RocksDB makes the cost less than I expected by limiting the bits that are set for a key, and must be checked on a probe, to one cache line. See the code in AddHash. For now I assume that the cost of a bloom filter probe is equivalent to a few (< 5) comparisons given the cache line optimization.

A Get operation on a B-Tree with 8B rows needs ~33 comparisons. With RocksDB it might need 20 comparisons for the memtable, bloom filter probes for 4 SSTs in L0, 3+6+9+12 SST search comparisons for L1 to L4, bloom filter probes for L1 to L4, and then ~33 comparisons to search the max level (L5). So it is easy to see that the search cost might be double the cost for a B-Tree.The search cost for Get with RocksDB can be reduced by configuring the LSM tree to use fewer sorted runs although we are still figuring out how much that can be reduced. With this example about 1/3 of the comparisons are from the memtable, another 1/3 are from the max level and the remaining 1/3 are from L0 to L4.Range Seek and Next

The cost for a range scan has two components: range seek to initialize the scan and range next to produce each row. For range seek an iterator is positioned within each sorted run in the LSM tree. For range next the merging iterator combines the per-run iterators. For RocksDB the cost of range seek depends on the number of sorted runs while the cost of range next does not (for leveled compaction, assuming uniform distribution).Range seek does binary search per sorted run. This is similar to a point query without a bloom filter. The cost across all sorted runs depends on the number of sorted runs. In the example above where RocksDB has a memtable, 4 SSTs in the L0, L1 to L5 and 8B rows that requires 20 comparisons for the memtable, 4 x 20 comparisons for the L0 SSTs, 21 + 24 + 27 + 30 + 33 comparison for L1 to L5. The total is 235 comparisons. There isn’t a way to avoid this and for short range queries the cost of range seek dominates the cost of range next. While this overhead is significant for short range queries with embedded RocksDB and an in-memory workload it is harder to notice with MyRocks because there is a lot of CPU overhead above MyRocks from optimize, parse and client RPC for a short range query. It is easier to notice the difference in CPU overhead between MyRocks and a B-Tree with longer range scans.

The cost for range next is interesting. LevelDB has a comment that suggests using a heap but the merging iterator code uses N-1 comparisons per row produced which means the overhead is dependent on the number of sorted runs. The cost of range next in RocksDB is much less dependent on the number of sorted runs because it uses an optimized binary heap and the number of comparisons to produce a row depends on the node that produces the winner. Only one comparison is done if the root produces the winner and the root produced the previous winner. Two comparisons are done if the root produces the winner but did not produce the previous winner. More comparisons are needed in other cases. The code is optimized for long runs of winners from one iterator and that is likely with leveled compaction because Lmax is ~10X larger than the next largest level and usually produces most winners. Using a simulation with uniform distribution the expected number of comparisons per row is <= 1.55 regardless of the number of sorted runs for leveled compaction. The optimization in the binary heap used by the merging iterator is limited to remembering the comparison result between the two children of the root node. Were that extended to remembering comparison results between any two sibling nodes in the heap then the expected number of comparisons would be reduced from ~1.55 to ~1.38. For a B-Tree:
The overhead for range seek is similar to a point query — ~33 comparisons when there are 8B rows.
There are no merging iterators. The overhead for range next is low — usually move to the next row in the current leaf page.

Where to ask questions about MyRocks and RocksDB

MyRocks
Best places to discuss MyRocks:

percona-discussion – if using MyRocks in Percona Server and report bugs here

maria-discuss – if using MyRocks in MariaDB Server and report bugs here

Other places to discuss MyRocks:

MyRocks group for FB MySQL – this existed before MyRocks made it into Percona and MariaDB. You are better off using the MariaDB and Percona groups.
Bugs for MyRocks in FB MySQL are here. Again, if using Percona or MariaDB please use their forums for bugs.

RocksDB

discuss RocksDB on the email list

discuss RocksDB on the Facebook group

report RocksDB bugs here

Cache amplification

How much of the database must be in cache so that a point-query does at most one read from storage? I call this cache-amplification or cache amplification. The answer depends on the index structure (b-tree, LSM, something else). Cache amplification can…

Sharded replica sets – MySQL and MongoDB

MongoDB used to have a great story for sharded replica sets. But the storage engine, sharding and replica management code had significant room for improvement. Over the last few releases they made remarkable progress on that and the code is starting to match the story. I continue to be impressed by the rate at which they paid off their tech debt and transactions coming to MongoDB 4.0 is one more example.It is time for us to do the same in the MySQL community.I used to be skeptical about the market for sharded replica sets with MySQL. This is popular with the web-scale crowd but that is a small market. Today I am less skeptical and assume the market extends far beyond web-scale. This can be true even if the market for replicasets, without sharding, is so much larger.The market for replica sets is huge. For most users, if you need one instance of MySQL then you also need HA and disaster recovery. So you must manage failover and for a long time (before crash-proof slaves and GTID) that was a lousy experience. It is better today thanks to cloud providers and DIY solutions even if some assembly is required. Upstream is finally putting a solution together with MySQL Group Replication and other pieces.
But sharded replica sets are much harder, and even more so if you want to do cross-shard queries and transactions. While there have been many attempts at sharding solutions for the MySQL community, it is difficult to provide something that works across customers. Fortunately Vitess has shown this can be done and already has many customers in production.

ProxySQL and Orchestrator might also be vital pieces of this stack. I am curious to see how the traditional vendors (MySQL, MariaDB, Percona) respond to this progress.Updates:I think binlog server should be part of the solution. But for that to happen we need a GPLv2 binlog server and that has yet to be published.

Meltdown vs storage

tl;dr – sysbench fileio throughput for ext4 drops by more than 20% from Linux 4.8 to 4.13I shared results from sysbench with a cached database to show a small impact from the Meltdown patch in Ubuntu 16.04. Then I repeated the test for an IO-bound configuration using a 200mb buffer pool for InnoDB and database that is ~1.5gb.The results for read-only tests looked similar to what I saw previously so I won’t share them. The results for write-heavy tests were odd as QPS for the kernel without the patch (4.8.0-36) were much better than for the kernel with the patch (4.13.0-26).The next step was to use sysbench fileio to determine whether storage performance was OK and it was similar for 4.8 and 4.13 with read-only and write-only tests. But throughput with 4.8 was better than 4.13 for a mixed test that does reads and writes.ConfigurationI used a NUC7i5bnh server with a Samsung 960 EVO SSD that uses NVMe. The OS is Ubuntu 16.04 with the HWE kernels — either 4.13.0-26 that has the Meltdown fix or 4.8.0-36 that does not. For the 4.13 kernel I repeat the test with PTI enabled and disabled. The test uses sysbench with one 2gb file, O_DIRECT and 4 client threads. The server has 2 cores and 4 HW threads. The filesystem is ext4.I used these command lines for sysbench:sysbench fileio –file-num=1 –file-test-mode=rndrw –file-extra-flags=direct \    –max-requests=0 –num-threads=4 –max-time=60 preparesysbench fileio –file-num=1 –file-test-mode=rndrw –file-extra-flags=direct \    –max-requests=0 –num-threads=4 –max-time=60 runAnd I see this:cat /sys/block/nvme0n1/queue/write_cachewrite backResultsThe next step was to understand the impact of the filesystem mount options. I used ext4 for these tests and don’t have much experience with it. The table has the throughput in MB/s from sysbench fileio that does reads and writes. I noticed a few things:
Throughput is much worse with the nobarrier mount option. I don’t know whether this is expected.
There is a small difference in performance from enabling the Meltdown fix – about 3%
There is a big difference in performance between the 4.8 and 4.13 kernels, whether or not PTI is enabled for the 4.13 kernel. I get about 25% more throughput with the 4.8 kernel.

4.13    4.13    4.8    mount optionspti=on  pti=off no-pti100     104     137     nobarrier,data=ordered,discard,noauto,dioread_nolock 93     119     128     nobarrier,data=ordered,discard,noauto226     235     275     data=ordered,discard,noauto233     239     299     data=ordered,discard,noauto,dioread_nolockIs it the kernel?I am curious about what happened between 4.8 and 4.13 to explain the 25% loss of IO throughput.I have another set of Intel NUC servers that use Ubuntu 16.04 without the HWE kernels — 4.4.0-109 with the Meltdown fix and 4.4.0-38 without the Meltdown fix. These servers still use XFS. I get ~2% more throughput with the 4.4.0-38 kernel than the 4.4.0-109 kernel (whether or not PTI is enabled).The loss in sysbench fileio throughput does not reproduce for XFS. The filesystem mount options are “noatime,nodiratime,discard,noauto” and tests were run with /sys/block/nvme0n1/queue/write_cache set to write back and write through. The table below has MB/s of IO throughput.4.13    4.13    4.8pti=on  pti=off no-pti225     229     232     write_cache=”write back”125     168     138     write_cache=”write through”

Meltdown vs MySQL part 2: in-memory sysbench and a core i5 NUC

This is my second performance report for the Meltdown patch using in-memory sysbench and a small server. In this test I used a core i5 NUC with the 4.13 and 4.8 kernels. In the previous test I used a core i3 NUC with the 4.4 kernel.
results for 4.13 are mixed — sometimes there is more QPS with the fix enabled, sometimes there is more with the fix disabled. The typical difference is small, about 2%.
QPS for 4.8, which doesn’t have the Meltdown fix, are usually better than with 4.13, the largest difference is ~10% and the difference tend to be larger at 1 client than at 2 or 8.

Configuration

My usage of sysbench is described here. The servers are described here. For this test I used the core i5 NUC (NUC7i5bnh) with Ubuntu 16.04. I have 3 such servers and ran tests with the fix enabled (kernel 4.13.0-26), the fix disabled via pti=off (kernel 4.13.0-26) and the old kernel (4.8.0-36) that doesn’t have the fix. From cat /proc/cpuinfo I see pcid. This server uses the HWE kernels to make wireless work. I repeated tests after learning that 4.13 doesn’t support the nobarrier mount option for XFS. My workaround was to switch to ext4 and the results here are from ext4.

The servers have 2 cores and 4 HW threads. I normally use them for low-concurrency benchmarks with 1 or 2 concurrent database clients. For this test I used 1, 2 and 8 concurrent clients to determine whether more concurrency and more mutex contention would cause more of a performance loss.

The sysbench test was configured to use 1 table with 4M rows and InnoDB. The InnoDB buffer pool was large enough to cache the table. The sysbench client runs on the same host as mysqld.I just noticed that all servers had the doublewrite buffer and binlog disabled. This was leftover from debugging the XFS nobarrier change.

Results

My usage of sysbench is described here which explains the tests that I list below. Each test has QPS for 1, 2 and 8 concurrent clients. Results are provided for

pti enabled – kernel 4.13.0-26 with the Meltdown fix enabled
pti disabled – kernel 4.13.0-26 with the Meltdown fix disabled via pti=off
old kernel, no pti – kernel 4.8.0-36 which doesn’t have the Meltdown fix

After each of the QPS sections, there are two lines for QPS ratios. The first line compares the QPS for the kernel with the Meltdown fix enabled vs disabled. The second line compares the QPS for the kernel with the Meltdown fix vs the old kernel. A value less than one means that MySQL gets less QPS with the Meltdown fix.update-inlist1       2       8       concurrency5603    7546    8212    pti enabled5618    7483    8076    pti disabled5847    7613    8149    old kernel, no pti—–   —–   —–0.997   1.008   1.016   qps ratio: pti on/off0.958   0.991   1.007   qps ratio: pti on / old kernelupdate-one1       2       8       concurrency11764   18880   16699   pti enabled12074   19475   17132   pti disabled12931   19573   16559   old kernel, no pti—–   —–   —–0.974   0.969   0.974   qps ratio: pti on/off0.909   0.964   1.008   qps ratio: pti on / old kernelupdate-index1       2       8       concurrency7202    12688   16738   pti enabled7197    12581   17466   pti disabled7443    12926   17720   old kernel, no pti—–   —–   —–1.000   1.000   0.958   qps ratio: pti on/off0.967   0.981   0.944   qps ratio: pti on / old kernelupdate-nonindex1       2       8       concurrency11103   18062   22964   pti enabled11414   18208   23076   pti disabled12395   18529   22168   old kernel, no pti—–   —–   —–0.972   0.991   0.995   qps ratio: pti on/off0.895   0.974   1.035   qps ratio: pti on / old kerneldelete1       2       8       concurrency19197   30830   43605   pti enabled19720   31437   44935   pti disabled21584   32109   43660   old kernel, no pti—–   —–   —–0.973   0.980   0.970   qps ratio: pti on/off0.889   0.960   0.998   qps ratio: pti on / old kernelread-write range=1001       2       8       concurrency11956   20047   29336   pti enabled12475   20021   29726   pti disabled13098   19627   30030   old kernel, no pti—–   —–   —–0.958   1.001   0.986   qps ratio: pti on/off0.912   1.021   0.976   qps ratio: pti on / old kernelread-write range=100001       2       8       concurrency488     815     1080    pti enabled480     768     1073    pti disabled504     848     1083    old kernel, no pti—–   —–   —–1.016   1.061   1.006   qps ratio: pti on/off0.968   0.961   0.997   qps ratio: pti on / old kernelread-only range=1001       2       8       concurrency12089   21529   33487   pti enabled12170   21595   33604   pti disabled11948   22479   33876   old kernel, no pti—–   —–   —–0.993   0.996   0.996   qps ratio: pti on/off1.011   0.957   0.988   qps ratio: pti on / old kernelread-only.pre range=100001       2       8       concurrency392     709     876     pti enabled397     707     872     pti disabled403     726     877     old kernel, no pti—–   —–   —–0.987   1.002   1.004   qps ratio: pti on/off0.972   0.976   0.998   qps ratio: pti on / old kernelread-only range=100001       2       8       concurrency394     701     874     pti enabled389     698     871     pti disabled402     725     877     old kernel, no pti—–   —–   —–1.012   1.004   1.003   qps ratio: pti on/off0.980   0.966   0.996   qps ratio: pti on / old kernelpoint-query.pre1       2       8       concurrency18490   31914   56337   pti enabled19107   32201   58331   pti disabled18095   32978   55590   old kernel, no pti—–   —–   —–0.967   0.991   0.965   qps ratio: pti on/off1.021   0.967   1.013   qps ratio: pti on / old kernelpoint-query1       2       8       concurrency18212   31855   56116   pti enabled18913   32123   58320   pti disabled17907   32941   55430   old kernel, no pti—–   —–   —–0.962   0.991   0.962   qps ratio: pti on/off1.017   0.967   1.012   qps ratio: pti on / old kernelrandom-points.pre1       2       8       concurrency3043    5940    8131    pti enabled2944    5681    7984    pti disabled3030    6015    8098    old kernel, no pti—–   —–   —–1.033   1.045   1.018   qps ratio: pti on/off1.004   0.987   1.004   qps ratio: pti on / old kernelrandom-points1       2       8       concurrency3053    5930    8128    pti enabled2949    5756    7981    pti disabled3058    6011    8116    old kernel, no pti—–   —–   —–1.035   1.030   1.018   qps ratio: pti on/off0.998   0.986   1.001   qps ratio: pti on / old kernelhot-points1       2       8       concurrency3931    7522    9500    pti enabled3894    7535    9214    pti disabled3914    7692    9448    old kernel, no pti—–   —–   —–1.009   0.998   1.031   qps ratio: pti on/off1.004   0.977   1.005   qps ratio: pti on / old kernelinsert1       2       8       concurrency12469   21418   25158   pti enabled12561   21327   25094   pti disabled13045   21768   21258   old kernel, no pti—–   —–   —–0.992   1.004   1.002   qps ratio: pti on/off0.955   0.983   1.183   qps ratio: pti on / old kernel

XFS, nobarrier and the 4.13 Linux kernel

tl;drMy day
nobarrier isn’t supported as a mount option for XFS in kernel 4.13.0-26 with Ubuntu 16.04. I assume this isn’t limited to Ubuntu. Read this for more detail on the change.
write throughput is much worse on my SSD without nobarrier
there is no error on the command line when mounting a device that uses the nobarrier option
there is an error message in dmesg output for this

There might be two workarounds:

switch from XFS to ext4
echo “write through” > /sys/block/$device/queue/write_cache

The StoryI have a NUC cluster at home for performance tests with 3 NUC5i3ryh and 3 NUC7i5bnh. I recently replaced the SSD devices in all of them because previous testing wore them out. I use Ubuntu 16.04 LTS and recently upgraded the kernel on some of them to get the fix for Meltdown.The NUC7i5bnh server has a Samsung 960 EVO SSD that uses NVMe. I use the HWE kernel to make wireless work. The old kernel without the Meltdown fix is 4.8.0-36 and the kernel with the Meltdown fix is 4.13.0-26. Note that with the old kernel I used XFS with the nobarrier option. With the new kernel I assumed I was still getting nobarrier, but I was not. I have since switched from XFS to ext4.The NUC5i3ryh server has a Samsung 850 EVO SSD that uses SATA. The old kernel without the Meltdown fix is 4.4.0-38 and the kernel with the Meltdown fix is 4.4.0-109. I continue to use XFS on these.Results sysbench for NUC5i3ryh show not much regression from the Meltdown fix. Results for the NUC7i5bnh show a lot of regression for the write-heavy tests and not much for the read-heavy tests.
I started to debug the odd 7i5bnh results and noticed that write IO throughput was much lower for servers with the Meltdown fix using 4.13.0-26. 
Then I used sysbench fileio to run IO tests without MySQL and noticed that read IO was fine, but write IO throughput was much worse with the 4.13.0-26 kernel.
Then I consulted my local experts, Domas Mituzas and Jens Axboe.
Then I noticed the error message in dmesg output

Meltdown vs MySQL part 1: in-memory sysbench and a core i3 NUC

This is my first performance report for the Meltdown patch using in-memory sysbench and a small server.

the worst case overhead was ~5.5%
a typical overhead was ~2%
QPS was similar between the kernel with the Meltdown fix disabled and the old kernel
the overhead with too much concurrency (8 clients) wasn’t worse than than the overhead without too much concurrency (1 or 2 clients)

Configuration

My usage of sysbench is described here. The servers are described here. For this test I used the core i3 NUC (NUC5i3ryh) with Ubuntu 16.04. I have 3 such servers and ran tests with the fix enabled (kernel 4.4.0-109), the fix disabled via pti=off (kernel 4.4.0-109) and the old kernel (4.4.0-38) that doesn’t have the fix. From cat /proc/cpuinfo I see pcid.

The servers have 2 cores and 4 HW threads. I normally use them for low-concurrency benchmarks with 1 or 2 concurrent database clients. For this test I used 1, 2 and 8 concurrent clients to determine whether more concurrency and more mutex contention would cause more of a performance loss.

The sysbench test was configured to use 1 table with 4M rows and InnoDB. The InnoDB buffer pool was large enough to cache the table. The sysbench client runs on the same host as mysqld.

Results

My usage of sysbench is described here which explains the tests that I list below. Each test has QPS for 1, 2 and 8 concurrent clients. Results are provided for

pti enabled – kernel 4.4.0-109 with the Meltdown fix enabled
pti disabled – kernel 4.4.0-109 with the Meltdown fix disabled via pti=off
old kernel, no pti – kernel 4.4.0-38 which doesn’t have the Meltdown fix

After each of the QPS sections, there are two lines for QPS ratios. The first line compares the QPS for the kernel with the Meltdown fix enabled vs disabled. The second line compares the QPS for the kernel with the Meltdown fix vs the old kernel. A value less than one means that MySQL gets less QPS with the Meltdown fix.

update-inlist
1       2       8       concurrency
2039    2238    2388    pti enabled
2049    2449    2369    pti disabled
2059    2199    2397    old kernel, no pti
—–   —–   —–
0.995   0.913   1.008   qps ratio: pti on/off
0.990   1.017   0.996   qps ratio: pti on / old kernel

update-one
1       2       8       concurrency
8086    11407   9498    pti enabled
8234    11683   9748    pti disabled
8215    11708   9755    old kernel, no pti
—–   —–   —–
0.982   0.976   0.974   qps ratio: pti on/off
0.984   0.974   0.973   qps ratio: pti on / old kernel

update-index
1       2       8       concurrency
2944    4528    7330    pti enabled
3022    4664    7504    pti disabled
3020    4784    7555    old kernel, no pti
—–   —–   —–
0.974   0.970   0.976   qps ratio: pti on/off
0.974   0.946   0.970   qps ratio: pti on / old kernel

update-nonindex
1       2       8       concurrency
6310    8688    12600   pti enabled
6103    8482    11900   pti disabled
6374    8723    12142   old kernel, no pti
—–   —–   —–
1.033   1.024   1.058   qps ratio: pti on/off
0.989   0.995   1.037   qps ratio: pti on / old kernel

delete
1       2       8       concurrency
12348   17087   23670   pti enabled
12568   17342   24448   pti disabled
12665   17749   24499   old kernel, no pti
—–   —–   —–
0.982   0.985   0.968   qps ratio: pti on/off
0.974   0.962   0.966   qps ratio: pti on / old kernel

read-write range=100
1       2       8       concurrency
 9999   14973   21618   pti enabled
10177   15239   22088   pti disabled
10209   15249   22153   old kernel, no pti
—–   —–   —–
0.982   0.982   0.978   qps ratio: pti on/off
0.979   0.981   0.975   qps ratio: pti on / old kernel

read-write range=10000
1       2       8       concurrency
430     762     865     pti enabled
438     777     881     pti disabled
439     777     882     old kernel, no pti
—–   —–   —–
0.981   0.980   0.981   qps ratio: pti on/off
0.979   0.980   0.980   qps ratio: pti on / old kernel

read-only range=100
1       2       8       concurrency
10472   19016   26631   pti enabled
10588   20124   27587   pti disabled
11290   20153   27796   old kernel, no pti
—–   —–   —–
0.989   0.944   0.965   qps ratio: pti on/off
0.927   0.943   0.958   qps ratio: pti on / old kernel

read-only.pre range=10000
1       2       8       concurrency
346     622     704     pti enabled
359     640     714     pti disabled
356     631     715     old kernel, no pti
—–   —–   —–
0.963   0.971   0.985   qps ratio: pti on/off
0.971   0.985   0.984   qps ratio: pti on / old kernel

read-only range=10000
1       2       8       concurrency
347     621     703     pti enabled
354     633     716     pti disabled
354     638     716     old kernel, no pti
—–   —–   —–
0.980   0.981   0.988   qps ratio: pti on/off
0.980   0.973   0.981   qps ratio: pti on / old kernel

point-query.pre
1       2       8       concurrency
16104   29540   46863   pti enabled
16716   30052   49404   pti disabled
16605   30392   49872   old kernel, no pti
—–   —–   —–
0.963   0.982   0.948   qps ratio: pti on/off
0.969   0.971   0.939   qps ratio: pti on / old kernel

point-query
1       2       8       concurrency
16240   29359   47141   pti enabled
16640   29785   49015   pti disabled
16369   30226   49530   old kernel, no pti
—–   —–   —–
0.975   0.985   0.961   qps ratio: pti on/off
0.992   0.971   0.951   qps ratio: pti on / old kernel

random-points.pre
1       2       8       concurrency
2756    5202    6211    pti enabled
2764    5216    6245    pti disabled
2679    5130    6188    old kernel, no pti
—–   —–   —–
0.997   0.997   0.994   qps ratio: pti on/off
1.028   1.014   1.003   qps ratio: pti on / old kernel

random-points
1       2       8       concurrency
2763    5177    6191    pti enabled
2768    5188    6238    pti disabled
2701    5076    6182    old kernel, no pti
—–   —–   —–
0.998   0.997   0.992   qps ratio: pti on/off
1.022   1.019   1.001   qps ratio: pti on / old kernel

hot-points
1       2       8       concurrency
3414    6533    7285    pti enabled
3466    6623    7287    pti disabled
3288    6312    6998    old kernel, no pti
—–   —–   —–
0.984   0.986   0.999   qps ratio: pti on/off
1.038   1.035   1.041   qps ratio: pti on / old kernel

insert
1       2       8       concurrency
7612    10051   11943   pti enabled
7713    10150   12322   pti disabled
7834    10243   12514   old kernel, no pti
—–   —–   —–
0.986   0.990   0.969   qps ratio: pti on/off
0.971   0.981   0.954   qps ratio: pti on / old kernel

Sysbench: IO-bound and a fast server, part 2

This post has results for sysbench with an IO-bound workload and a fast server. It uses 1 table with 800M rows. The previous post had results for 8 tables with 100M rows/table. The goal is to understand the impact from more contention. Before that I published a report comparing sysbench with 8 tables vs 1 table for an in-memory workload.All of the data is on github. With the exception of the scan section, the graphs below have the absolute QPS for 48 concurrent clients using 8 tables and 1 table.tl;dr
MyRocks scans are up to 2X faster with readahead
update-one – all engines do worse. This is expected.
update-index – InnoDB 5.6 & 5.7 do worse. This doesn’t repeat on the in-memory benchmark.
read-write – InnoDB 5.6 does better on the 1 table test. This is odd.
hot-points – all engines do worse. This is expected. MyRocks can do 3X better by switching from LRU to Clock once that feature is GA.
insert – InnoDB gets ~50% of the QPS with 1 table vs 8 tables.

scanThis scan test uses one connection to scan the 800M row table. For the previous post there were 8 connections each scanning a different table with 100M rows/table. Fortunately the results here are similar to the results from the previous test.
InnoDB 5.6 is faster than MyRocks without readahead.
MyRocks with readahead is faster than InnoDB 5.6 but slower than InnoDB 5.7/8.0

update-oneAll engines lose QPS with 1 table, the result is expected given all updates go to one row on this test. The result here is similar to the in-memory benchmark.
update-indexMyRocks and InnoDB 8.0 do great. InnoDB 5.6/5.7 lose QPS with 1 table but I did not debug that.
read-write range=100Most engines do the same for 1 vs 8 tables. InnoDB 5.6 does much better with 1 table but I did not debug that.
hot-pointsAll engines lose QPS with 1 table and this is similar to the in-memory benchmark. That is expected because the test fetches the same 100 rows per query and this stays in-memory. The QPS for MyRocks can be ~3X better by switching from using LRU to Clock for the block cache but that feature might not be GA today.
insertInnoDB gets ~2X more QPS with 8 tables than with 1. That result is similar to the in-memory benchmark. I didn’t debug the source of contention.

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