About ZFS Performance


If you are a regular reader of this blog, you likely know I like the ZFS filesystem a lot. ZFS has many very interesting features, but I am a bit tired of hearing negative statements on ZFS performance. It feels a bit like people are telling me “Why do you use InnoDB? I have read that MyISAM is faster.” I found the comparison of InnoDB vs. MyISAM quite interesting, and I’ll use it in this post.

To have some data to support my post, I started an AWS i3.large instance with a 1000GB gp2 EBS volume. A gp2 volume of this size is interesting because it is above the burst IOPS level, so it offers a constant 3000 IOPS performance level.

I used sysbench to create a table of 10M rows and then, using export/import tablespace, I copied it 329 times. I ended up with 330 tables for a total size of about 850GB. The dataset generated by sysbench is not very compressible, so I used lz4 compression in ZFS. For the other ZFS settings, I used what can be found in my earlier ZFS posts but with the ARC size limited to 1GB. I then used that plain configuration for the first benchmarks. Here are the results with the sysbench point-select benchmark, a uniform distribution and eight threads. The InnoDB buffer pool was set to 2.5GB.

In both cases, the load is IO bound. The disk is doing exactly the allowed 3000 IOPS. The above graph appears to be a clear demonstration that XFS is much faster than ZFS, right? But is that really the case? The way the dataset has been created is extremely favorable to XFS since there is absolutely no file fragmentation. Once you have all the files opened, a read IOP is just a single fseek call to an offset and ZFS doesn’t need to access any intermediate inode. The above result is about as fair as saying MyISAM is faster than InnoDB based only on table scan performance results of unfragmented tables and default configuration. ZFS is much less affected by the file level fragmentation, especially for point access type.

More on ZFS metadata

ZFS stores the files in B-trees in a very similar fashion as InnoDB stores data. To access a piece of data in a B-tree, you need to access the top level page (often called root node) and then one block per level down to a leaf-node containing the data. With no cache, to read something from a three levels B-tree thus requires 3 IOPS.

Simple three levels B-tree

The extra IOPS performed by ZFS are needed to access those internal blocks in the B-trees of the files. These internal blocks are labeled as metadata. Essentially, in the above benchmark, the ARC is too small to contain all the internal blocks of the table files’ B-trees. If we continue the comparison with InnoDB, it would be like running with a buffer pool too small to contain the non-leaf pages. The test dataset I used has about 600MB of non-leaf pages, about 0.1% of the total size, which was well cached by the 3GB buffer pool. So only one InnoDB page, a leaf page, needed to be read per point-select statement.

To correctly set the ARC size to cache the metadata, you have two choices. First, you can guess values for the ARC size and experiment. Second, you can try to evaluate it by looking at the ZFS internal data. Let’s review these two approaches.

You’ll read/hear often the ratio 1GB of ARC for 1TB of data, which is about the same 0.1% ratio as for InnoDB. I wrote about that ratio a few times, having nothing better to propose. Actually, I found it depends a lot on the recordsize used. The 0.1% ratio implies a ZFS recordsize of 128KB. A ZFS filesystem with a recordsize of 128KB will use much less metadata than another one using a recordsize of 16KB because it has 8x fewer leaf pages. Fewer leaf pages require less B-tree internal nodes, hence less metadata. A filesystem with a recordsize of 128KB is excellent for sequential access as it maximizes compression and reduces the IOPS but it is poor for small random access operations like the ones MySQL/InnoDB does.

To determine the correct ARC size, you can slowly increase the ARC size and monitor the number of metadata cache-misses with the arcstat tool. Here’s an example:

# echo 1073741824 > /sys/module/zfs/parameters/zfs_arc_max
# arcstat -f time,arcsz,mm%,mhit,mread,dread,pread 10
    time  arcsz  mm%  mhit  mread  dread  pread
10:22:49   105M    0     0     0      0      0
10:22:59   113M  100     0    22     73      0
10:23:09   120M  100     0    20     68      0
10:23:19   127M  100     0    20     65      0
10:23:29   135M  100     0    22     74      0

You’ll want the ‘mm%’, the metadata missed percent, to reach 0. So when the ‘arcsz’ column is no longer growing and you still have high values for ‘mm%’, that means the ARC is too small. Increase the value of ‘zfs_arc_max’ and continue to monitor.

If the 1GB of ARC for 1TB of data ratio is good for large ZFS recordsize, it is likely too small for a recordsize of 16KB. Does 8x more leaf pages automatically require 8x more ARC space for the non-leaf pages? Although likely, let’s verify.

The second option we have is the zdb utility that comes with ZFS, which allows us to view many internal structures including the B-tree list of pages for a given file. The tool needs the inode of a file and the ZFS filesystem as inputs. Here’s an invocation for one of the tables of my dataset:

# cd /var/lib/mysql/data/sbtest
# ls -li | grep sbtest1.ibd
36493 -rw-r----- 1 mysql mysql 2441084928 avr 15 15:28 sbtest1.ibd
# zdb -ddddd mysqldata/data 36493 > zdb5d.out
# more zdb5d.out
Dataset mysqldata/data [ZPL], ID 90, cr_txg 168747, 4.45G, 26487 objects, rootbp DVA[0]=<0:1a50452800:200> DVA[1]=<0:5b289c1600:200> [L0 DMU objset] fletcher4 lz4 LE contiguous unique double size=800L/200P birth=3004977L/3004977P fill=26487 cksum=13723d4400:5d1f47fb738:fbfb87e6e278:1f30c12b7fa1d1
    Object  lvl   iblk   dblk  dsize  lsize   %full  type
     36493    4    16K    16K  1.75G  2.27G   97.62  ZFS plain file
                                        168   bonus  System attributes
        dnode maxblkid: 148991
        path    /var/lib/mysql/data/sbtest/sbtest1.ibd
        uid     103
        gid     106
        atime   Sun Apr 15 15:04:13 2018
        mtime   Sun Apr 15 15:28:45 2018
        ctime   Sun Apr 15 15:28:45 2018
        crtime  Sun Apr 15 15:04:13 2018
        gen     3004484
        mode    100640
        size    2441084928
        parent  36480
        links   1
        pflags  40800000004
Indirect blocks:
               0 L3    0:1a4ea58800:400 4000L/400P F=145446 B=3004774/3004774
               0  L2   0:1c83454c00:1800 4000L/1800P F=16384 B=3004773/3004773
               0   L1  0:1eaa626400:1600 4000L/1600P F=128 B=3004773/3004773
               0    L0 0:1c6926ec00:c00 4000L/c00P F=1 B=3004773/3004773
            4000    L0 EMBEDDED et=0 4000L/6bP B=3004484
            8000    L0 0:1c69270c00:400 4000L/400P F=1 B=3004773/3004773
            c000    L0 0:1c7fbae400:800 4000L/800P F=1 B=3004736/3004736
           10000    L0 0:1ce3f53600:3200 4000L/3200P F=1 B=3004484/3004484
           14000    L0 0:1ce3f56800:3200 4000L/3200P F=1 B=3004484/3004484
           18000    L0 0:18176fa600:3200 4000L/3200P F=1 B=3004485/3004485
           1c000    L0 0:18176fd800:3200 4000L/3200P F=1 B=3004485/3004485
           [more than 140k lines truncated]

The last section of the above output is very interesting as it shows the B-tree pages. The ZFSB-tree of the file sbtest1.ibd has four levels. L3 is the root page, L2 is the first level (from the top) pages, L1 are the second level pages, and L0 are the leaf pages. The metadata is essentially L3 + L2 + L1. When you change the recordsize property of a ZFS filesystem, you affect only the size of the leaf pages.

The non-leaf page size is always 16KB (4000L) and they are always compressed on disk with lzop (If I read correctly). In the ARC, these pages are stored uncompressed so they use 16KB of memory each. The fanout of a ZFS B-tree, the largest possible ratio of a number of pages between levels, is 128. With the above output, we can easily calculate the required size of metadata we would need to cache all the non-leaf pages in the ARC.

# grep -c L3 zdb5d.out
# grep -c L2 zdb5d.out
# grep -c L1 zdb5d.out
# grep -c L0 zdb5d.out

So, each of the 330 tables of the dataset has 1160 non-leaf pages and 145447 leaf pages; a ratio very close to the prediction of 0.8%. For the complete dataset of 749GB, we would need the ARC to be, at a minimum, 6GB to fully cache all the metadata pages. Of course, there is some overhead to add. In my experiments, I found I needed to add about 15% for ARC overhead in order to have no metadata reads at all. The real minimum for the ARC size I should have used is almost 7GB.

Of course, an ARC of 7GB on a server with 15GB of Ram is not small. Is there a way to do otherwise? The first option we have is to use a larger InnoDB page size, as allowed by MySQL 5.7. Instead of the regular Innodb page size of 16KB, if you use a page size of 32KB with a matching ZFS recordsize, you will cut the ARC size requirement by half, to 0.4% of the uncompressed size.

Similarly, an Innodb page size of 64KB with similar ZFS recordsize would further reduce the ARC size requirement to 0.2%. That approach works best when the dataset is highly compressible. I’ll blog more about the use of larger InnoDB pages with ZFS in a near future. If the use of larger InnoDB page sizes is not a viable option for you, you still have the option of using the ZFS L2ARC feature to save on the required memory.

So, let’s proposed a new rule of thumb for the required ARC/L2ARC size for a a given dataset:

  • Recordsize of 128KB => 0.1% of the uncompressed dataset size
  • Recordsize of 64KB => 0.2% of the uncompressed dataset size
  • Recordsize of 32KB => 0.4% of the uncompressed dataset size
  • Recordsize of 16KB => 0.8% of the uncompressed dataset size

The ZFS revenge

In order to improve ZFS performance, I had 3 options:

  1. Increase the ARC size to 7GB
  2. Use a larger Innodb page size like 64KB
  3. Add a L2ARC

I was reluctant to grow the ARC to 7GB, which was nearly half the overall system memory. At best, the ZFS performance would only match XFS. A larger InnoDB page size would increase the CPU load for decompression on an instance with only two vCPUs; not great either. The last option, the L2ARC, was the most promising.

The choice of an i3.large instance type is not accidental. The instance has a 475GB ephemeral NVMe storage device. Let’s try to use this storage for the ZFS L2ARC. The warming of an L2ARC device is not exactly trivial. In my case, with a 1GB ARC, I used:

echo 1073741824 > /sys/module/zfs/parameters/zfs_arc_max
echo 838860800 > /sys/module/zfs/parameters/zfs_arc_meta_limit
echo 67108864 > /sys/module/zfs/parameters/l2arc_write_max
echo 134217728 > /sys/module/zfs/parameters/l2arc_write_boost
echo 4 > /sys/module/zfs/parameters/l2arc_headroom
echo 16 > /sys/module/zfs/parameters/l2arc_headroom_boost
echo 0 > /sys/module/zfs/parameters/l2arc_norw
echo 1 > /sys/module/zfs/parameters/l2arc_feed_again
echo 5 > /sys/module/zfs/parameters/l2arc_feed_min_ms
echo 0 > /sys/module/zfs/parameters/l2arc_noprefetch

I then ran ‘cat /var/lib/mysql/data/sbtest/* > /dev/null’ to force filesystem reads and caches on all of the tables. A key setting here to allow the L2ARC to cache data is the zfs_arc_meta_limit. It needs to be slightly smaller than the zfs_arc_max in order to allow some data to be cache in the ARC. Remember that the L2ARC is fed by the LRU of the ARC. You need to cache data in the ARC in order to have data cached in the L2ARC. Using lz4 in ZFS on the sysbench dataset results in a compression ration of only 1.28x. A more realistic dataset would compress by more than 2x, if not 3x. Nevertheless, since the content of the L2ARC is compressed, the 475GB device caches nearly 600GB of the dataset. The figure below shows the sysbench results with the L2ARC enabled:

Now, the comparison is very different. ZFS completely outperforms XFS, 5000 qps for ZFS versus 3000 for XFS. The ZFS results could have been even higher but the two vCPUs of the instance were clearly the bottleneck. Properly configured, ZFS can be pretty fast. Of course, I could use flashcache or bcache with XFS and improve the XFS results but these technologies are way more exotic than the ZFS L2ARC. Also, only the L2ARC stores data in a compressed form, maximizing the use of the NVMe device. Compression also lowers the size requirement and cost for the gp2 disk.

ZFS is much more complex than XFS and EXT4 but, that also means it has more tunables/options. I used a simplistic setup and an unfair benchmark which initially led to poor ZFS results. With the same benchmark, very favorable to XFS, I added a ZFS L2ARC and that completely reversed the situation, more than tripling the ZFS results, now 66% above XFS.


We have seen in this post why the general perception is that ZFS under-performs compared to XFS or EXT4. The presence of B-trees for the files has a big impact on the amount of metadata ZFS needs to handle, especially when the recordsize is small. The metadata consists mostly of the non-leaf pages (or internal nodes) of the B-trees. When properly cached, the performance of ZFS is excellent. ZFS allows you to optimize the use of EBS volumes, both in term of IOPS and size when the instance has fast ephemeral storage devices. Using the ephemeral device of an i3.large instance for the ZFS L2ARC, ZFS outperformed XFS by 66%.

The post About ZFS Performance appeared first on Percona Database Performance Blog.


AWS introduces 1-click Lambda functions app for IoT

When Amazon introduced AWS Lambda in 2015, the notion of serverless computing was relatively unknown. It enables developers to deliver software without having to manage a server to do it. Instead, Amazon manages it all and the underlying infrastructure only comes into play when an event triggers a requirement. Today, the company released an app in the iOS App Store called AWS IoT 1-Click to bring that notion a step further.

The 1-click part of the name may be a bit optimistic, but the app is designed to give developers even quicker access to Lambda event triggers. These are designed specifically for simple single-purpose devices like a badge reader or a button. When you press the button, you could be connected to customer service or maintenance or whatever makes sense for the given scenario.

One particularly good example from Amazon is the Dash Button. These are simple buttons that users push to reorder goods like laundry detergent or toilet paper. Pushing the button connects to the device to the internet via the home or business’s WiFi and sends a signal to the vendor to order the product in the pre-configured amount. AWS IoT 1-Click extends this capability to any developers, so long as it is on a supported device.

To use the new feature, you need to enter your existing account information. You configure your WiFi and you can choose from a pre-configured list of devices and Lambda functions for the given device. Supported devices in this early release include AWS IoT Enterprise Button, a commercialized version of the Dash button and the AT&T LTE-M Button.

Once you select a device, you define the project to trigger a Lambda function, or send an SMS or email, as you prefer. Choose Lambda for an event trigger, then touch Next to move to the configuration screen where you configure the trigger action. For instance, if pushing the button triggers a call to IT from the conference room, the trigger would send a page to IT that there was a call for help in the given conference room.

Finally, choose the appropriate Lambda function, which should work correctly based on your configuration information.

All of this obviously requires more than one click and probably involves some testing and reconfiguring to make sure you’ve entered everything correctly, but the idea of having an app to create simple Lambda functions could help people with non-programming background configure buttons with simple functions with some training on the configuration process.

It’s worth noting that the service is still in Preview, so you can download the app today, but you have to apply to participate at this time.


This Week in Data with Colin Charles 39: a valuable time spent at

Colin Charles

Colin CharlesJoin Percona Chief Evangelist Colin Charles as he covers happenings, gives pointers and provides musings on the open source database community. 2018 just ended, and it was very enjoyable to be in Bangalore for the conference. The audience was large, the conversations were great, and overall I think this is a rather important conference if you’re into the “DevOps” movement (or are a sysadmin!). From the data store world, Oracle MySQL was a sponsor, as was MyDBOPS (blog), and Elastic. There were plenty more, including Digital Ocean/GoJek/Walmart Labs — many MySQL users.

I took a handful of pictures with people, and here are some of the MyDBOPS team and myself.  They have over 20 employees, and serve the Indian market at rates that would be more palatable than straight up USD rates. Traveling through Asia, many businesses always do find local partners and offer local pricing; this really becomes more complex in the SaaS space (everyone pays the same rate generally) and also the services space.

Colin at Rootconf with Oracle
Some of the Oracle MySQL team who were exhibiting were very happy they got a good amount of traffic to the booth based on stuff discussed at the talk and BOF.

From a talk standpoint, I did a keynote for an hour and also a BoF session for another hour (great discussion, lots of blog post ideas from there), and we had a Q&A session for about 15 minutes. There were plenty of good conversations in the hallway track.

A quick observation that I notice happens everywhere: many people don’t realize features that have existed in MySQL since 5.6/5.7.  So they are truly surprised with stuff in 8.0 as well. It is clear there is a huge market that would thrive around education. Not just around feature checklists, but also around how to use features. Sometimes, this feels like the MySQL of the mid-2000’s — getting apps to also use new features, would be a great thing.


This seems to have been a quiet week on the releases front.

Are you a user of Amazon Aurora MySQL? There is now the Amazon Aurora Backtrack feature, which allows you to go back in time. It is described to work as:

Aurora uses a distributed, log-structured storage system (read Design Considerations for High Throughput Cloud-Native Relational Databases to learn a lot more); each change to your database generates a new log record, identified by a Log Sequence Number (LSN). Enabling the backtrack feature provisions a FIFO buffer in the cluster for storage of LSNs. This allows for quick access and recovery times measured in seconds.

Link List

Upcoming appearances


I look forward to feedback/tips via e-mail at or on Twitter @bytebot.


The post This Week in Data with Colin Charles 39: a valuable time spent at appeared first on Percona Database Performance Blog.


How to Enable Amazon RDS Remote Access

Amazon RDS remote access

It’s easy to enable Amazon RDS remote access when launching an Amazon RDS instance, but there can be many issues. I created this blog as a guide describing the various issues/configurations we might encounter.

As the first step, we need to select a VPC where we will launch our Amazon RDS instance. The default VPC has all the required settings to make the instance remotely available; we just have to enable it by selecting “Yes” at Public accessibility.

For this example, we used the Default VPC and asked AWS to create a new security group.

Once the instance is created, we can connect to the “Endpoint” address:

[root@server1 ~]# mysql -h -u dbuser -p
Enter password: XXXXXX
mysql> s
mysql  Ver 14.14 Distrib 5.7.19-17, for Linux (x86_64) using  6.2
Connection id: 14
Current database:
Current user:
SSL: Cipher in use is AES256-SHA
Current pager: stdout
Using outfile: ''
Using delimiter: ;
Server version: 5.6.37 MySQL Community Server (GPL)
Protocol version: 10
Connection: via TCP/IP
Server characterset: latin1
Db     characterset: latin1
Client characterset: utf8
Conn.  characterset: utf8
TCP port: 3306
Uptime: 1 min 56 sec
Threads: 2  Questions: 9986  Slow queries: 0 Opens: 319  Flush tables: 1 Open tables: 80  Queries per second avg: 86.086

When AWS creates the security group after we select the option to make it publicly accessible, it appears that AWS takes care of everything. But what if we check the created security groups?

It created a rule to enable incoming traffic, as security group works as a whitelist (it denies everything except the matching rules). 

As we can see here, AWS only created the inbound rule for my current IP address, which means once we change IPs or try to connect from another server, it will fail. To get around that, we need to add another rule:

Adding the rule opens the port for the world. This is dangerous! Since anyone can try connecting, it’s much better if we can supply a list of IPs or ranges we want enabled for remote access, even from outside of AWS.

Running remotely accessible RDS in custom VPC

To run RDS in a new VPC or in an existing VPC, we need to ensure a couple of things. 

The VPC needs to have at least two subnets. We believe this is something Amazon asks so that the VPC is ready if you choose to move to a Multi-AZ master, or to simply spread the read-only instances across multiple AZ for higher availability.

If you want to make the RDS cluster remotely available, we need to attach an IGW (Internet Gateway) to the VPC. If you don’t, it isn’t able to communicate with the outside world. To do that, go to VPC -> Internet gateways and hit “Create Internet Gateway”:

Once it’s created, select “Attach to VPC” and select your VPC. 

Still, you won’t be able to reach the internet as we need to add route towards the newly attached internet gateway. 

To do that, go to “Route Tables” and select our VPC, and add the following route ( means it’s going to be the default gateway, and all non-internal traffic needs to be routed towards it):

Hit Save. Now the VPC has Internet access, just like AWS’s Default VPC.

The post How to Enable Amazon RDS Remote Access appeared first on Percona Database Performance Blog.


The Final Countdown: Are You Ready for Percona Live 2018?

Are you ready for Percona Live

Are you ready for Percona Live 2018It’s hard to believe Percona Live 2018 starts on Monday! We’re looking forward to seeing everyone in Santa Clara next week! Here are some quick highlights to remember:

  • In addition to all the amazing sessions and keynotes we’ve announced, we’ll be hosting the MySQL Community Awards and the Lightning Talks on Monday during the Opening Reception.
  • We’ve also got a great lineup of demos in the exhibit hall all day Tuesday and Wednesday – be sure to stop by and learn more about open source database products and tools.
  • On Monday, we have a special China Track now available from Alibaba Cloud, PingCAP and Shannon Systems. We’ve just put a $20.00 ticket on sale for that track, and if you have already purchased any of our other tickets, you are also welcome to attend those four sessions.
  • Don’t forget to make your reservation at the Community Dinner. It’s a great opportunity to socialize with everyone and Pythian is always a wonderful host!

Thanks to everyone who is sponsoring, presenting and attending! The community is who makes this event successful and so much fun to be a part of!

The post The Final Countdown: Are You Ready for Percona Live 2018? appeared first on Percona Database Performance Blog.


Percona Monitoring and Management (PMM) 1.10.0 Is Now Available

Percona Monitoring and Management

Percona Monitoring and Management (PMM) is a free and open-source platform for managing and monitoring MySQL® and MongoDB® performance. You can run PMM in your own environment for maximum security and reliability. It provides thorough time-based analysis for MySQL® and MongoDB® servers to ensure that your data works as efficiently as possible.

Percona Monitoring and ManagementWe focused mainly on two features in 1.10.0, but there are also several notable improvements worth highlighting:

  • Annotations – Record and display Application Events as Annotations using pmm-admin annotate
  • Grafana 5.0 – Improved visualization effects
  • Switching between Dashboards – Restored functionality to preserve host when switching dashboards
  • New Percona XtraDB Cluster Overview graphs – Added Galera Replication Latency graphs on Percona XtraDB Cluster Overview dashboard with consistent colors

The issues in the release include four new features & improvements, and eight bugs fixed.


Application events are one of the contributors to changes in database performance characteristics, and in this release PMM now supports receiving events and displaying them as Annotations using the new command pmm-admin annotate. A recent Percona survey reveals that Database and DevOps Engineers highly value visibility into the Application layer.  By displaying Application Events on top of your PMM graphs, Engineers can now correlate Application Events (common cases: Application Deploys, Outages, and Upgrades) against Database and System level metric changes.


For example, you have just completed an Application deployment to version 1.2, which is relevant to UI only, so you want to set tags for the version and interface impacted:

pmm-admin annotate "Application deploy v1.2" --tags "UI, v1.2"

Using the optional --tags allows you to filter which Annotations are displayed on the dashboard via a toggle option.  Read more about Annotations utilization in the Documentation.

Grafana 5.0

We’re extremely pleased to see Grafana ship 5.0 and we were fortunate enough to be at Grafanacon, including Percona’s very own Dimitri Vanoverbeke (Dim0) who presented What we Learned Integrating Grafana and Prometheus!



Included in Grafana 5.0 are a number of dramatic improvements, which in future Percona Monitoring and Management releases we plan to extend our usage of each feature, and the one we like best is the virtually unlimited way you can size and shape graphs.  No longer are you bound by panel constraints to keep all objects at the same fixed height!  This improvement indirectly addresses the visualization error in PMM Server where some graphs would appear to be on two lines and ended up wasting screen space.

Switching between Dashboards

PMM now allows you to navigate between dashboards while maintaining the same host under observation, so that for example you can start on MySQL Overview looking at host serverA, switch to MySQL InnoDB Advanced dashboard and continue looking at serverA, thus saving you a few clicks in the interface.

New Percona XtraDB Cluster Galera Replication Latency Graphs

We have added new Percona XtraDB Cluster Replication Latency graphs on our Percona XtraDB Cluster Galera Cluster Overview dashboard so that you can compare latency across all members in a cluster in one view.

Issues in this release

New Features & Improvements

  • PMM-2330Application Annotations DOC Update
  • PMM-2332Grafana 5 DOC Update
  • PMM-2293Add Galera Replication Latency Graph to Dashboard PXC/Galera Cluster Overview RC Ready
  • PMM-2295Improve color selection on Dashboard PXC/Galera Cluster Overview RC Ready

Bugs fixed

  • PMM-2311Fix misalignment in Query Analytics Metrics table RC Ready
  • PMM-2341Typo in text on password page of OVF RC Ready
  • PMM-2359Trim leading and trailing whitespaces for all fields on AWS/OVF Installation wizard RC Ready
  • PMM-2360Include a “What’s new?” link for Update widget RC Ready
  • PMM-2346Arithmetic on InnoDB AHI Graphs are invalid DOC Update
  • PMM-2364QPS are wrong in QAN RC Ready
  • PMM-2388Query Analytics does not render fingerprint section in some cases DOC Update
  • PMM-2371Pass host when switching between Dashboards

How to get PMM

PMM is available for installation using three methods:

Help us improve our software quality by reporting any Percona Monitoring and Management bugs you encounter using our bug tracking system.

The post Percona Monitoring and Management (PMM) 1.10.0 Is Now Available appeared first on Percona Database Performance Blog.


Webinar Tuesday April 17, 2018: Which Amazon Cloud Technology Should You Chose? RDS? Aurora? Roll Your Own?

Amazon Cloud Technology

Amazon Cloud TechnologyPlease join Percona’s Senior Technical Operations Engineer, Daniel Kowalewski as he presents Which Amazon Cloud Technology Should You Chose? RDS? Aurora? Roll Your Own? on Tuesday, April 17, 2018, at 10:00 am PDT (UTC-7) / 1:00 pm EDT (UTC-4).

Are you running on Amazon, or planning to migrate there? In this talk, we are going to cover the different technologies for running databases on Amazon Cloud environments.

We will focus on the operational aspects, benefits and limitations for each of them.

Register for the webinar now.

Amazon Cloud TechnologyDaniel Kowalewski, Senior Technical Operations Engineer

Daniel joined Percona in August of 2015. Previously, he earned a B.S. in Computer Science from the University of Colorado in 2006 and was a DBA there until he joined Percona. In addition to MySQL, Daniel also has experience with Oracle and Microsoft SQL Server, but he much prefers to stay in the MySQL world. Daniel lives near Denver, CO with his wife, two-year-old son, and dog. If you can’t reach him, he’s probably in the mountains hiking, camping, or trying to get lost.

The post Webinar Tuesday April 17, 2018: Which Amazon Cloud Technology Should You Chose? RDS? Aurora? Roll Your Own? appeared first on Percona Database Performance Blog.


This Week in Data with Colin Charles 35: Percona Live 18 final countdown and a roundup of recent news

Colin Charles

Colin CharlesJoin Percona Chief Evangelist Colin Charles as he covers happenings, gives pointers and provides musings on the open source database community.

Percona Live is just over a week away — there’s an awesome keynote lineup, and you really should register. Also don’t forget to save the date as Percona Live goes to Frankfurt, Germany November 5-7 2018! Prost!

In acquisitions, we have seen MariaDB acquire MammothDB and Idera acquire Webyog.

Some interesting Amazon notes: Amazon Aurora Continues its Torrid Growth, More than Doubling the Number of Active Customers in the Last Year (not sure I’d describe it as torrid but this is great for MySQL and PostgreSQL), comes with a handful of customer mentions. In addition, there have already been 65,000 database migrations on AWS. For context, in late November 2017, it was 40,000 database migrations.


Link List

Upcoming appearances


I look forward to feedback/tips via e-mail at or on Twitter @bytebot.


The post This Week in Data with Colin Charles 35: Percona Live 18 final countdown and a roundup of recent news appeared first on Percona Database Performance Blog.


With Fargate, AWS wants to make containers more cloud native

At its re:Invent developer conference, AWS made so many announcements that even some of the company’s biggest launches only got a small amount of attention. While the company’s long-awaited Elastic Container Service for Kubernetes got quite a bit of press, the launch of the far more novel Fargate container service stayed under the radar.

When I talked to him earlier this week, AWS VP and Amazon CTO (and EDM enthusiast) Werner Vogels admitted as much. “I think some of the Fargate stuff got a bit lost in all the other announcements that there were,” he told me. “I think it is a major step forward in making containers more cloud native and we see quite a few of our customers jumping on board with Fargate.”

Fargate, if you haven’t followed along, is a technology for AWS’ Elastic Container Service (ECS) and Kubernetes Service (EKS) that abstracts all of the underlying infrastructure for running containers away. You pick your container orchestration engine and the service does the rest. There’s no need for managing individual servers or clusters. Instead, you simply tells ECS or EKS that you want to launch a container with Fargate, define the CPU and memory requirements of your application and let the service handle the rest.

To Vogels, who also published a longer blog post on Fargate today, the service is part of the company’s mission to help developers focus on their applications — and not the infrastructure. “I always compare it a bit to the early days of cloud,” said Vogels. “Before we had AWS, there were only virtual machines. And many companies build successful businesses around it. But when you run virtual machines, you still have to manage the hardware. […] One of the things that happened when we introduced EC2 [the core AWS cloud computing service] in the early days, was sort of that it decoupled things from the hardware. […] I think that tremendously improved developer productivity.”

But even with the early containers tools, if you wanted to run them directly on AWS or even in ECS, you still had to do a lot of work that had little to do with actually running the containers. “Basically, it’s the same story,” Vogels said. “VMs became the hardware for the containers. And a significant amount of work for developers went into that orchestration piece.”

What Amazon’s customers wanted, however, was being able to focus on running their containers — not what Vogels called the “hands-on hardware-type of management.” “That was so pre-cloud,” he added and in his blog post today, he also notes that “container orchestration has always seemed to me to be very not cloud native.”

In Vogels’ view, it seems, if you are still worried about infrastructure, you’re not really cloud native. He also noted that the original promise of AWS was that AWS would worry about running the infrastructure while developers got to focus on what mattered for their businesses. It’s services like Fargate and maybe also Lambda that take this overall philosophy the furthest.

Even with a container service like ECS or EKS, though, the clusters still don’t run completely automatically and you still end up provisioning capacity that you don’t need all the time. The promise of Fargate is that it will auto-scale for you and that you only pay for the capacity you actually need.

“Our customers, they just want to build software, they just want to build their applications. They don’t want to be bothered with how to exactly map this container down to that particular virtual machine — which is what they had to do,” Vogels said. “With Fargate, you select the type of CPUs you want to use for a particular task and it will autoscale this for you. Meaning that you actually only have to pay for the capacity you use.”

When it comes to abstracting away infrastructure, though, Fargate does this for containers, but it’s worth noting that a serverless product like AWS Lambda takes it even further. For Vogels, this is a continuum and driven by customer demand. While AWS is clearly placing big bets on containers, he is also quite realistic about the fact that many companies will continue to use containers for the foreseeable future. “VMs won’t go away,” he said.

With a serverless product like Lambda, you don’t even think about the infrastructure at all anymore, not even containers — you get to fully focus on the code and only pay for the execution of that code. And while Vogels sees the landscape of VMs, containers and serverless as a continuum, where customers move from one to the next, he also noted that AWS is seeing enterprises that are skipping over the container step and going all in on serverless right away.


Calling All Polyglots: Percona Live 2018 Keynote Schedule Now Available!

Percona Live 2018 Keynotes

Percona Live 2018 KeynotesWe’ve posted the Percona Live 2018 keynote addresses for the seventh annual Percona Live Open Source Database Conference 2018, taking place April 23-25, 2018 at the Santa Clara Convention Center in Santa Clara, CA. 

This year’s keynotes explore topics ranging from how cloud and open source database adoption accelerates business growth, to leading-edge emerging technologies, to the importance of MySQL 8.0, to the growing popularity of PostgreSQL.

We’re excited by the great lineup of speakers, including our friends at Alibaba Cloud, Grafana, Microsoft, Oracle, Upwork and VividCortex, the innovative leaders on the Cool Technologies panel, and Brendan Gregg from Netflix, who will discuss how to get the most out of your database on a Linux OS, using his experiences at Netflix to highlight examples.  

With the theme of “Championing Open Source Databases,” the conference will feature multiple tracks, including MySQL, MongoDB, Cloud, PostgreSQL, Containers and Automation, Monitoring and Ops, and Database Security. Once again, Percona will be offering a low-cost database 101 track for beginning users who want to learn how to use and operate open source databases.

The Percona Live 2018 keynotes include:

Tuesday, April 24, 2018

  • Open Source for the Modern Business – Peter Zaitsev of Percona will discuss how open source database adoption continues to grow in enterprise organizations, the expectations and definitions of what constitutes success continue to change. A single technology for everything is no longer an option; welcome to the polyglot world. The talk will include several compelling open source projects and trends of interest to the open source database community and will be followed by a round of lightning talks taking a closer look at some of those projects.
  • Cool Technologies Showcase – Four industry leaders will introduce key emerging industry developments. Andy Pavlo of Carnegie Mellon University will discuss the requirements for enabling autonomous database optimizations. Nikolay Samokhvalov of will discuss new PostgreSQL tools. Sugu Sougoumarane of PlanetScale Data will explore how Vitess became a high-performance, scalable and available MySQL clustering cloud solution in line with today’s NewSQL storage systems. Shuhao Wu of Shopify explains how to use Ghostferry as a data migration tool for incompatible cloud platforms.
  • State of the Dolphin 8.0 – Tomas Ulin of Oracle will discuss the focus, strategy, investments and innovations that are evolving MySQL to power next-generation web, mobile, cloud and embedded applications – and why MySQL 8.0 is the most significant MySQL release in its history.
  • Linux Performance 2018 – Brendan Gregg of Netflix will summarize recent performance features to help users get the most out of their Linux systems, whether they are databases or application servers. Topics include the KPTI patches for Meltdown, eBPF for performance observability, Kyber for disk I/O scheduling, BBR for TCP congestion control, and more.

Wednesday, April 25, 2018

  • Panel Discussion: Database Evolution in the Cloud – An expert panel of industry leaders, including Lixun Peng of Alibaba, Sunil Kamath of Microsoft, and Baron Schwartz of VividCortex, will discuss the rapid changes occurring with databases deployed in the cloud and what that means for the future of databases, management and monitoring and the role of the DBA and developer.
  • Future Perfect: The New Shape of the Data Tier – Baron Schwartz of VividCortex will discuss the impact of macro trends such as cloud computing, microservices, containerization, and serverless applications. He will explore where these trends are headed, touching on topics such as whether we are about to see basic administrative tasks become more automated, the role of open source and free software, and whether databases as we know them today are headed for extinction.
  • MongoDB at Upwork – Scott Simpson of Upwork, the largest freelancing website for connecting clients and freelancers, will discuss how MongoDB is used at Upwork, how the company chose the database, and how Percona helps make the company successful.

We will also present the Percona Live 2018 Community Awards and Lightning Talks on Monday, April 23, 2018, during the Opening Night Reception. Don’t miss the first day of tutorials and Opening Night Reception!

Register for the conference on the Percona Live Open Source Database Conference 2018 website.


Limited Sponsorship opportunities for Percona Live 2018 Open Source Database Conference are still available, and offer the opportunity to interact with the DBAs, sysadmins, developers, CTOs, CEOs, business managers, technology evangelists, solution vendors, and entrepreneurs who typically attend the event. Contact for sponsorship details.

  • Diamond Sponsors – Percona, VividCortex
  • Platinum – Alibaba Cloud, Microsoft
  • Gold Sponsors – Facebook, Grafana
  • Bronze Sponsors – Altinity, BlazingDB, Box, Dynimize, ObjectRocket, Pingcap, Shannon Systems, SolarWinds, TimescaleDB, TwinDB, Yelp
  • Contributing Sponsors – cPanel, Github, Google Cloud, NaviCat
  • Media Sponsors – Database Trends & Applications, Datanami, EnterpriseTech, HPCWire,, Packt

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