Domino server and application performance in the real worldPresentation Transcript
Andrew Pollack, NCT
English is the only language I speak
-- Unless you count programming languages
I will try to speak clearly, but if I am moving too quickly, or too slowly, please make some kind of sign, so I can adjust!
We will all point at you
Set all noise making toys to “Stun” please
If you need to type on a laptop or a Blackberry – move toward the back please
Administrator & Developer since version 2.0
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Site Performance Reviews
Security Review & Penetration Testing
IBM Lotus Beacon Award Winner
Lieutenant of Cumberland, Maine – Engine 1
In firefighting, just like Server Administration it's all in the planning
Performance with a Big Picture approach
Defining Performance In User Terms
Key Performance Choke Points
Common General Tweaks
Application Specific Strategies
Finding Your Own Choke Points
A face lift may make you look better for a while, but it won’t cure cancer
No Single INI Variable -- #1 Server Fix
Focus On The Basics!
No Super Storage Network
No Ultimate Network Switch
Third Party Application
No God-like Consultant
Not Even Me!
Performance Problems Are like snowflakes
Individually, they don’t matter much at all
You notice them only once they stack up
Poorly Performing Disk I/O
+ Agents Changing Many Documents
+ Many Views to Update
== Very Slow System
These kinds of problems create a feedback loop, which amplifies the problems
It’s not how you feel, its how you look. Darling, you look marvelous! -- Billy Crystal
If the user must wait for something, it will always seem slow – no matter how fast you make it.
Nothing is worse than an hourglass cursor and a bar slowly moving across the screen
Except NOT having the bar
Move anything not immediately required by the user to a background process
Cache Commonly Referenced Data
Don’t pop-up modal dialog boxes
* More on this when we talk about application design in a few minutes!
We’re going the wrong way, but we’re making excellent time!
Bandwidth vs. Latency
How big around is the pipe?
How long is the pipe from end to end?
Even light takes several minutes to reach us from the Sun. That’s latency
Latency impacts “Chatty” connections – Notes can be chatty
Ping times larger than 100ms are “high” latency.
WAN links, Satellite links, Modems, and VPN’s are all prone to latency issues
Multi-Hop connections across buffered routers and firewalls can introduce latency
Encryption software can introduce latency
Avoid opening and closing many documents
Avoid DB Lookups by caching common values
Example: Use a db open script to write common lookup values to a local environment variable each time the user opens the database
Use “RunOnServer” to move complex agent work to the server, the read the result from a profile document
This is the #1, #2, and #3 Root performance problem on Domino Server
Nearly any other performance problem is made many times worse if the Disk I/O is overwhelmed
Most Domino Servers are not well optimized for Disk I/O
One “Data” drive is used for too much
databases, index rebuilds, temporary files, swap files, and even transaction logging
Transaction Logging used in conjunction with journaling file systems
Poor choice of RAID configurations
Too heavy a reliance on Storage Area Networks
Many of the following recommendations balance performance with safety
You need to assess each as it relates to your overall data strategy
ALWAYS put your transaction logging files on a separate drive
Move your view indexing temporary files to another drive (INI Parameter)
Consider moving disk-intensive applications to their own drive
If you must have memory swapping, give it its own drive
Active Log Files
Things that load once and are not re-accessed frequently do not need to be on high performance resources
The Operating System
Application Program Files
Archive Log Files
One Disk may have multiple partitions
Different partitions are NOT different spindles
All the partitions on the same drive, share the same read-write head and are impacted by data access as a single entity.
Multiple drives in a RAID array don’t count
A RAID array is treated by the system as a single drive. By definition, data is written across the whole array
The “Best-Case” is multiple drives on different drive controllers
Most RAID arrays are configured to improve redundancy, not necessarily speed.
Not all data requires redundancy
Loss of some data is very low risk
Memory Swap Files
Indexing scratch space
Inexpensive SATA drives can be used for a real performance gain
Solid State Drives – Possible future use for very high speed, relatively small footprint data, like transaction logging
Consider the benefits of a SAN
Highly redundant storage
Single backup point
Consolidated free space
I have yet to see a SAN that truly outperforms local high speed disks
Not all Domino Data needs these features
Transaction Logs – Consider local RAID if possible
Indexing Scratch Space – Use Cheap, Local, Fast Drives
If you’re already clustering Domino, only one of the clustered machines may need to be on the SAN
Windows NTFS – And you can’t turn it off!
Linux ext3 file system
IBM AIX, SUN Solaris, and Apple OSX all make use of Journaling File Systems
Not all the same – but generally speaking, disk WRITES are doubled
Don’t put Transaction Logs on Journaling File Systems – its redundant, and redundant again.
Data is written twice (at least)
Formatted with a JFS
Data is written twice
Using Transaction Logging
Data is written twice
2 x 2 x 2 = 8 Times the Data Writes
Now think about that on a pair of clustered servers
These should be obvious
More RAM is better – Up to what is supported
Depending on the OS, you may need to partition your server to take full advantage
Drive Cache – If your OS lets you manage it, you should work to really optimize this
Most Anti-Virus Software is EVIL when it runs against Domino Databases
Make sure your AV is Domino aware!
Developers really LOVE when administrators give them feedback like this
Why are you using “NoCache”?
Cache times are very small, does your data really change on a second by second basis?
Can be very chatty – a killer on high latency networks, but not as bad for web apps
Requires more views to be up to date – big performance hit in databases that change a lot
Many lookups on the same form, to the same place for different values?
Use it once to get the UNID, then use @GetDocValue
Use a profile document, or local environment variables updated in the dbopen script to store commonly looked up data
For application performance tuning, views are the first, second, and third place to look
View indexing is very disk intensive – and can amplify disk I/O shortcommings
To update a view, a full database scan often needs to happen. That can be very very slow on large databases
Any view performance problem grows exponentially with the volume of data
These problems are often not caught in test
If your view column (bad) or selection formula (worse) uses @Now, @Today, etc.. You’re hurting performance
Time dependant views are “Always” considered out of date and must be re-indexed for every use
If you’ve got one, you’ve got more. Developers that do this tend to repeat the pattern
Use a FOLDER instead
Run a agent to select the right documents for the folder on a periodic basis – Daily for “@Today” or Hourly for @Hour(@Now), etc.
This will still cause an update, but only once each time the update happens
Categorize documents based on a stored date value, then use a “show single category” option on the view
If you MUST use a time specific view, set its update frequency to the absolute least frequent you can
It will still update for each user access, however, unlike a folder which is static
Consider a column formula with 10 steps
Now consider 100,000 Documents in it
That column must execute 1 Million steps for each view index rebuild – just in that column
Many column formulas are much more complex, and serve many times that many documents
Create Hidden Fields on the Document
At “Save” time, compute the value that would be on the view column in the hidden fields
Display the value of the hidden field as the view column formula.
What was a complex formula executing hundreds of thousands of times is now a single field value
Consider a database with 100,000 documents
Consider that database having 10 views
Consider each view having 5 columns
Each time data in the database is updated, every selection formula has to be checked to see if the view is impacted
Every view has to be updated by the indexer
Embed The View on a Form or Frameset
Categorize the view in the same way you woul distinguish the different views
Use Show Single Category to differentiate the data to the user
Compute text values on the form to result in very different data in each category if needed
Use multi-column hidden views so that he same view can serve multiple lookup needs
Make sure your developers coordinate so that duplicate lookup views are not created
Calculate and Store a HASH value
A HASH is a short, nearly unique, value crated by applying a mathematical formula to a set of data. For example, you can hash an entire paragraph and get a short string as a hash value. The same source will always produce the same hash, but any change to the source will produce a different hash.
You can tell if a document has changed, simply by comparing the HASH value
Andrew’s Magic Hash story
If you fall asleep, please don’t drool on the table
C’mon, it’s a true story!
Read View Entries – Not Documents
Turn off view updating while working with documents in the agent
Turn off MIME conversion when working with mail documents
Run agents periodically, not “Before New Mail Arrives” – that slows down the router
Placing blame, for fun and profit!
Look for Disk Performance First
Start Simple: Are the drive lights sitting on for long periods of time?
Use the operating system’s tools
Performance monitor in Windows, “top” in Linux, etc.
Processes like “logasio” which is for transaction logging will show up
Check for network latency and bandwidth
Start Simple: Use Ping to check latency
Use Domino’s DDM and Statistics tools
See Gabriella Davis’s current and past sessions on these tools
Admit it – you really came here looking for cool INI settings like DominoRunFaster=11
Here’s some that I use
For busy mail servers, can speed up delivery
Move full text indexing to its own thread, distinct from the indexer – This is the costs to “runfaster” I have found
Use memory outside the Domino server
According to Kerr, this is a must have for busy web servers
Use These Together:
Tells the server to use the old index while the new one catches up
Fine tunes when the directory indexes get refreshed
Very powerful, but very complex
Check the Lotus Notes Knowledge base
Starts at around 300
Not as critical as it used to be
Check your success with this console command
show stat database.database.b*
Don’t check too soon after a change, its only valid over time