DB (SQL) automated stress/load tools? - sql

I want to measure the performance and scalability of my DB application. I am looking for a tool that would allow me to run many SQL statements against my DB, taking the DB and script (SQL) file as arguments (+necessary details, e.g. host name, port, login...).
Ideally it should let me control parameters such as number of simulated clients, duration of test, randomize variables or select from a list (e.g. SELECT FROM ... WHERE value = #var, where var is read from command line or randomized per execution). I would like to test results to be saved as CSV or XML file that I can analyze and plot them. And of course in terms of pricing I prefer "free" or "demo" :-)
Surprisingly (for me at least) while there are dozens of such tools for web application load testing, I couldn't find any for DB testing!? The ones I did see, such as pgbench, use a built-in DB based on some TPC scenario, so they help test the DBMS configuration and H/W but I cannot test MY DB! Any suggestions?
Specifically I use Postgres 8.3 on Linux, though I could use any DB-generic tool that meets these requirements. The H/W has 32GB of RAM while the size of the main tables and indexes is ~120GB. Hence there can be a 1:10 response time ratio between cold vs warm cache runs (I/O vs RAM). Realistically I expect requests to be spread evenly, so it's important for me to test queries against different pieces of the DB.
Feel free to also contact me via email.
Thanks!
-- Shaul Dar (info#shauldar.com)

JMeter from Apache can handle different server types. I use it for load tests against web applications, others in the team use it for DB calls. It can be configured in many ways to get the load you need. It can be run in console mode and even be clustered using different clients to minimize client overhead ( and so falsifying the results).
It's a java application and a bit complex at first sight. But still we love it. :-)

k6.io can stress test a few relational databases with the xk6-sql extension.
For reference, a test script could be something like:
import sql from 'k6/x/sql';
const db = sql.open("sqlite3", "./test.db");
export function setup() {
db.exec(`CREATE TABLE IF NOT EXISTS keyvalues (
id integer PRIMARY KEY AUTOINCREMENT,
key varchar NOT NULL,
value varchar);`);
}
export function teardown() {
db.close();
}
export default function () {
db.exec("INSERT INTO keyvalues (key, value) VALUES('plugin-name', 'k6-plugin-sql');");
let results = sql.query(db, "SELECT * FROM keyvalues;");
for (const row of results) {
console.log(`key: ${row.key}, value: ${row.value}`);
}
}
Read more on this short tutorial.

The SQL Load Generator is another such tool:
http://sqlloadgenerator.codeplex.com/
I like it, but it doesn't yet have the option to save test setup.

We never really found an adequate solution for stress testing our mainframe DB2 database so we ended up rolling our own. It actually just consists of a bank of 30 PCs running Linux with DB2 Connect installed.
29 of the boxes run a script which simply wait for a starter file to appear on an NFS mount then start executing fixed queries based on the data. The fact that these queries (and the data in the database) are fixed means we can easily compare against previous successful runs.
The 30th box runs two scripts in succession (the second is the same as all the other boxes). The first empties then populates the database tables with our known data and then creates the starter file to allow all the other machines (and itself) to continue.
This is all done with bash and DB2 Connect so is fairly easily maintainable (and free).
We also have another variant to do random queries based on analysis of production information collected over many months. It's harder to check the output against a known successful baseline but, in that circumstance, we're only looking for functional and performance problems (so we check for errors and queries that take too long).
We're currently examining whether we can consolidate all those physical servers into virtual machines, on both the mainframe running zLinux (which will use the shared-memory HyperSockets for TCP/IP, basically removing the network delays) and Intel platforms with VMWare, to free up some of that hardware.
It's an option you should examine if you don't mind a little bit of work up front since it gives you a great deal of control down the track.

Did you check Bristlecone an open source tool from Continuent? I don't use it, but it works for Postgres and seems to be able to do the things that your request. (sorry as a new user, I cannot give you the direct link to the tool page, but Google will get you there ;o])

Related

Schema migration in Redis

I have an application using redis. I used a key name user:<id> to store user info. Then locally I changed my app code to use key name user:<id>:data for that purpose.
I am scared by the fact that if I git push this new code to my production server things will break. And the reason for this is that since my production redis server would already have the keys will older key names.
So the only way I think is to stop my app, change all the older key names to new ones & then restart it. Do you have a better alternative? Thanks for help :)
Pushing new code to your production environment is always a scary business (that's why only the toughest survive in this profession ;)). I strongly recommend that before you change your production code and database, make sure that you test the workflow and its results locally.
Almost any update to the application requires its stoppage - even if only to replace the relevant files. This is even truer for any changes that involve a database exactly because of the reason you had mentioned.
Even if you can deploy your code changes without stopping the application per se (e.g. a PHP page), you will still want the database change to be done "atomically" - i.e. without any application requests intervening and possibly breaking. While some database can be taken offline for maintenance, even then you usually stop the app or else errors will be generated all over the place.
If that is indeed the case, you'll be stopping the app (or putting it into maintenance mode) regardless of the database change, so we take your question to actually mean: what's the fastest way to rename all/some keys in my database?
To answer that question, similarly to the pseudo-code suggested above, I suggest you use a Lua script such as the following and EVAL it once you stop the app:
for _,k in ipairs(redis.call('keys', 'user:*')) do
if k:sub(-5) ~= ':data' then
redis.call('rename', k, k .. ':data')
end
end
A few notes about this script that you should keep in mind:
Although the KEYS command is not safe to use in production, since you are doing maintenance it can be used safely. For all other use cases where you need to scan you keys, Redis' SCAN is much more advisable.
Since Lua scripts are "atomic", you can in theory run this script without stopping the app - as long as the script runs (which depends on the size of your dataset) the app's requests will be blocked. Put differently, this approach solves the concern of getting mixed key names (old & new). This, however, is probably not what you'd want to do in any case because a) your app may still error/timeout during that time but mainly because b) it will need to be able to handle both types of key names (i.e. running with old keys -> short/long pause -> running with new keys) making your code much more complex.
The if condition is not required if you're going to run the script only once and succeed.
Depending on the actual contents of your database, you may want to further filter out keys that should not be renamed.
To ensure compatibility, refrain from hardcoding / computationally generating key names - instead, they should be passed as arguments to the script.
You can run a migration script in your redis client language, using RENAME.
If you don't have any other control over the total of keys, you first issue a KEYS user:* to list all keys, then substring for getting the numeric id, then renaming.
You can issue all of this in a transaction.
So a little pseudocode and redis commands:
MULTI
KEYS user:*
For each key {
id = <Get id from key>
RENAME user:id user:id:data
}
EXEC
Got it ?

Extracting Data from a VERY old unix machine

Firstly apologies if this question seems like a wall of text, I can't think of a way to format it.
I have a machine with valuable data on(circa 1995), the machine is running unix (SCO OpenServer 6) with some sort of database stored on it.
The data is normally accessed via a software package of which the license has expired and the developers are no longer trading.
The software package connects to the machine via telnet to retrieve data and modify data (the telnet connection no longer functions due to the license being changed).
I can access the machine via an ODBC driver (SeaODBC.dll) over a network, this was how I was planning to extract the data but so far I have retrieved 300,000 rows in just over 24 hours, in total I estimate there will be around 50,000,000 rows total so at current speed it will take 6 months!
I need either a quicker way to extract the data from the machine via ODBC or a way to extract the entire DB locally on the machine to an external drive/network drive or other external source.
I've played around with the unix interface and the only large files I can find are in a massive matrix of single character folder (eg A\G\data.dat, A\H\Data.dat ect).
Does anyone know how to find out the installed DB systems on the machine? Hopefully it is a standard and I'll be able to find a way to export everything into a nicely formatted file.
Edit
Digging around the file system I have found a folder under root > L which contains lots of single lettered folders, each single lettered folder contains more single letter folders.
There are also files which are named after the table I need (eg "ooi.r") which have the following format:
<Id>
[]
l for ooi_lno, lc for ooi_lcno, s for ooi_invno, id for ooi_indate
require l="AB"
require ls="SO"
require id=25/04/1998
{<id>} is s
sort increasing Id
I do not recognize those kinds of filenames A\G\data.dat and so on (filenames with backslashes in them???) and it's likely to be a proprietary format so I wouldn't expect much from that avenue. You can try running file on these to see if they are in any recognized format just to see...
I would suggest improving the speed of data extraction over ODBC by virtualizing the system. A modern computer will have faster memory, faster disks, and a faster CPU and may be able to extract the data a lot more quickly. You will have to extract a disk image from the old system in order to virtualize it, but hopefully a single sequential pass at reading everything off its disk won't be too slow.
I don't know what the architecture of this system is, but I guess it is x86, which means it might be not too hard to virtualize (depending on how well the SCO OpenServer 6 OS agrees with the virtualization). You will have to use a hypervisor that supports full virtualization (not paravirtualization).
I finally solved the problem, running a query using another tool (not through MS Access or MS Excel) worked massively faster, ended up using DaFT (Database Fishing Tool) to SELECT INTO a text file. Processed all 50 million rows in a few hours.
It seems the dll driver I was using doesn't work well with any MS products.

How can I speed up batch processing job in Coldfusion?

Every once in awhile I am fed a large data file that my client uploads and that needs to be processed through CMFL. The problem is that if I put the processing on a CF page, then it runs into a timeout issue after 120 seconds. I was able to move the processing code to a CFC where it seems to not have the timeout issue. However, sometime during the processing, it causes ColdFusion to crash and has to restarted. There are a number of database queries (5 or more, mixture of updates and selects) required for each line (8,000+) of the file I go through as well as other logic provided by me in the form of CFML.
My question is what would be the best way to go through this file. One caveat, I am not able to move the file to the database server and process it entirely with the DB. However, would it be more efficient to pass each line to a stored procedure that took care of everything? It would still be a lot of calls to the database, but nothing compared to what I have now. Also, what would be the best way to provide feedback to the user about how much of the file has been processed?
Edit:
I'm running CF 6.1
I just did a similar thing and use CF often for data parsing.
1) Maintain a file upload table (Parent table). For every file you upload you should be able to keep a list of each file and what status it is in (uploaded, processed, unprocessed)
2) Temp table to store all the rows of the data file. (child table) Import the entire data file into a temporary table. Attempting to do it all in memory will inevitably lead to some errors. Each row in this table will link to a file upload table entry above.
3) Maintain a processing status - For each row of the datafile you bring in, set a "process/unprocessed" tag. This way if it breaks, you can start from where you left off. As you run through each line, set it to be "processed".
4) Transaction - use cftransaction if possible to commit all of it at once, or at least one line at a time (with your 5 queries). That way if something goes boom, you don't have one row of data that is half computed/processed/updated/tested.
5) Once you're done processing, set the file name entry in the table in step 1 to be "processed"
By using the approach above, if something fails, you can set it to start where it left off, or at least have a clearer path of where to start investigating, or worst case clean up in your data. You will have a clear way of displaying to the user the status of the current upload processing, where it's at, and where it left off if there was an error.
If you have any questions, let me know.
Other thoughts:
You can increase timeouts, give the VM more memory, put it in 64 bit but all of those will only increase the capacity of your system so much. It's a good idea to do these per call and do it in conjunction with the above.
Java has some neat file processing libraries that are available as CFCS. if you run into a lot of issues with speed, you can use one of those to read it into a variable and then into the database
If you are playing with XML, do not use coldfusion's xml parsing. It works well for smaller files and has fits when things get bigger. There are several cfc's written out there (check riaforge, etc) that wrap some excellent java libraries for parsing xml data. You can then create a cfquery manually if need be with this data.
It's hard to tell without more info, but from what you have said I shoot out three ideas.
The first thing, is with so many database operations, it's possible that you are generating too much debugging. Make sure that under Debug Output settings in the administrator that the following settings are turned off.
Enable Robust Exception Information
Enable AJAX Debug Log Window
Request Debugging Output
The second thing I would do is look at those DB queries and make sure they are optimized. Make sure selects are happening with indicies, etc.
The third thing I would suspect is that the file hanging out in memory is probably suboptimal.
I would try looping through the file using file looping:
<cfloop file="#VARIABLES.filePath#" index="VARIABLES.line">
<!--- Code to go here --->
</cfloop>
Have you tried an event gateway? I believe those threads are not subject to the same timeout settings as page request threads.
SQL Server Integration Services (SSIS) is the recommended tool for complex ETL (Extract, Transform, and Load) work, which is what this sounds like. (It can be configured to access files on other servers.) The question might be, can you work up an interface between Cold Fusion and SSIS?
If you can upgrade to cf8 and take advantage of cfloop file="" which would give you greater speed and the file would not be put in memory (which is probably the cause of the crashing).
Depending on the situation you are encountering you could also use cfthread to speed up processing.
Currently, an event gateway is the only way to get around the timeout limits of an HTTP request cycle. CF does not have a way to process CF pages offline, that is, there is no command-line invocation (one of my biggest gripes about CF - very little offling processing).
Your best bet is to use an Event Gateway or rewrite your parsing logic in straight Java.
I had to do the same thing, Ben Nadel has written a bunch of great articles uses java file io, to allow you to more speedily read files, write files etc...
Really helped improve the performance of our csv importing application.

Automating WebTrends analysis

Every week I access server logs processed by WebTrends (for about 7 profiles) and copy ad clickthrough and visitor information into Excel spreadsheets. A lot of it is just accessing certain sections and finding the right title and then copying the unique visitor information.
I tried using WebTrends' built-in query tool but that is really poorly done (only uses a drag-and-drop system instead of text-based) and it has a maximum number of parameters and maximum length of queries to query with. As far as I know, the tools in WebTrends are not suitable to my purpose of automating the entire web metrics gathering process.
I've gotten access to the raw server logs, but it seems redundant to parse that given that they are already being processed by WebTrends.
To me it seems very scriptable, but how would I go about doing that? Is screen-scraping an option?
I use ODBC for querying metrics and numbers out of webtrends. We even fill a scorecard with all key performance metrics..
Its in German, but maybe the idea helps you: http://www.web-scorecard.net/
Michael
Which version of WebTrends are you using? Unless this is a very old install, there should be options to schedule these reports to be emailed to you, and also to bookmark queries. Let me know which version it is and I can make some recommendations.

Do you put your database static data into source-control ? How?

I'm using SQL-Server 2008 with Visual Studio Database Edition.
With this setup, keeping your schema in sync is very easy. Basically, there's a 'compare schema' tool that allow me to sync the schema of two databases and/or a database schema with a source-controlled creation script folder.
However, the situation is less clear when it comes to data, which can be of three different kind :
static data referenced in the code. typical example : my users can change their setting, and their configuration is stored on the server. However, there's a system-wide default value for each setting that is used in case the user didn't override it. The table containing those default settings grows as more options are added to the program. This means that when a new feature/option is checked in, the system-wide default setting is usually created in the database as well.
static data. eg. a product list populating a dropdown list. The program doesn't rely on the existence of a specific product in the list to work. This can be for example a list of unicode-encoded products that should be deployed in production when the new "unicode version" of the program is deployed.
other data, ie everything else (logs, user accounts, user data, etc.)
It seems obvious to me that my third item shouldn't be source-controlled (of course, it should be backuped on a regular basis)
But regarding the static data, I'm wondering what to do.
Should I append the insert scripts to the creation scripts? or maybe use separate scripts?
How do I (as a developer) warn the people doing the deployment that they should execute an insert statement ?
Should I differentiate my two kind of data? (the first one being usually created by a dev, while the second one is usually created by a non-dev)
How do you manage your DB static data ?
I have explained the technique I used in my blog Version Control and Your Database. I use database metadata (in this case SQL Server extended properties) to store the deployed application version. I only have scripts that upgrade from version to version. At startup the application reads the deployed version from the database metadata (lack of metadata is interpreted as version 0, ie. nothing is yet deployed). For each version there is an application function that upgrades to the next version. Usually this function runs an internal resource T-SQL script that does the upgrade, but it can be something else, like deploying a CLR assembly in the database.
There is no script to deploy the 'current' database schema. New installments iterate trough all intermediate versions, from version 1 to current version.
There are several advantages I enjoy by this technique:
Is easy for me to test a new version. I have a backup of the previous version, I apply the upgrade script, then I can revert to the previous version, change the script, try again, until I'm happy with the result.
My application can be deployed on top of any previous version. Various clients have various deployed version. When they upgrade, my application supports upgrade from any previous version.
There is no difference between a fresh install and an upgrade, it runs the same code, so I have fewer code paths to maintain and test.
There is no difference between DML and DDL changes (your original question). they all treated the same way, as script run to change from one version to next. When I need to make a change like you describe (change a default), I actually increase the schema version even if no other DDL change occurs. So at version 5.1 the default was 'foo', in 5.2 the default is 'bar' and that is the only difference between the two versions, and the 'upgrade' step is simply an UPDATE statement (followed of course by the version metadata change, ie. sp_updateextendedproperty).
All changes are in source control, part of the application sources (T-SQL scripts mostly).
I can easily get to any previous schema version, eg. to repro a customer complaint, simply by running the upgrade sequence and stopping at the version I'm interested in.
This approach saved my skin a number of times and I'm a true believer now. There is only one disadvantage: there is no obvious place to look in source to find 'what is the current form of procedure foo?'. Because the latest version of foo might have been upgraded 2 or 3 versions ago and it wasn't changed since, I need to look at the upgrade script for that version. I usually resort to just looking into the database and see what's in there, rather than searching through the upgrade scripts.
One final note: this is actually not my invention. This is modeled exactly after how SQL Server itself upgrades the database metadata (mssqlsystemresource).
If you are changing the static data (adding a new item to the table that is used to generate a drop-down list) then the insert should be in source control and deployed with the rest of the code. This is especially true if the insert is needed for the rest of the code to work. Otherwise, this step may be forgotten when the code is deployed and not so nice things happen.
If static data comes from another source (such as an import of the current airport codes in the US), then you may simply need to run an already documented import process. The import process itself should be in source control (we do this with all our SSIS packages), but the data need not be.
Here at Red Gate we recently added a feature to SQL Data Compare allowing static data to be stored as DML (one .sql file for each table) alongside the schema DDL that is currently supported by SQL Compare.
To understand how this works, here is a diagram that explains how it works.
The idea is that when you want to push changes to your target server, you do a comparison using the scripts as the source data source, which generates the necessary DML synchronization script to update the target. This means you don't have to assume that the target is being recreated from scratch each time. In time we hope to support static data in our upcoming SQL Source Control tool.
David Atkinson, Product Manager, Red Gate Software
I have come across this when developing CMS systems.
I went with appending the static data (the stuff referenced in the code) to the database creation scripts, then a separate script to add in any 'initialisation data' (like countries, initial product population etc).
For the first two steps, you could consider using an intermediate format (ie XML) for the data, then using a home grown tool, or something like CodeSmith to generate the SQL, and possible source files as well, if (for example) you have lookup tables which relate to enumerations used in the code - this helps enforce consistency.
This has another benefit that if the schema changes, in many cases you don't have to regenerate all your INSERT statements - you just change the tool.
I really like your distinction of the three types of data.
I agree for the third.
In our application, we try to avoid putting in the database the first, because it is duplicated (as it has to be in the code, the database is a duplicate). A secondary benefice is that we need no join or query to get access to that value from the code, so this speed things up.
If there is additional information that we would like to have in the database, for example if it can be changed per customer site, we separate the two. Other tables can still reference that data (either by index ex: 0, 1, 2, 3 or by code ex: EMPTY, SIMPLE, DOUBLE, ALL).
For the second, the scripts should be in source-control. We separate them from the structure (I think they typically are replaced as time goes, while the structures keeps adding deltas).
How do I (as a developer) warn the people doing the deployment that they should execute an insert statement ?
We have a complete procedure for that, and a readme coming with each release, with scripts and so on...
First off, I have never used Visual Studio Database Edition. You are blessed (or cursed) with whatever tools this utility gives you. Hopefully that includes a lot of flexibility.
I don't know that I'd make that big a difference between your type 1 and type 2 static data. Both are sets of data that are defined once and then never updated, barring subsequent releases and updates, right? In which case the main difference is in how or why the data is as it is, and not so much in how it is stored or initialized. (Unless the data is environment-specific, as in "A" for development, "B" for Production. This would be "type 4" data, and I shall cheerfully ignore it in this post, because I've solved it useing SQLCMD variables and they give me a headache.)
First, I would make a script to create all the tables in the database--preferably only one script, otherwise you can have a LOT of scripts lying about (and find-and-replace when renaming columns becomes very awkward). Then, I would make a script to populate the static data in these tables. This script could be appended to the end of the table script, or made it's own script, or even made one script per table, a good idea if you have hundreds or thousands of rows to load. (Some folks make a csv file and then issue a BULK INSERT on it, but I'd avoid that is it just gives you two files and a complex process [configuring drive mappings on deployment] to manage.)
The key thing to remember is that data (as stored in databases) can and will change over time. Rarely (if ever!) will you have the luxury of deleting your Production database and replacing it with a fresh, shiny, new one devoid of all that crufty data from the past umpteen years. Databases are all about changes over time, and that's where scripts come into their own. You start with the scripts to create the database, and then over time you add scripts that modify the database as changes come along -- and this applies to your static data (of any type) as well.
(Ultimately, my methodology is analogous to accounting: you have accounts, and as changes come in you adjust the accounts with journal entries. If you find you made a mistake, you never go back and modify your entries, you just make a subsequent entries to reverse and fix them. It's only an analogy, but the logic is sound.)
The solution I use is to have create and change scripts in source control, coupled with version information stored in the database.
Then, I have an install wizard that can detect whether it needs to create or update the db - the update process is managed by picking appropriate scripts based on the stored version information in the database.
See this thread's answer. Static data from your first two points should be in source control, IMHO.
Edit: *new
all-in-one or a separate script? it does not really matter as long as you (dev team) agree with your deployment team. I prefer to separate files, but I still can always create all-in-one.sql from those in the proper order [Logins, Roles, Users; Tables; Views; Stored Procedures; UDFs; Static Data; (Audit Tables, Audit Triggers)]
how do you make sure they execute it: well, make it another step in your application/database deployment documentation. If you roll out application which really needs specific (new) static data in the database, then you might want to perform a DB version check in your application. and you update the DB_VERSION to your new release number as part of that script. Then your application on a start-up should check it and report an error if the new DB version is required.
dev and non-dev static data: I have never seen this case actually. More often there is real static data, which you might call "dev", which is major configuration, ISO static data etc. The other type is default lookup data, which is there for users to start with, but they might add more. The mechanism to INSERT these data might be different, because you need to ensure you do not destoy (power-)user-created data.