My current system, I'll call it B, gets data from an old legacy SQL database, A, via replication. I do not have any control over this legacy system other than access to the database. Replication was initially chosen to keep the two systems separate as well as to reduce any chances of performance hits on the legacy system, A, from my system, B. In B, there is a task that runs every 15 minutes that loads data from the replicated source tables and transforms it into the format I need and saves it to a SQL database that I own. This task is currently very slow as it loads up all possible data and checks for changes before it decides if any updates need to be made.
I'm creating a new system, C, that also needs to use the data from A as well as interact with B. The data is needed in a completely different format for C, so I am not able to reuse much of B, so speeding up B is not an option. Ideally, I'd like to switch B over to whatever solution chosen for C, but in the meantime, whatever is picked needs to play well with replication.
I am researching new options to get data from A as well as some way to get change notifications. Ideally, I'd love for A to send messages when there are changes but this is not possible due to the fragility of A.
I've looked into SQL Query Notifications, specifically SQLDependency and SQLTableDependency. I need to be able to see what data has changed, so SQLTableDependency might be better but its only listening when the application is running so nothing is listening when it stops. I'd like to be able to cache data instead of stitching it into the format I need when I load my website.
I've also looked into Change Tracking and Change Data Capture. Both seem like they could work in my current set up but they both seem heavier than what I need. I am also concerned about both of these running with replication. For example, if replication is re-initializing, all data is truncated and it looks like I have a ton of changes?
Am I going in the right direction given the constraints of my system? Does anyone have any other ideas of some way to get data changes? Is there some other messaging system that can be used with SQL?
Thanks!
Related
We have data stored in a data warehouse as follows:
Price
Date
Product Name (varchar(25))
We currently only have four products. That changes very infrequently (on average once every 10 years). Once every business day, four new data points are added representing the day's price for each product.
On the website, a user can request this information by entering a date range and selecting one or more products names. Analytics shows that the feature is not heavily used (about 10 users requests per week).
It was suggested that the data warehouse should daily push (SFTP) a CSV file containing all data (currently 6718 rows of this data and growing by four each day) to the web server. Then, the web server would read data from the file and display that data whenever a user made a request.
Usually, the push would only be once a day, but more than one push could be possible to communicate (infrequent) price corrections. Even in the price correction scenario, all data would be delivered in the file. What are problems with this approach?
Would it be better to have the web server make a request to the data warehouse per user request? Or does this have issues such as a greater chance for network errors or performance issues?
Would it be better to have the web server make a request to the data warehouse per user request?
Yes it would. You have very little data, so there is no need to try and 'cache' this in some way. (Apart from the fact that CSV might not be the best way to do this).
There is nothing stopping you from doing these requests from the webserver to the database server. With as little information as this you will not find performance an issue, but even if it would be when everything grows, there is a lot to be gained on the database-side (indexes etc) that will help you survive the next 100 years in this fashion.
The amount of requests from your users (also extremely small) does not need any special treatment, so again, direct query would be the best.
Or does this have issues such as a greater chance for network errors or performance issues?
Well, it might, but that would not justify your CSV method. Examples and why you need not worry, could be
the connection with the databaseserver is down.
This is an issue for both methods, but with only one connection per day the change of a 1-in-10000 failures might seem to be better for once-a-day methods. But these issues should not come up very often, and if they do, you should be able to handle them. (retry request, give a message to user). This is what enourmous amounts of websites do, so trust me if I say that this will not be an issue. Also, think of what it would mean if your daily update failed? That would present a bigger problem!
Performance issues
as said, this is due to the amount of data and requests, not a problem. And even if it becomes one, this is a problem you should be able to catch at a different level. Use a caching system (non CSV) on the database server. Use a caching system on the webserver. Fix your indexes to stop performance from being a problem.
BUT:
It is far from strange to want your data-warehouse separated from your web system. If this is a requirement, and it surely could be, the best thing you can do is re-create your warehouse-database (the one I just defended as being good enough to query directly) on another machine. You might get good results by doing a master-slave system
your datawarehouse is a master-database: it sends all changes to the slave but is inexcessible otherwise
your 2nd database (on your webserver even) gets all updates from the master, and is read-only. you can only query it for data
your webserver cannot connect to the datawarehouse, but can connect to your slave to read information. Even if there was an injection hack, it doesn't matter, as it is read-only.
Now you don't have a single moment where you update the queried database (the master-slave replication will keep it updated always), but no chance that the queries from the webserver put your warehouse in danger. profit!
I don't really see how SQL injection could be a real concern. I assume you have some calendar type field that the user fills in to get data out. If this is the only form just ensure that the only field that is in it is a date then something like DROP TABLE isn't possible. As for getting access to the database, that is another issue. However, a separate file with just the connection function should do fine in most cases so that a user can't, say open your webpage in an HTML viewer and see your database connection string.
As for the CSV, I would have to say querying a database per user, especially if it's only used ~10 times weekly would be much more efficient than the CSV. I just equate the CSV as overkill because again you only have ~10 users attempting to get some information, to export an updated CSV every day would be too much for such little pay off.
EDIT:
Also if an attack is a big concern, which that really depends on the nature of the business, the data being stored, and the visitors you receive, you could always create a backup as another option. I don't really see a reason for this as your question is currently stated, but it is a possibility that even with the best security an attack could happen. That mainly just depends on if the attackers want the information you have.
I am new to databases and I'm trying to decide which one would suit my needs the most. I am in the planning stages of a program that will store directory references to thousands of PDF files at multiple locations (all under one domain). Basically, all it will be is date, location, originator's name and link to the file in each of the fields within the database. Nothing more (no files, nothing fancy). All I'll need to do with the database is sort fields by location, date, name of the originator -- that's it. There will be instances where multiple writes would need to occur at the same time. I've read up on SQLite website that only one write is supported at any given time. Does it mean writes to a specific file or database period?
What I mean is that multiple records would need to be added from different clients at any given time, but the existing records would not need to be modified (and if they do, it would be done from a specific client). To give a little bit more detail, what I'll have is several locations at which service application will be running in the background and listening to folders. Once a file enters a folder, it gets renamed under a specific format and added to the database. It is very likely that two folder listening apps would try to add files to the database at the same time.
Would I be able to accomplish this with SQLite or is it one write at any given time to the entire database? If only one write is possible, period, to the entire database, is there a way to implement some sort of spooling system (sort of like on a printer), where writes would wait in a queue with life timers on them?
If it's not possible, then I will look at MySQL. Cost is of a concern, so I'm steering towards these two.
Only one write can occur at the exact time, but by default writes are automatically queued and you can achieve thousands of writes per second.
The main concern is what type of application is this? Is it a web application developed on one machine and deployed to a single other production machine? Then the extra trouble of installing and maintaining MySQL is not a concern and you're better off using MySQL. If this is a desktop application installed on many desktops, then using an embedded database is far easier for development, installation, and maintenance and in that case use SQLite.
I have a NoSQL database that we are using for data processing, as it can be used for my application faster than SQL can. I'm treating our NoSQL database almost like a cache of information, with the SQL being the authority of data, and the NoSQL store being updated with changes. Right now this is being done through our application, so when a request comes in for a change, it is made in the SQL database, and the NoSQL database. This is failing at times as sometimes the NoSQL update fails, or other situations cause the NoSQL database to get out of sync.
I could do a batch update every X minutes, however it is a lot of information in the data stores, and it would take hours to ensure that they are in sync. We have some timestamps to do a difference of what has been changed, but this is not always accurate.
I'm wondering what some recommended strategy for keeping a data store(secondary database cache) in sync with my main store are?
I know I've done with this with messaging in the past - specifically JMS with ActiveMQ. I would send the updates to a NoSQL store (Mongo) by using a queue. This way messages could accumulate in the queue and if the connection to the NoSQL store ever got severed, it could pick up where it left off.
It worked really well because ActiveMQ was really stable and simple to work with.
I've always seen this done with diffs like you mentioned. You introduce date fields all over and then keep track of the latest sync. The nice thing about this approach is that it easily allows you to replay transactions by modifying the last sync date.
One last piece of advice ... write good tools around pumping data from point A to point B (in this case SQL to NoSQL). I wrote several tools to bulk load the NoSQL store from SQL at my last job and it made life easy if anything got really out of sync. Between scripts and bulk loading processes, I could always recover.
I'm considering MongoDB right now. Just so the goal is clear here is what needs to happen:
In my app, Finch (finchformac.com for details) I have thousands and thousands of entries per day for each user of what window they had open, the time they opened it, the time they closed it, and a tag if they choose one for it. I need this data to be backed up online so it can sync to their other Mac computers, etc.. I also need to be able to draw charts online from their data which means some complex queries hitting hundreds of thousands of records.
Right now I have tried using Ruby/Rails/Mongoid in with a JSON parser on the app side sending up data in increments of 10,000 records at a time, the data is processed to other collections with a background mapreduce job. But, this all seems to block and is ultimately too slow. What recommendations does (if anyone) have for how to go about this?
You've got a complex problem, which means you need to break it down into smaller, more easily solvable issues.
Problems (as I see it):
You've got an application which is collecting data. You just need to
store that data somewhere locally until it gets sync'd to the
server.
You've received the data on the server and now you need to shove it
into the database fast enough so that it doesn't slow down.
You've got to report on that data and this sounds hard and complex.
You probably want to write this as some sort of API, for simplicity (and since you've got loads of spare processing cycles on the clients) you'll want these chunks of data processed on the client side into JSON ready to import into the database. Once you've got JSON you don't need Mongoid (you just throw the JSON into the database directly). Also you probably don't need rails since you're just creating a simple API so stick with just Rack or Sinatra (possibly using something like Grape).
Now you need to solve the whole "this all seems to block and is ultimately too slow" issue. We've already removed Mongoid (so no need to convert from JSON -> Ruby Objects -> JSON) and Rails. Before we get onto doing a MapReduce on this data you need to ensure it's getting loaded into the database quickly enough. Chances are you should architect the whole thing so that your MapReduce supports your reporting functionality. For sync'ing of data you shouldn't need to do anything but pass the JSON around. If your data isn't writing into your DB fast enough you should consider Sharding your dataset. This will probably be done using some user-based key but you know your data schema better than I do. You need choose you sharding key so that when multiple users are sync'ing at the same time they will probably be using different servers.
Once you've solved Problems 1 and 2 you need to work on your Reporting. This is probably supported by your MapReduce functions inside Mongo. My first comment on this part, is to make sure you're running at least Mongo 2.0. In that release 10gen sped up MapReduce (my tests indicate that it is substantially faster than 1.8). Other than this you can can achieve further increases by Sharding and directing reads to the the Secondary servers in your Replica set (you are using a Replica set?). If this still isn't working consider structuring your schema to support your reporting functionality. This lets you use more cycles on your clients to do work rather than loading your servers. But this optimisation should be left until after you've proven that conventional approaches won't work.
I hope that wall of text helps somewhat. Good luck!
I am hoping to use the Diff-Match-Patch algorithms available from google as apart of the Google-Mobwrite real time collaborative text editor protocol in order to embed a real time collaborative text editor in my program.
Anyways I was wondering what exactly might be the most efficient way of storing "global" copies of each document that users are editing. I would like to have each document stored on a server that is not local to any user and each time a user performs an "operation" ( delete insert paste cut ) that the diff is computed between their copy and the server and its patched etc... if you know the Google mobwrite protocol you probably understand what I am saying.
Should the servers text files be stored as a file that is changed or inside an sql database as a long string or what? Should I be using websockets to communicate with the server? I am honestly kind of an amateur when it comes to this but am generally a fast learner. Does anyone have any tips or resources I could follow perhaps? Thanks lot
This would be a big project to tackle from scratch, so I suggest you use one of the many open source projects in this area. For example, etherPad:
https://code.google.com/p/etherpad/
Mobwrite is using Differential Synchronization technique and its totally different from Operational Transformation technique.
Differential Synchronization suppose to have a communication circle that always starts from the client(the browser), which means you cant use web-sockets to send diffs from the server directly. The browser needs to request the server frequently to get the updates (lets say every 2 seconds), otherwise your shadow-copies will be out of sync.
For storing your shadow-copies when the user is active, you can use whatever you want, but its better to to use in-memory DB (Redis) since you need fast access to do the diffs and patches. And when the user leaves the session you don't need his copy anymore. But, If you need persistence in you app, you should persist only the server-copy not the shadow-copy (shadow-copies are used to find-out the diffs), then you can use MySQL or whatever you like.
But for Operational Transformation technique there are some nice libs out there
NodeJS:
ShareJS (sharejs.org): supports all operations for JSON.
RacerJS: synchronization model built on top of ShareJS
DerbyJS: Complete framework that uses RacerJS as its model.
OpenCoweb (opencoweb.org):
The server is either Java or Python, the client is built with Dojo