The Web is my ESB, but it's slow...
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One reason is that people people roll their own ESB. There are many nice ones, but they seem big, complex and expensive. Wikipedia has a handy list of ESB's and vendors. Instead of using a purpose-built ESB, it seems sensible to use the the web as an ESB. There's nothing wrong with using the web as an ESB. What's wrong is assuming that the web has our imaginary level of performance.
It appears that there are two assumptions people make. Here's what happens.
They design a really complex web transaction and then complain. Attributes of these complex web transactions:
- They're part of the presentation, for example, a response to an HTML-based GET request. With a person watching it execute.
- They involve aggregating information from other web services.
- Sometimes, they involve multi-step
- It's slow. The user is forced to wait for a long time.
unreliable. Sometimes an information source doesn't respond at all.
In the case that the transaction is an "order from stock" (it involves competition for physical goods) then the user must wait. When ordering books from inventory, or airplane seats, or hotel rooms, the web site must display a clever animation while it grinds away doing the transaction.
But, when the transaction is placing an order, or it involves aggregating information, then there are better things than making the user sit there and watch the beach-ball spin while your transaction grinds away.
- Make them wait while you grind. This is the "do nothing" solution; if it's slow or crashes, the user will complain.
- Queue Up a Work Request. Tell the user you're queueing it up. Allow them to monitor the status of their queued work request.
- Pre-Cache. We can often gather the expected information in advance and store it locally. When we're providing some kind standard information aggregates, we should gather it in advance.
The work queue is no different from an eBay auction. You place an order or request and monitor the status. Information aggregation shouldn't take a week; it should be quick.
The user fills in their form, or uploads their request. Your web transaction puts it into the queue, and gives immediate feedback that it was accepted.
Your web site must include, therefore, a background processor that actually handles the request. You can spawn a "nohup" subprocess. You can have a "crontab" schedule that checks the queue every minute. You can have a proper daemon spawned by "init".
The background process dequeues the request, gathers the data. It handles slow, timeout, crashes, etc. When it's done, the status is updated. Maybe an email is sent.
Many applications aggregate data. Except in the rare case that the data involves competition over physical goods (inventory levels, current availability, etc.) the data doesn't change constantly.
Indeed, many times the data is changed on a pretty slow schedule. Weather forecasts, econometric data, etc., changes slowly. It's easy to query this data and cache it locally. This gives the illusion of immediate response.
In some cases, the data may involve something like a Twitter feed, where there is a constant flow of data, but there's no competition over physical goods. Folks like to wring their hands over getting the absolute up-to-the-second Twitter information. This is, of course, impossible because the Internet is (1) slow and (2) unreliable. What does up-to-the-second mean when your request is trashed by a momentary problem with your web host's DNS server?
Even Twitter postings can be pre-cached. Polling the Twitter server -- and caching the interesting tweets -- every few minutes will yield results that are every bit as current as trying to get a "live" feed. Remember, the folks tweeting have latency and unreliability at their end. The Twitter servers have latency and unreliability. Your web server has latency and unreliability. Your user's browser has latency and unreliability.
In some applications, the data is very high value. Electronic Health Records, for example. Econometric Data from commercial sources (see the NABE Tools page) for example. In the case of high-value data we have to account for (1) slow and we have to resolve (2) unreliable.
We can't fix slow. We have to handled it by a combination of pre-caching and managing request work queues. Use Case 1: users make a standard econometrics request; we have the current data that we've subscribed to. Done. Use Case 2: users make a non-standard request; we queue up the task, we gather the information from sources, when we've finished the job, we close the task and notify the user.
The unreliable is handled by service level agreements and relatively simple work-flow techniques. When integrating data from several sources, we don't simply write a dumb sequence of REST (or SOAP) requests. We have to break the processing down so that each source is handled separately and can be retried until it works.
Background Processing Tier
This says that a standard web architecture should have the following tiers.
- Presentation Tier. JSP pages, Django View Functions and Templates.
- Services Tier. An actual ESB. Or we can write our own Backend Processor. Either way, we must have a separate server with it's own work queue to handle long-running transactions.
- Persistence Tier. Database (or files). Your presentation and ESB (or Backend) can share a common database. This can be decomposed into further tiers like ORM, access and actual database.
You can try some other architectures, but they are often painful and complex. The most common attempt appears to be multi-threading. Folks try to write a web presentation transaction that's multi-threaded and handles the long-running background processing as a separate thread. Sadly threads compete for I/O resources, so this is often ineffective.
Writing a REST+WSGI ESB (in Python) is relatively straight-forward.
Use wsgiref, or werkzeug. Create the "services" as WSGI applications that plug into the simple WSGI framework. Add authentication, URL processing, logging, and other aspects via the WSGI processing pipeline. Do the work, and formulate a JSON (or XML) response.
Published at DZone with permission of Steven Lott, DZone MVB. See the original article here.
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