(Asking on behalf of a client.) In Corda is there any issue with a flow initiating many (even hundreds) of subflows, for example updating many participants from a single flow? If not, what is considered the best practice when one main transaction needs to trigger many other transactions?
Initiating many subflows within a flow is not an issue and will not affect performance.
Deciding whether to use many subflows or keep everything in a single flow is a matter of personal preference and readability.
I am currently working on a mobile application that uses the OneNote REST API that sometimes have really enormous ping, so the cache implementation is one of the most important aspects with impact on my application's performance. But, for the purpose of implementing traffic-effective cache with data staying up-to-date the timestamp is needed for all of the entities that are not stable or their amount can grow (practically any entities fit these conditions). So, The question is whether the timestamp (e.g. lastModified, lastModifiedTime etc.) properties do not present in some entities, for example - permissions, principal objects or they are just hidden and it is possible to use the $expand to get them.
If you don't see a timestamp in one of the entities, then the entity does not have it.
Adding timestamp for some of this entities can be challenging for us, as they the datastore we're using might not have it, but I encourage you to ask for suggestions in our uservoice page.
https://onenote.uservoice.com/forums/245490-onenote-developer-apis
I'm starting a new project and am interested in architecting it as microservices. I'm trying to wrap my head around it:
Say that I have an order service and a product service. Now I want to make a report service that gives me all orders that contain a product from a certain product category.
Since order's dont know about products that means that I would need to fetch all orders, loop them and fetch products for each order and then return those how match.
Is this assumption correct or is there any more efficient way of doing this with microservices?
In a microservices architecture, the procedure is to distill the use cases and the service boundaries of the application. In the question above, there are at least two service boundaries, namely one for transactions and another for reporting.
When you have two different service boundaries, the typical approach is to duplicate some data elements between them eg. whenever you make a sale, the data, should be sent to both the reporting and transactional services. One possible approach of broadcasting the data to the different boundaries is to use a message queue. Duplicating the data allows them to be evolve and operate independently and become self sufficient which is one of the goals of microservices.
A personal word of advice though, you might want to start with a monolith before going the microservices route. Microservices are generally more operationally heavy; it will be difficult to reason about its advantages during the initial application stages. It tends to work better after having developed the monolithic application since it would be easier to see what didn't work and what could be improved by a microservices-like system.
I'm beginning the process of instrumenting a web application, and using StatsD to gather as many relevant metrics as possible. For instance, here are a few examples of the high-level metric names I'm currently using:
http.responseTime
http.status.4xx
http.status.5xx
view.renderTime
oauth.begin.facebook
oauth.complete.facebook
oauth.time.facebook
users.active
...and there are many, many more. What I'm grappling with right now is establishing a consistent hierarchy and set of naming conventions for the various metrics, so that the current ones make sense and that there are logical buckets within which to add future metrics.
My question is two fold:
What relevant metrics are you gathering that you have found indespensible?
What naming structure are you using to categorize metrics?
This is a question that has no definitive answer but here's how we do it at Datadog (we are a hosted monitoring service so we tend to obsess over these things).
1. Which metrics are indispensable? It depends on the beholder. But at a high-level, for each team, any metric that is as close to their goals as possible (which may not be the easiest to gather).
System metrics (e.g. system load, memory etc.) are trivial to gather but seldom actionable because they are too hard to reliably connect them to a probable cause.
On the other hand number of completed product tours matter to anyone tasked with making sure new users are happy from the first minute they use the product. StatsD makes this kind of stuff trivially easy to collect.
We have also found that the core set of key metrics for any teamchanges as the product evolves so there is a continuous editorial process.
Which in turn means that anyone in the company needs to be able to pick and choose which metrics matter to them. No permissions asked, no friction to get to the data.
2. Naming structure The highest level of hierarchy is the product line or the process. Our web frontend is internally called dogweb so all the metrics from that component are prefixed with dogweb.. The next level of hierarchy is the sub-component, e.g. dogweb.db., dogweb.http., etc.
The last level of hierarchy is the thing being measured (e.g. renderTime or responseTime).
The unresolved issue in graphite is the encoding of metric metadata in the metric name (and selection using *, e.g. dogweb.http.browser.*.renderTime) It's clever but can get in the way.
We ended up implementing explicit metadata in our data model, but this is not in statsd/graphite so I will leave the details out. If you want to know more, contact me directly.
A web application we wrote intended for one customer is going to be product-ized and sold to dozens of companies, and we will be doing the hosting.
I could use some guidance about the pros and cons of rolling out a seperate instance for each customer versus going with a single (or very small number of) multi-tenant instances.
At first, as we ramp up, I will have to roll out a seperate instance of the application for each new customer (they will come online one at a time) because it's the only immediate option. I imagine this won't scale very well as far as maintenance goes - rolling out changes will become very tedious and possibly error-prone once there are more than 4 or 5 instances out there. Unless we automate that somehow.
Also, the single-instance philosophy seems like it might lead to a bunch of forks if people need customizations. And it would be nice to avoid that.
So what has your experience been with this?
Bonus question #1: What's the performance difference between 10 SQL Servers with 2m records each versus one huge one with 20m? Let's say they are all in one table and we're mainly doing inserts and selects on single records. Sometimes the selects are on an indexed varchar(12) or date field.
Bonus Question #2: I imagine that to avoid forking, we would have to make the customizations configurable, or build a plug-in architecture. However, that might increase the cost of doing customizations, and I don't want to be one of those shops that takes a week to resize a textbox, and I don't want to over-invest in infrastructure. Any thoughts on that?
Scale Details
Each customer will have a decent amount of data -- up to a few million records.
There will be a very small number of concurrent users, only a few per customer, plus a handful of internal reps on our end.
It's unclear whether each customer will require customizations, but I would say some of them probably will, and maybe some of those changes will be things that other customers will not want to see.
when faced with a similar challenge, here's what we did:
we have one code base with multiple sql servers. we do maintain multiple iis servers with copies of the same code base. we are free to move clients around from sql server to sql server to maximize performance.
if a customer has the $ for it, we will install them on their own server and maintain a separate iis server for them. this accommodates the largest customers for whom paying much more money every month (10 fold more money). we do not, however, give them a separate code base. if they need a mod, we make it visible on a per client basis (see #3)
custom programming usually results in a configurable option. even the people who pay us to have their own server get the same version of the code. sometimes its as simple as a clause in the code that says "if the customer = "ourbigcustomer then turn on this option". yes, that's kludgy hard-coding, but if the customer has enough money, that is fine with me.
i didn't quite get from your question whether you wanted to mix different customer's data into one big database .. our rule is we never do that (never ever). it is one of the wisest choices we ever made. it makes data manipulation much less risky and restores of data easier.
I don't see a good reason for either of your two options. I think the real answer lies somewhere in the middle: having multiple instances, each hosting multiple clients.
This adds another layer of automation processing, but it means you can keep the hosting cheap (you won't need to go out and buy a Cray any time soon) and (hopefully) this sort of mentality means you could do failover backups fairly easily.
But let's not get ahead of ourselves... We're talking about a webapp, right? Get your database(s) and aspnet on different machines. Cluster your databases and you'll have a much happier time playing around with various front-end scenarios. You'll also be able to upscale whichever area runs out of puff first.
By the sounds of it, you'll end up with one clustered database over half if not a full dozen database machines and only a couple of front-end boxes.
As for customisations, you've nailed it. You either provide a completely database-hosted set of editable templates or you have to customise who instances. I'm all for the first. It's a lot of work (without much in return) but it's well worth it as you should only need to change the core code when (you will!) you do upgrades. Hunting through a hundred customers' custom instances to make sure they upgrade safely will kill a developer! Template are the answer. At the very very least, you could allow custom CSS without much pain (but they'd need somebody who knew their stuff).
Edit: I've seen a couple of posts going for the all-in-one method. Splitting the instances over multiple machines insulates you from a couple of things:
If you introduce a bug not caught in testing, only a few clients are effected at once
Hardware fails. Having one mega-server fall over will annoy a lot of people at once. Having a failover mega-server is massively expensive. Having a spare failover box per three or four running servers is much cheaper and annoys fewer people.
Performance can be balanced between boxes on a client-by-client basis, so you can put a few light-use clients with a heavy client, or just fill a box with a few medium-use clients, etc.
On the same idea, usage spikes or other slowdowns only effect clients on the same box. Of course this doesn't mean the same for the database, but you can split that up into a cluster of clusters when you get there.
The big advantage of individual instances will be scaling out as each customer's demand increases. For example if you're running on a single server and one customer suddenly needs more preformance you're stuffed. But if they're all individual then moving that customer to a shiny new server is relatively easy.
The big disadvantage will be in managing the instances all individually. (regardless of whether they're all running on the same server or not).
Regardless you should only ever have one instance of the codebase. And customisation should all be controlled through plugins and configuration. Front end should naturally be seperate from content. Although the cost of making a change may be higher, the benefit in terms of features you can offer your other customers (which will just be customisations you've been asked to do) will pay off I'm sure. Which is to say nothing as to how much easier it'll be to manage a single codebase, as opposed to several.
I would strongly advise going with the single instance hosted by your company. This has the following advantages:
You have physical access to all code
and databases to make changes and
updates.
You control the quality of the
hardware it is running on.
When you fix a bug in common code,
you have fixed it once for all
customers.
You can refactor the application
design to better support customer
specific code and avoid forking.
As the number of customers grow, you
can scale-up and scale-out your
servers to meet
performance/responsiveness
requirements.
Your application code and databases
cannot be tampered with by
"inquistive" customers.
I would have to say it is almost more important where your application is running as opposed to how many separate instances there are of it.
Sure, maintaining multiple separate instances is not ideal due to the support/maintenance overhead, but if these apps. are all on servers you control, life is much easier then needing remote/ physical access to different customers networks and servers.
Joel Spolsky also talks about exactly this on StackOverflow podcast 67.
One thing Joel has learned from
selling Fogbugz: software designed to
be installed on a server in-house at a
customer’s site, under full control of
that customer, is almost never worth
the hassle
20 million records relatively speaking is not a huge SQL Server database. A single well provisioned SQL Server could handle this size comfortably. More important however is the number of concurrent accesses to the database. However you say that there will be only a few users per customer so is unlikely to hit you until the level of concurrency grows.
All of the above are good points but you are missing two key questions. What price point is the service offered at and how many customers (order of magnitude) will you ultimately have to support (ie market size)? In 3 years will you have a maximum of 10 customers each of which will pay you $500,000 per year or 500 customers each paying you $10,000 per year? For a small set of high paying premium customers the advantages of individual deployments is clear, whereas the lower prices and larger customer bases demand a shared solution (a la Oli's comment) is the best way to go. Or go with a cloud platform, although I've only read the hype and tinkered rather than deployed that in the field.
Bonus Question 1: table layout, indexing, number of reads / writes, efficiency and complexity of stored procedures (you are using procs or at least prepared statements, right?) all matter a heck of a lot more than the number of physical records in the database to a point. Beyond that you will likely find yourself needing to either provide individual SQL Server instances for each customer or for a pool of customers, once again depending on some of the questions I raised above.
Bonus Question 2: Putting the time into your design for templating and a plugin architecture is essential in this situation and you need to do it sooner rather than later. Once you're in the grind of customizing code for paying customers you will likely not have the time to do it right. This point cannot be stressed enough. Templates and admin tools that give you quick and deep access to data-driven changes in your product will save you a lot of time down the road. As your company / group expands you can then add less technical staff that can be "product experts" who can perform 90% of customizations and maintenance, freeing up your core to continue development or move on to other projects. Finally, don't neglect your data tier in this planning process. Having a core data tier of (almost) immutable stored procs and tables is very important, with custom tables and stored procs clearly demarcated using a good naming convention.
Good luck, feel free to provide more details if you'd like more specific suggestions.
Based on some of the advice received here, we did end up implementing a monolithic multi-tenant version of our application.
I'm glad we did. By the time it was done, we had 3 or 4 forks of the code base (mainly custom skins and things we didn't have n-level support for, but also some actual features), and it was only getting crazier.
We got the multi-tenant version up and successfully folded everything in. There ended up being a lot to think about and a lot to keep track of, but our customers never even knew they had been moved to a new system.
I will say that the actual customer migration was a bit of a bear. I thought at first that we would be able to do it by hand in the backend, but I ended up having to write some fairly involved scripts to get the job done. There were just too many identity columns, and it's not like you can just turn off constraints temporarily when you're importing into a live production system.