you could use the mysql binary logs, stored on an infinidisk, to accomplish much the same thing, however, the fact that the pgsql WAL’s are copied automatically by the database server, and no nasty hacks are needed makes PostgreSQL a much cleaner first choice IMHO. However I’ve of course not tested this… yet..
Amazon AWS
EC2 S3 PGSQL WAL PITR Infinidisk: The backend stack that just might change web services forever!
I have written mostly about MySQL here in the past. The reason for this is simple: MySQL is what I know. I have always been a die hard “everything in its place and a place for everything” fanatic. I’ll bash Microsoft with the best of them, but I still recognize their place in the market. And now it’s time for me to examine the idea of PostgreSQL And this blog entry about amazon web services is the reason. I don’t claim to exactly agree with everything said here… as a matter of fact I tend to disagree with a lot of it… but I saw “PS: Postgresql seems to win hands down over MySQL in this respect; WAL is trivial to implement with Postgresql)” and thought to myself: “hmm, whats that?” I found the answer in the PostgreSQL documentation on Write Ahead Logging (WAL) and it all made sense! The specific end goal here is Continuous Archiving and Point-In-Time Recovery (PITR). This plus the S3 Infinidisk certainly do make for an interesting concept. One that I am eager to try out! I imagine that the community version of infinidisk would suffice here since we’re not depending on random access here… that ought to make for some chewy goodness!
I’m always excited when we see something new for amazon web services
http://www.openfount.com/blog/s3infidisk-for-ec2
This certainly looks very interesting! I cant help but wonder if the memory caching in the neterprise version is enough to run small MySQL instances on? At the very least being able to MySQLdump regularly to a file directly on S3 would be useful as opposed to mysqldump to a file, split it into chunks, copy the chunks off to s3.
Perhaps I’ll contact them next week and see if they’ll let me take it for a test drive?!
HA EC2 Part #3: What Happens Once You’re Inside the Cloud
Onto what happens inside the cloud!
Since we’re looking to load balance what happens inside the cloud you might be tempted to ask why not use the same sort of method we used for load balancing (well at least fail-over) outside the cloud. And the answer is a resounding YOU CAN! But Rather like a cooking show where you _could_ use water to hydrate something but you could also “bring a little flavor to the party” by using chicken broth or wine, we can find ourselves with the option for a mighty fine set of bonus features — if we’re willing to look past the vanilla DNS round robin load balancing.
Ok, I suppose before I go on I should address you people still scratching your heads that I said you could use DNS for load balancing. I know you’re thinking that you’ll never achieve real balanced load with this method, and you’re right! Like I said more features await! And for everyone wondering why they should use a real load balancer if DNS is going to be “good enough” anyhow: remember that slight problem we mentioned about caching DNS servers? Well that problem would apply here as well, and since previously we couldn’t avoid it but now we can I see no reason not to. Plus it will be more of a headache here than there because the likelihood of a load balancer *LEFT ALONE* failing is a lot less likely than a web or database server which is constantly doing a great many things (and is subject to the whims of people — in the process of development.) Finally you get a single point at which you need to employ firewall configuration instead of X points where X is the number of back-end web servers.
Now as far as load balancers go there seem to be two prevalent kinds. First there is the TCP load balancer, and second there is the proxy.
A TCP load balancer functions on by IP address, Protocol, and Port. For example you may specify a group of servers as the back-end cluster for the IP address a.b.c.d port 80. And thats all you can do with that IP address and that port. It avoids a lot of complication by not caring, in the slightest, why or how you got there, or whether that particular set of web servers is really what you want. For this reason it’s not possible to specify fancy things like all requests on port 80 for example1.com go to cluster A, and example2.com to cluster B. To do that you need another IP address, or to access the cluster on a different port (say a.b.c.d port 81). For former is OK when you have multiple IP’s to work with and you can share IP’s between fail-over load balancers, but neither of those luxuries hold true in the EC2 environment. And the later is fine if you are planning to do this for your development team, but if you’re trying to drive normal web traffic to port 81 it might end up being a little less than convenient for your users.
Which is why I’m going to be focusing on the proxy.
Improperly configured the reverse proxy is a sure ticket to trouble on the internal LAN… Fortunately we aren’t dealing with an internal LAN, we’re dealing with servers which are publicly available anyway. What you can get from a reverse proxy, though, are extra features. You get load balancing, you get fail-over, you get url and host rule based redirection, and you get apache log file aggregation. You get it all… and, I might mention, at a very convenient low low price of FREE! WOO!
Um, er.. >clears throat< yea, never-mind that!
I would say that you have a couple of commonly known programs which can handle reverse proxying in Apache, and Squid. But… lets face it… both of those carry with them a more-so-than-necessarily complicated setup process and are like swatting at a fly with a cannonball. Sure they’ll work, but there re better alternatives for this. Two seemingly popular alternatives are pound and, the newcomer, perlbal. Both of these daemons offer better functionality that LVS (in the back-end server fail-over department) but which do we want to use?
The choice between the two is tough, and I can’t say that I’ve extensively used either, however pound does shine in three areas. First pound does have a notion of the idea of a SESSION and can even manage persistence/affinity (seemingly a LOT better than LVS manages it I might add (the persistence tables *ARE* wiped for a server that goes down!)) However if you have a web application which was developed with a “shared nothing” approach (which is a GOOD thing) this benefit does not really apply, so it’ll be up to the next to to knock your socks off. Second pound does SSL wrapping, taking that load off of your web servers (which is a good thing for responsiveness, isn’t it?). And finally pound offers a logging mode to emulate the apache combined log file format (both with and without virtualhosts.) Which puts pound in a class all by itself (and right up there with the hardware balancers I think). If none of those features matter to you or if (as is very possible) I’m wrong about perlbals feature-set. Then just pick one already (flip a coin, choose the one written in the language you prefer… or… hey… go read their docs and see which one you like better!)
The only drawback that comes to mind right now about using this approach is that you will be making use of text configuration files, so some parsing and rebuilding will end up being necessary for the registering and de-registering of web servers. I’ll add more in the comments if and when I think of more…
So there you have it.
Using a good DNS Service (with a low TTL, and decent API) mixed with a decent reverse proxy you have all the benefits of
- a load balancer
- load balancer fail-over
- rules based request redirector
- log consolidator
- back-end server filover
- and a single point for fire-walling
While this hasn’t exactly been a HOWTO, or a TOASTER, I hope that it’s definitely a pointer in the right direction for people who are looking to scale their applications which have been built on top of the Amazon EC2 (and SQS, and S3) services.
HA EC2 Part #2: Load Balancing the Load Balancer
Lets first address the problem of the dynamic IP address on the load balancer, because it doesn’t matter how good your EC2-side setup is if your clients can no longer reach your load balancer after a reboot. Also complicated because normally you want two load balancers to act as a fail-over pair in case one of them pops for some reason. Which means that we not only need to have the load balancers register with something somewhere we also need a method of de-registering them if, for some reason, they fail. And since downed machines usually don’t do a good job f anything useful we cannot count on them de-registering themselves unless we’re shutting them down manually. Which we don’t really plan on doing, now, do we?!
So here’s the long and short of the situation. Some piece of it, some starting point has to be outside the cloud. Now I know what you’re thinking: “but he just said we weren’t going to be talking about outside the cloud” but no, no, I did not say that; I said that we weren’t going to be talking about running a full proxy outside the cloud. I read that the EC2 team are working on a better solution for all of this, but for right now it’s in a roll your own state, so lets roll our own, shall we?
The basic building block of any web request is DNS. When you type in www.amazonaws.com your machine automagically checks with DNS servers somewhere, somehow, and eventually gets an IP address like this: 72.21.206.80. Now there can be multiple steps in this process, for example when we looked up www.amazonaws.com it *actually* points to rewrite.amazon.com, and finally rewrite.amazon.com points to 72.21.206.80. And this is a process we’re going to take advantage of. But first, some discussion on the possible ramifications of doing this:
DNS (discussed above) is a basic building block of how the internet works. And as such has had a dramatic amount of code written concerning it over the years. And the one type of code which may cause us grief at this stage is the caching proxy server. Now normally when you look up a name you’re asking your ISP’s DNS servers to look the name up for you, and since it doesn’t know it asks one of the primary name servers which server in the internet handles naming for that domain. once it finds that out it asks, a lot like this: “excuse me pdns1.ultradns.net, what is the address for rewrite.amazon.com?” to which your ISP gets a reply a lot like “The address for rewrite.amazon.com is 72.21.206.80 but thats only valid for 5 minutes.” So for 5 minutes the DNS server is supposed to be allowed to remember that information. So after 4 minutes when you ask again it doesn’t go to the source, it simply spouts off what it found out before. However after 5 minutes it’s supposed to check again… But some DNS servers ignore that amount of time (called a Time To Live (TTL)) and cache that reply for however long they feel like (hours, days, weeks?!) And when this happens a client might not get the right IP address if there has been a change and a naughty caching DNS server refuses to look it up for another week.
Alas, there is nothing we can do to fix that. I only mention it so that people don’t come knocking down my door yelling at me about a critical design flaw when it comes to edge cases. And to caution you: when your instance is a load balancer. It’s *ONLY* a load balancer. Don’t use it to run cron jobs, I don’t care if it’s got extra space and RAM, just leave it be. Because the fewer things happening with your load balancer the fewer chances of something going wrong, and the lower the chance of a new IP address, and the lower the chance of running into the above problem if the IP address doesn’t change, right? right!
So when you choose a DNS service you choose one which meets the following criteria:
- API, you need scriptable access to your DNS service
- Low (1-2 minutes) TTL
(so that when something changes you only have 60 or 120 seconds to wait)
Ideally you will have two load balancer images. LB1 and LB2 (for the sake of me not having to type long names every time). You can do this dynamically (i.e. X number of load balancers off the same image), and if you’re a good enough scriptor to be able to do it, then HOW to do it should be fairly obvious.
When LB1 starts up it will automatically register itself at lb1.example.com via your DNS providers API. It will then check for the existence of lb.example.com, if thats not set then it will create it as pointing to itself. If lb.example.com was previously set it till preform a check (HTTP GET (or even a ping)) to make sure that LB2 (which is currently “active” at lb.example.com) is functional. If LB2 is not functional LB1 registers itself as lb.example.com. LB2 performs the same startup sequence, but with lb1 and lb2 switched where necessary.
Now, at regular intervals (lets say 60 seconds), LB1 checks the health of LB2 and vic a versa. If something happens to one of them the other will, if necessary, register itself at lb.example.com.
Well, I think that basically covers the portion of how this would work outside the EC2 cloud, next I’ll deal with what happens inside the EC2 cloud. (piece not written yet… so it’ll take a bit longer than the last two)
HA EC2 Part #1: Identifying the Challenges
I was recently asked to look into load balancing web servers on the Amazon Elastic Cloud Computing Service (EC2). And managing this presents some very interesting problems which need to be worked around. To look at the subject I’ll break it into 3 distinct pieces. #1: Identifying the Challenges (Which you’re currently reading), #2: Load Balancing the Load Balancer, and finally #3 What Happens Once You’re Inside the Cloud. No promises as to how quickly I get these out 🙂
First lets look at what this would normally entail:
You would have a data center, and a router which feeds into a DMZ. On the DMZ you would have a set of load balancers (either hardware or software.) A set so that if one failed the other would take over its job. These load balancers have static IP addresses on the DMZ as well as on the LAN. They also have a shared IP address which they are the balancers for. When one goes down the other takes over the IP address. In a hardware solution this might be accomplished in a fairly elegant and network invisible way. In a software solution this normally entails using IP aliases and forcibly updating the ARP cache on the router.
So the load balancers are the bridge between the DMZ and the LAN. On the LAN, with the load balancers, are a group of web servers. also with static IP addresses. There is a monitoring functionality on the load balancer which detects if a web server is no longer available. When that happens the load balancer updates an internal table and no longer sends requests to that particular web server. When the web server becomes available again the load balancer detects this, updates those internal tables, and begins sending requests to the server once more. All of that happens with varying levels of complexity.
For the scenario of the web servers reply there are multiple possible configurations. The web server may reply to the load balancer and the load balancer would then handle getting the proper response from your data-center to the client (a full reverse proxy). The web server might also reply directly to the client through a network route (in Linux Virtual Server (LVS) terms this is called “Direct Routing” (LVS-DR))
[ WAN ] -> [ Server ]
[ ROUTER ] |-> [ Server ]
[ DMZ ] <-> [ Load Balancer ] <-> [ LAN ] <-+-> [ Server ]
|-> [ Server ]
-> [ Server ]
The first thing that jumps out at me is that there is one key assumption in the above setup possibilities, and that is that everything is able to obtain a static IP address. That is that every time a given machine goes down, it comes back up at the same IP address. This is not true of the EC2 service. Your EC2 instances are dynamically allocated new IP addresses (and host-names) each time they are started (and consequently restarted.) So…
- No static IP for the load balancer
- No static IP for the web servers
Which means that on top of the challenges of installing and configuring a normal software load balancing solution there are several fold more challenges to overcome to be “successful” in your endeavor.
- You need to notify your clients if the load balancer address has changed
- You need to notify your web servers if the load balancer address has changed
- You need to notify your load balancer if the address of a web server has changed
Now you could, technically, circumvent the first o these challenges by housing the load balancer outside of the EC2 cloud, however this doesn’t make a whole lot of sense seeing as you would end up paying twice for all the bandwidth consumed (You would have to pay for the incoming request at the load balancer, then to make the same request to a web server, then the cost of the reply from the web server to the load balancer, and finally the cost of the reply from the load balancer to the client) so for the sake of this little mental pushup we’ll not even consider that a viable option, only worth mentioning (and we have, so now that thats over…)
Series: CRM on S3 & EC2, Part2
So we’ve touched a bit on what to look for in your database. The comments made were by no means specific, and the requirements will vary from place to place. But the underlying principals are what are really important there. Now lets move on to something a bit more specific. Backup.
There is an important caveat to this information: Nobody has done this enough to really have a set of scalable one-size-fits-all tools (or a tool chain) fit for the job… You’ll have to be OK with doing some in-house experimentation. And be OK with the idea of maybe making a couple of miss-steps along the way. As is the case with any new (OK new to YOU) technologies there are some things you just have to learn as you go.
To setup a system that is fault tolerant, and to develop a system in which you manage your risks requires a balance of acceptable versus unacceptable trade off situations. Your main types of backups are as follows:
A) Simple local backup. your old stand-by tar and his friends bzip2, gzip, and even compress. They’ve been doing backups since backups were backups (or almost anyhow) and they are good friends. In this kind of a situation they aren’t the whole solution but you can bet your butt that they’re a part of it.
B) Hard-Copy backup. This isn’t what you want, but worth mentioning. This kind of backups consists of hard disks, tapes, CDs DVDs, etc, which are copied to and then physically removed from the machine. The advantage to this type of backup is that you can take them offsite incase of a local disaster, but in an EC2+S3 business there is no such thing as a local disaster. So if you, once per week/month/whatever, just copy down your latest backups from S3 that should suffice.
C) Copy elsewhere backup. This is going to be bread and butter for the bulk of the solution. It’s not the entire solution. But it’s a fairly big piece. In this case S3 is your “elsewhere”
D) Streaming backups. Examples of streaming backups are MySQL’s replication, or pushing data into an Amazon SQS pipe for playback at a later point. Also a key player in what will surely be your ending strategy.
Well that was fun. Learning just enough to be dangerous but not enough to actually do anything… And certainly not enough to answer the question. So lets get to it.
You will have two distinct areas of backup which will be important to you. You have the UI end, and the DB end. Both these sections should be approached with different goals in mind, because the usage pattern on them ends up being different.
The Front End
You’ve no doubt got a development environment setup somewhere, and as you make bug fixes to this environment, or add features, or change layouts to announce your IPO, or whatever you need to push a snapshot to your servers *AND* any new servers you bring up need to have the new UI code and not the old UI code.
For the sake of argument, here, I’ll assume that you have a SVN or CVS server which holds your version-controlled code (you *ARE* using version control right?) So your build process should, ideally, update the stable branch on your Revision Control Server, and send out a message to your UI servers that an update is available. They should then download the new code from RCS to a temporary directory, and once there you pull the fast-move trick:
$ mv public_html public_html.$(date +%s) && mv public_html.new public_html
At this point all of your UI servers received the message at the same time, and update at the same time. Any new server should have, in its startup scripts sometime after the network is brought up, a process which performs the above update before even bringing up your HTTP service.
And that was the easy part… Now for MySQL
As for MySQL, I’ve outlined my thoughts on that here already in my article: MySQL on Amazon EC2 (my thoughts) Which options you choose here depend on a couple of things: First the skill level of the people who will be implementing the entire procedure *AND* the skill level of the people who will be maintaining it (if those people aren’t the same people). But one very serious word of caution: Whatever you do stop thinking of an EC2 instance as 160GB of space for MySQL and start thinking of it as 60Gb (70GB MAX) because backing up something that you do not have the space to copy is a difficult task which normally required bringing things offline — trust me on this.
My gut feeling for you is that if you owned/rented one physical server to be your write server for your database setup. something roughly equal to the specs of the EC2 virtual machine, except with 320Gb of disk space. That would be your best bet for now. You could keep your replication logs around for the entire history of your database… for a while
You also should keep one extra MySQL instance (on EC2 if you like) up and running for the sole purpose of being up to date. You would then periodically turn it off and copy the entire thing up to S3. So that when you had to restore a new instance you would simply copy those files down, assign the server-id, and let it suck everything new down via replication.
The gotcha here is that this wont last forever… at least not on one database. There will come a time, if you get a “lot” of usage, when the process of downing a server copying it, copying it, bringing it up and waiting for replication will become infeasible. It will eventually just stop adding up. It’s at that point you will have to make a couple of careful choices. If you have properly laid out your schema you can pull your single monolithic database apart, distribute it amongst several database clusters, and carry on as you have been. If you have properly laid out your schema in a different way you will be able to assign certain users to certain clusters and simply write a migration tool for moving users and their data around between database clusters. If you have not properly laid out your data you can choose whether to spend time and money re-working your application to make it right. Or you can spend time and money on buying big “enterprise class hardware” and give yourself time to make things right.
Unless you can truly count on being able to bleed money later on. You’ll VERY CAREFULLY consider your schema now. It will make all the difference. And if you end up with 2+TB of data which is completely unmanageable… well don’t say I didn’t warn you… Those kinds of optimizations may seem silly now when you’re only expecting 5-25GB of data but they wont be silly in 2-4 years.
Series: CRM on S3 & EC2, Part1
Danny de Wit wrote in with a request for collaboration on how to best use EC2 and S3 for his new Ruby On Rails CRM application. And I’m happy to oblige.
At this point I dont know much about what he’s doing, so I hope to start rough and open a dialogue with him and work through the excersice over a bit of time.
The story so far
We have a rails front end, a Dabatase backend, EC2, and S3
Well… that was a quick rundown…
Summary of what we will need to accomplish the task on S3 and EC2
First off we will need to be able to think outside the traditional boxes. But I think Danny is open to that. Second we will need to deal with the database itself. Third We have to deal with the issue of dynamic IP addresses. Fourth we have to deal with some interesting administrative glue (monitoring, alerting, responding) Fifth we have to deal with backups. And finally we have to deal with code distribution.
Now, Where do we start?
First we should start with the database. I wont lie to you, most of the challenge in regards to using these services will be centered around the database. We need to examine how it’s laid out, how its accessed, and what our expectations are when it comes to size. Specifically what we need to look for are two main things: A) bottlenecks, and B) data partitioning strategies.
Bottlenecks. We have to examine where we may or may not have trouble as far as data replication goes. Because if we are making hourly backups and we have to bring up another server at the half hour marker we’re going to have to have a strategy in place to bring the data store up to date. And the layout of the database can make this particularly prohibitive or it could make this very easy. And besides… having a bunch of servers doesnt help if they cant stay in sync.
Data partitioning. It’s easy to say “later on we’ll just distribute the data between multiple servers” but unless you’ve planned for a layout which supports this you might have a particularly difficult time doing so without makor reworking on your application. Also data partitioning can be your friend in the speed department as well. If you’re thoughtful about HOW you store your daya you can use the layout itself to your advantage. For example a good schema might actively promote load ballancing where a bad schema will cause excessive load on particular segments. A good schema will actually act as an implied index for your data, and a bad schema will require excessive sorting and indexing
So what now?
So, Danny, the ball is in your court. You have my e-mail address. You have my blog address. Lets get together and talk database before we move forward into the glue.
Random Musing: Bluring the Line Between Storage and Database?
As food for thought…
If you had a table `items`
- itemId char(40),
- itemName varchar(128),
Another table `tags`
- tagId char(40),
- tagName char(40),
And a third table `owners`
- ownerId char(40),
- ownerUsername char(40),
- ownerPassword varchar(128),
It would theoretically be possible to have an S3 bucket ItemsToTags inside which you put empty objects named (ownerId)-(itemId)-(tagId). And a TagsToItems S3 bucket inside which you put empty objects named (ownerIf)-(tagId)-(itemId), it would then be possible to use the Listing Keys Hierarchically using Prefix and Delimiter method of accessing your S3 buckets to quickly determine what items belong to a tag for an owner, and what tags belong to an tag for an owner. You would be taking advantage of the fact that that There is no limit to the number of objects that one bucket can hold, and no impact on performance when using many buckets versus just a few buckets. You could reasonably store all of your objects in a single bucket, or organize them across several different buckets. (both the above links are to quotes taken directly from the S3 API docs provided by amazon themselves)
Using this method it would be possible, I think, to use the S3 datastore in a VERY cheap manner and avoid having to deal with the massive cost of maintaining these kinds of indexes in a RDBMS or on your own filesystems… Interesting. And since the data could be *anything* and you have, by default you have a many to many relationship here you could theoretically store *anything* and sort by tags…
Granted to find a tag related to multiple items you would have to make multiple requests, and weed out the diffs. but. if you’re only talking on the order of 2 or 3 tages per piece of data… it might just be feasible..
Now… Throw in an EC2 front end, and a SQS interface… interesting…
Makes me wonder what the cost and speed would be (if it would be an acceptable tradeoff for not having to maintain a massive database cluster)
Disclaimer: this is a random musing. I’m not advising that anybody actually do this…
Where should AmazonAWS go next?
We have SQS, we have S3, and we have EC2, so what next from the Amazon AWS team?.
There is really only one piece of the puzzle missing… And its a piece that has a lot of people griping. I have a strong hunch that Amazon is working on the problem, because I have a strong hunch that it is (or was) one of their major hurdles. And that problem is the database service.
How do you provide an easy to use interface to relational lookup-able storage? How do you make it universal? How do you make it secure? How do you make it FAST?
The first 3 questions are all answerable in roughly the same way: Make it a service, and let the service handle the interface, security, and universality. They’ve sucessfully applied the web-service to messaging, storage, and cpu power, theres no reason that this wouldnt be the final piece to the jigsaw puzzle. The last question carries with it the greatest problem, though. Allowing people to store data and run queries without the innevitable tanking of the server process would be a challange, to say the least (artificial intelligence is no match for human stupidity, after all).
But thats besides the point. If you break down into two components: anchors and tags — that is something is data or something is data about the data. provide a schema that works without collision problems, and – more importantly works both ways (finding tags related to an anchor, AND finding anchors relating to a tag) you cover probably 90% of peoples needs in one fell swoop.
I’ve been thinking a lot about how to do this, lately, as I’ve been drowning in a sea of data myself which is easy to manage in one direction but difficult in the other while keeping the size of the whole thing down.
Not only would that provide Amazon with the ability to have its finger in basically every new technological cookie jar BUT would provide huge massive gigantic enormous amounts of datas on what people really think about things. It would be an exceptional win for amazon, I think, and could indeed be leveraged to a huge advantage in the marketplace market. Because, as netflix has shown us recently, reliably finding things which relate to other things is *big* business.