till's blog

This is part three of a beginner series for people with a MySQL/PHP background. Apologies for the delay, this blog entry has been in draft since the 13th December of last year (2009).

Follow these links for the previous parts:

• part I
• part II

Recap

Part I introduced the CouchDB basics which included basic requests using PHP and cURL. Part II focused on create, read, update and delete operations in CouchDB. I also introduced my nifty PHP CouchDB called ArmChair!

ArmChair is my own very simple and (hopefully) easy-to-use approach to accessing CouchDB from PHP. The objective is to develop it with each part of this series to make it a more comprehensive solution.

Part III

Part three will target basic view functions in CouchDB — think of views as a WHERE-clause in MySQL. They are similar, but also not. :-)

Map-Reduce-Thingy

If you read up on CouchDB before coming to this blog, you will probably heard of map-reduce. There, or maybe elsewhere. A lot of people attribute Google's success to map-reduce. Because they are able to process a lot of data in parallel (across multiple cores and/or machines) in relatively little time.

I guess the PageRank in Google Search or Google Analytics are examples of where it could be used.

In the following, I'll try to explain what map-reduce is. For people without a science degree. (And that includes me!)

Map

Generally, map-reduce is a way to process data. It's made off two things, map and reduce.

The idea is that the map-function is very robust and it allows data to be broken up into smaller pieces so it can be processed in parallel. In most cases the order data is processed in doesn't really matter. What generally counts is that it is processed at all. And since map allows us to run the processing in parallel, it's easier to scale out. (That's the secret sauce!)

And when I write scale-out, I don't suggest to built a cluster of 1000 servers in order to process a couple thousand documents. It's already sufficient in this case to utilize all cores in my own computer when the map task is run in parallel.

In CouchDB, the result of map is a list of keys and values.

Reduce

Reduce is called once the map-part is done. It's an optional step in terms of CouchDB — not every map requires a reduce to follow.

Real world example

• take a simple photo application (such as flickr) with comments
• use map to sort through the comments and emit the names of users who left one
• use reduce to only get unique references and see how many comments were left by these user

In SQL:

SELECT user, count(*) FROM comments GROUP BY user

Why the fuzz?

Just so people don't feel offended. Map-reduce is slightly more complicated than my example SQL-query but it's also not some secret-super-duper thing. Its strength is really parallelization which requires the ability to break data into chunks to process them. The end.

These are some random operational things I learned about CouchDB. While I realize that my primary use-case (a CouchDB install with currently 230+ million documents) may be oversized for many, these are still things important things to know and to consider. And I would have loved to know of some of these before we grew that large.

I hope these findings are useful for others.

Compaction

CouchDB doesn't take great care of diskspace — the assumption is that disk is cheap. To get on top of it, you should run database and view compaction as often as you can. And the good news is, these operations help you to reclaim a lot of space (e.g. I've seen an uncompacted view of 200 GB trim down to ~30 GB).

Cleanup

In case you changed the actual view code, make sure to run the clean-up command (curl -X POST http://server/db/_view_cleanup) to regain disk space.

Performance impact

Database and view compaction (especially on larger sets) will slow down reads and writes considerably. Schedule downtime, or do it in off-peak hours if you think the operation is fast enough.

This is especially hideous when you run in a cloud environment where disk i/o generally sucks (OHAI, EBS!!!).

To stop either of those background-tasks, just restart CouchDB.

(Just fyi, the third option is of course to throw resources (hardware) at it.)

Resuming view compaction?

HA, HA! [Note, sarcasm!] — view compaction is not resumable, like database compaction.

View files

I suggest you split views into several design documents — this will have the following benefit.

For each design document, CouchDB will create a .view file (by default these are in var/lib/couchdb/database-name/.database-name_design/). It's just faster to run compact and cleanup operation on multiple (hopefully smaller files) versus one giant file.

In the end, you don't run the operation against the file directly, but against CouchDB — but CouchDB will deal with a smaller file which makes the operation faster and generally shorter — I call this poor man's view partitioning.

Warming the cache

Cache warming is when a cache is populated with items in order to avoid the cache and server being hit with too much traffic when a server starts up and here is what you can do with CouchDB in this regard.

The basics are obvious — updates to a CouchDB view are performed when the view is requested. This has certain advantages and works well in most situations. Something I noticed is that especially on smaller VPS servers where resources tend to be oversold and and are rare in general, generating view updates can slow your application down to a full stop.

As a matter of fact and CouchDB does often not respond during that operation when the disk was saturated (take into account that even a 2 GB database will get hard to work with if you only have 1 GB of RAM for CouchDB and the OS, and whatever else is running on the same server).

The options are:

1. To get more traffic so views are constantly update and the updates performed are kept at a minimum.
2. Make your application query views with ?stale=ok and instead update the views on a set interval, for example via a curl request in a cronjob.

Cache-warming for dummies, the CouchDB way.

View data

For various reasons such as space management and performance, it doesn't hurt to put all views on its own dedicated partition.

In order to do this, add the following to your local.ini (in [couchdb]): view_index_dir = /new_device

And assuming your database is called "helloworld" and the view dir is /new_device, your .view-files will end up in /new_device/.helloworld_design.

Overshard

I've blogged on CouchDB and CouchDB-Lounge before. No matter if you use the Lounge or build sharding into your application — consider it. From what I learned it's better to shard earlier (= overshard), than when it's too late. The faster your CouchDB grows, the more painful it will be to deal with all the data stuck in.

My suggestion is that when you rapidly approach 50,000,000 documents and see yourself exceeding (and maybe doubling) this number soon, take a deep breath and think about a sharding strategy.

Oversharding has the advantage that for example you run 10 CouchDB instances on the same server and move each of them (or a couple) to their own dedicated hardware once they exceed the resources of the single hardware.

If sharding is not your cup of tea, just outsource to Cloudant — they do a great job.

CouchDB-Lounge

CouchDB-Lounge is Meebo's python-based sharding framework for CouchDB. The lounge is essentially an nginx-webserver and a twistd service which proxies your requests to different shards using a shards.conf. The number of shards and also the level of redundancy are all defined in it.

CouchDB-Lounge is a great albeit young project. The current shortcomings IMHO include general stability of the twistd service and absence of features such as _bulk_docs which makes migrating a larger set into CouchDB-Lounge a tedious operation. Never the less, this is something to keep an eye on.

Related to CouchDB-Lounge, there's also lode — a JavaScript- and node.js-based rewrite of the Python lounge.

Erlang-Lounge

What I call Erlang-Lounge is Cloudant's internal erlang-based sharding framework for CouchDB. It's in production at Cloudant and to be released soon. From what I know Cloudant will probably offer a free opensource version and support once they released it.

Disk, CPU or memory — which is it?

This one is hard to say. But despite how awesome Erlang is, even CouchDB depends on the system's available resources. ;-)

Disk

For starters, disk i/o is almost always the bottleneck. To verify if this the bottleneck in your particular case, please run and analyze [iostat][] during certain operations which appear to be slow in your context. For everyone on AWS, consider a RAID-0 setup, for everyone else, buy faster disks.

CPU

The more CPU in a server, the more beam processes. CouchDB (or Erlang) seem to take great advantage of this resource. I haven't really figured out a connection between CPU and general performance though because in my case memory and disk were always the bottleneck.

Memory

... seems to be another underestimated bottleneck. For example, I noticed that replication can slow down to a state where it seems faster to copy-paste documents from one instance to another when CouchDB is unable to cache an entire b-tree in RAM.

We've been testing some things on a nifty high-memory 4XL AWS instance and during a compact operation, almost 90% of my ram (70 GB) was used by the OS to cache. And don't make my mistake and rely on (h)top to verify this, cat /proc/meminfo instead.

Caching

Caching is trivial with CouchDB.

e-tags

Each document and view responds with an Etag header — here is an example:

curl -I http://foo:[email protected]:5984/foobar/till-laptop_1273064525
HTTP/1.1 200 OK
Server: CouchDB/0.11.0a-1.0.7 (Erlang OTP/R13B)
Etag: "1-92b4825ffe4c61630d3bd9a456c7a9e0"
Date: Wed, 05 May 2010 13:20:12 GMT
Content-Type: text/plain;charset=utf-8
Content-Length: 1771
Cache-Control: must-revalidate

The Etag will only changes, when the data in the document change. Hence it's trivial to avoid hitting the database if you don't have to. The request above is a very lightweight HEAD request which only gathers the data and does not pull the entire document.

_changes

_changes represents a live-update feed of your CouchDB database. It's located at http://server/dbname/_changes.

Whenever a data changing operation is completed, _changes will reflect that, which makes it easy for a developer to stay on top to for example invalidate an application cache only when needed (and not like it's done usually when the cache expired).

Logging

Logrotate

First off, a lot of people run CouchDB from source which means that in 99% of all installs, the logrotation is not activated.

To fix this (on Ubuntu/Debian), do the following:

ln -s /usr/local/etc/logrotate.d/couch /etc/logrotate.d/couchdb

Make sure to familiarize yourself a little with logrotatet because depending on space and business of your installation, you should adjust the config a little to not run out of diskspace. If CouchDB is unable to log, it will crash.

Loglevel

In most cases it's more than alright to just run with a log level of error.

Add the following to your local.ini (in [log]): level = error

Log directory

Still running out of diskspace? Add the following to your local.ini (in [log]):

file = /path/to/more/diskspace/couch.log

... if you adjusted the above, you will need to correct the config for logrotate.d as well.

No logging?

Last but not least — if no logs are needed, just turn them off completely.

Fin

That's all kids.

Update, 2010-03-04: I just rolled a 0.0.2 release. In case you had 0.0.1 installed, just use pear upgrade-all to get it automatically. This release is trying to fix a random hang while reading documents from the source server.

I also opened a repository on Github.

---

As some may have guessed from a previous blog post we are currently running a test setup with CouchDB lounge. My current objective is to migrate our 200 million documents to it, and this is where I am essentially stuck this week.

No replication, no bulk docs

The lounge currently does not support replication (to it) or saving documents via bulk requests, so in essence migrating a lot of data into it is slow and tedious.

I have yet to figure out if there is a faster way (Maybe parallelization?), but DB_CouchDB_Replicator is the result of my current efforts.

I think I gave up on parallelization for now because it looked like hammering the lounge with a single worker was already enough, but generally I didn't have time to experiment much with it. It could have been my network connection too. Feedback in this area is very, very appreciated.

DB_CouchDB_Replicator

DB_CouchDB_Replicator is a small PHP script which takes two arguments, --source and --target. Both accept values in style of http://username:password@localhost:port/db and attempt to move all documents from source to target.

Since long running operations on the Internet are bound to fail, I also added a --resume switch, and while it's running it outputs a progress bar, so it should be fairly easy to resume. And you also get an idea of where it's currently at and how much more time it will eat up.

These switches may change, and I may add more — so keep an eye on --help. Also, keep in mind, that this is very alpha and I give no guarantees.

Installation

Installation is simple! :-)

apt-get install php-pear
pear config-set preferred_state alpha
pear channel-discover till.pearfarm.org
pear install till.pearfarm.org/DB_CouchDB_Replicator

Once installed, the replicator resides in /usr/local/bin or /usr/bin and is called couchdb-replicator.

Fin

The code is not yet on github, but will eventually end up there. All feedback is welcome!

One of our biggest issues with CouchDB is currently the lack of compaction of our database, and by lack of, I don't mean that CouchDB doesn't support it, I mean that we are unable to actually run it.

Compaction in a nutshell

Compaction in a nutshell is pretty cool.

As you know, CouchDB is not very space-efficient. For once, CouchDB saves revisions of all documents. Which means, whenever you update a document a new revision is saved. You can rollback any time, or expose it as a nifty feature in your application — regardless, those revisions are kept around until your database is compacted.

Think about it in terms of IMAP - emails are not deleted until you hit that magic "compact" button which 99% of all people who use IMAP don't know what it's for anyway.

Another thing is that whenever new documents are written to CouchDB and bulk mode is not used, it'll save them in a way which is not very efficient either. In terms of actual storage and indexing (so rumour has it).

Compaction woes

Since everything is simple with CouchDB, compaction is a simple process in CouchDB too. Yay!

When compaction is started, CouchDB will create a new database file where it stores the data in a very optimized way (I will not detail on this, go read a science book or google if you are really interested in this!). When the compaction process finished, CouchDB will exchange your old database file with the new database file.

The woes start with that e.g. when you have 700 GB uncompacted data, you will probably need another 400 GB for compaction to finish because it will create a second database file.

The second issue is that when you have constant writing on your database, the compaction process will actually never finish. It kind of sucks and for those people who aim to provide close to 100% availability, this is extremely painful to learn.

This is part II of my introductory series to move from MySQL a relational database (management) system to CouchDB. I will be using MySQL as an example. Part I of this series is available here.

Recap

In part I, I introduced CouchDB by explaining its basics. I continued by showing a simple request to create a document using curl (on the shell) and expanded how the same request could be written in PHP (using ext/curl) — or in HTTP_Request2 or with phpillow.

Part II will focus on the most basic operations and help you build a small wrapper for CouchDB. The code will be available on Github.

Getting data into CouchDB

As I explained before — CouchDB is all HTTP.

But for starters, we'll take a shortcut here and briefly talk about CouchDB's nifty administration frontend called futon. Think of Futon as a really easy to use phpMyAdmin. And by default futon is included in your CouchDB installation.

Features

Futon will allow you to virtually do anything you need:

• create a database
• create a document
• create a view
• build the view
• view ;-) (documents, views)
• update all of the above
• delete all of the above
• replicate with other CouchDB servers

Assuming the installation completed successfully, and CouchDB runs on 127.0.0.1:5984, the URL to your very own Futon is http://127.0.0.1:5984/_utils/. And it doesn't hurt to create a bookmark while you are at it.

Why GUI?

Purists will always argue that using a GUI is slower than for example hacking your requests from the shell.

That may be correct once you are a level 99 CouchDB super hero, but since we all start at level 1, Futon is a great way to interact with CouchDB to learn the basics. And besides, even level 99 CouchDB super heroes sometimes like to just click and see, and not type in crazy hacker commands to get it done.

I'll encourage everyone to check it out.

Read, write, update and delete.

Sometimes also referred to as CRUD (create, read, update, delete) — read, write, update and delete are the basics most web applications do.

Since most of you have done a "write a blog in X"-tutorial before — and I personally am tired of writing blogs in different languages or with different backends — let's use another example.

Think about a small guestbook application, it does all of the above — a fancy guest will even do update. For the sake of simplicity, I'll skip on the frontend in this example and we'll work on the backend and essentially create a small wrapper class for CouchDB.

Operations

By now — "CouchDB is all HTTP" — should sound all familiar. So in turn, all these CRUD operations in CouchDB translate to the following HTTP request methods:

• write/create - PUT or POST
• read - GET
• update - PUT or POST
• delete - DELETE

On write

Whenever you supply an ID of a new document along with the document, you should use PUT.

When you don't care about the document ID, use POST instead, and CouchDB will generate a unique ID for you.

This unique ID will not look like an autoincremented integer, but we should not cling to this concept anyway. Without diving into too advanced context now, but the auto_increment feature in MySQL is a little flawed in general and in a distributed context especially. More on this (maybe) in a later part of this series — in the mean-time, check out Joshua Schachter's post.

On update

By default, CouchDB keeps a revision ID of each document. To many this is a pretty cool feature — out of the box, so to speak. But there are two very important and fundamental things to be aware of.

1. CouchDB will keep previous revisions of a document around until you run compact on the database. Think of compact as a house keeping chore. It will wipe your database clean of previous revisions and even optimize the indices (in case you had a lot of data changing operations in the mean time). For CouchDB revisions are especially important in a distributed context (think replication — more on this later) and while it's cool to have them, they should not be used as a feature and be exposed to the user of your application.

2. In case we decide a document, we always have to provide the previous (or current) revision of the document. This sounds strange, but the reasons are simple — in case another update gets in between all we have to do is provide the necessary interfaces and workflows in our application to alert the user and avoid a possible conflict.

CouchDB and the HTTP standard

CouchDB's API adheres to the above in 99.999999999% of the time. And it only breaks the pattern once. The exception to the rule is that when you bulk request multiple documents, which is strictly speaking a GET operation CouchDB will allow you to post in this case.

The reason for this is that the length of GET request is limited (by RFC) and if we exceeded this by requesting too many document IDs, we would hit a hard limit. To get around this, the operation is POST — but more on this later.

Requirements

For the following examples we assume a working CouchDB installation and a database called guestbook. No admins are set up — we can read and write without permission.

For simplicty, we imagine a form with the following fields:

• author
• entry

... and in addition to those two keys that may be populated by the user we add:

• type (always: guestbook)
• added (a date of some kind)

... the last two are not absolutely necessary, but will come handy in future iterations of this tutorial.

Also, on the tech side, we need a webserver with PHP and HTTP_Request2 (pear install HTTP_Request2-alpha) in your include_path. :-)