Monthly Archives: September 2010

For about a year, I have been using the NOSQL datastore redis, in various web-serving environments, as a very fast backend to store and retrieve key-value data and data that best fits in lists, sets, and hash-tables. In addition to redis, my backend also employed mysql, because some data fits much better in a relational table. Getting certain types of data to fit into redis data objects would have added to the complexity of the system and in some cases: it’s simply not doable. BUT, I hated having 2 data-stores, especially when one (mysql) is fundamentally slower, this created a misbalance in how my code was architected. The Mysql calls can take orders of magnitude longer to execute, which is exacerbated when traffic surges. So I wrote Redisql which is an extension of redis that also supports a large subset of SQL. The Idea was to have a single roof to house both relational data and redis data and both types of data would exhibit similar lookup/insert latencies under similar concurrency levels, i.e. a balanced backend.

Redisql supports all redis data types and functionality (as it’s an extension of redis) and it also supports SQL SELECT/INSERT/UPDATE/DELETE (including joins, range-queries, multiple indices, etc…) -> lots of SQL, short of stuff like nested joins and Datawarehousing functionality (e.g. FOREIGN KEY CONSTRAINTS). So using a Redisql library (in your environment’s native language), you can either call redis operations on redis data objects or SQL operations on relational tables, its all in one server accessed from one library. Redisql morph commands convert relational tables (including range query and join results) into sets of redis data objects. They can also convert the results of redis commands on redis data objects into relational tables. Denormalization from relation tables to sets of redis hash-tables is possible, as is normalization from sets of redis hash-tables (or sets of redis keys) into relational tables. Data can be reordered and shuffled into the data structure (relational table, list, set, hash-table, OR ordered-set) that best fits your use cases, and the archiving of redis data objects into relational tables is made possible.

Not only is all the data under a single data roof in Redisql, but the lookup/insert speeds are uniform, you can predict the speed of a SET, an INSERT, an LPOP, a SELECT range query … so application code runs w/o kinks (no unexpected bizarro waits due to mysql table locks -> that lock up an apache thread -> that decrease the performance of a single machine -> which creates an imbalance in the cluster).

Uniform data access patterns between front-end and back-end can fundamentally change how application code behaves. On a 3.0Ghz CPU core, Redis SET/GET run at 110K/s and Redisql INSERT/SELECT run at 95K/s, both w/ sub millisecond mean-latencies, so all of a sudden the application server can fetch data from the datastore w/ truly minimal delay. The oh-so-common bottleneck: “I/O between app-server and datastore” is cut to a bare minimum, which can even push the bottleneck back into the app-servers, and that’s great news as app-servers are dead simple (e.g. add server) to scale horizontally. Redisql is an event-driven non-blocking asynchronous-I/O in-memory database, which i have dubbed an Evented Relational Database, for brevity’s sake.

During the development of Redisql, it became evident that optimizing the number of bytes a row occupied was an incredibly important metric, as Redisql is an In-Memory database (w/ disk persistence snapshotting). Unlike redis, Redisql can function if you go into swap space, but this should be done w/ extreme care. Redisql has lots of memory optimisations, it has been written from the ground up to allow you to put as much data as is possible into your machine’s RAM. Relational table per-row overhead is minimal and TEXT columns are stored in compressed form, when possible (using algorithms w/ negligible performance hits). Analogous to providing predictable request latencies at high concurrency levels, Redisql gives predictable memory usage overhead for data storage and provides detailed per-table, per-index memory usage via the SQL DESC command, as well as per row memory usage via the “INSERT … RETURN SIZE” command. The predictability of Redisql, which translates into tweakability for the seasoned programmer, changes the traditional programming landscape where the datastore is slower than the app-server.

Redisql is architected to handle the c10K problem, so it is world class in terms of networking speed AND all of Redisql’s data is in RAM, so there are no hard disk seeks to engineer around, you get all your data in a predictably FAST manner AND you can pack a lot of data into RAM as Redisql aggressively minimizes memory usage AND Redisql combines SQL and NOSQL under one roof, unifying them w/ commands to morph data betwixt them …. the sum of these parts, when integrated correctly w/ a fast app-server architecture is unbeatable as a dynamic web page serving platform with low latency at high concurrency.

The goal of Redisql is to be the complete datastore solution for applications that require the fastest data lookups/inserts possible. Pairing Redisql w/ an event driven language like Node.js, Ruby Eventmachine, or Twisted Python, should yield a dynamic web page serving platform capable of unheard of low latency at high concurrency, which when paired w/ intelligent client side programming, could process user events in the browser quickly enough to finally realize the browser as an applications platform.

Redisql: the polyglot that speaks SQL and redis, was written to be the Evented Relational Database, the missing piece in the 100% event driven architecture spanning from browser to app-server to database-server and back.


I live in silicon valley, and I am forever going to San Fran or San Jose on the caltrain. The caltrain is clean, but its too damn slow.

Then I thought about why its slow. It makes too many stops. Every stop it needs to slow down, stop, people board, it speeds up again. The overhead involved in the stops take up all the time, not the actual travelling.

We all want more stops, so statistically the caltrain is closer to our front door, but its the stops that are slowing the caltrain down, the express trains get me to San Fran 2X quicker, but they serve maybe 1/5 the stops, so they are not a solution either.

So this post (which is just for the record, cause no doubt it already exists in Japan) wants to present an idea to abstract out the stops.

Caltrain is not the best or worst example, but the simplest example is to imagine a subway line running from east to west in a major city, spanning 50 miles and having 25 stops. If the train can start at the east side and travel at 50 miles an hour w/o stop, you can traverse the city in an hour regardless of traffic, its optimal for moving people through a city.

So the tricky part is when someone gets on at mile 20 and wants to get off at mile 22 (which would be city center and the most common usage).

So you have little mini trains at each stop. You board them, and their doors close one minute before the big train (running at constant 50mph) passes the station. Once the doors of the mini train close the mini train starts to accelerate on a mini track next to the main track. The timing is such that when the mini-train reaches 50mph the main train is right next to it, and they dock in motion, allowing people to board (from mini-train to big-train) and if the next stop is close, people can disembark from the big-train to the mini-train which will then close its door and coast to the next stop.

The whole point is to have the big train move at a constant speed, never stopping. Take the stops out of the equation and mass transit would be FAST.

Just an idea, it is a hot spell in Silicon Valley, hard to sleep, my mind wanders 🙂

Update: I ran this by my friend faisal, and he has an idea to treat the train cars like lego blocks, opening up all sorts of new strategies, then i felt inspired and drew a pathetic one minute sketch on my tablet …

Web pages are quickly becoming instantly reactive. Typing into an HTML form does more than simply display the typed characters, under the covers the browser sends an Ajax request to a server that replies w/ instructions to modify the current webpage … the web page reacts to the keystroke “instantly”. When the client-server I/O follow this model, web pages served via a browser can yield a user experience akin to that of a desktop application.

The best prevalent example is Google’s Instant Search. The results of your search are shown AS you type them. If you search for “taco bell”, Google will display a different result set for each new letter typed. In the “taco bell” example, Google receives requests for “t”, “ta”, “tac”, “taco”, “taco “, “taco b”, “taco be”, “taco bel”, and “taco bell” (a total of 9 requests instead of one). Google goes on to state: it takes 300 milliseconds to type a letter and (one tenth of that) 30 milliseconds to glance at another part of the page. So the 9 result sets need to be returned and rendered quickly to be “Instant”. This represents an increase in request concurrency (9 in the time span where one used to be made) and a decrease in latency (“instant” as opposed to the time needed to load a new web page).

History has shown that once Google adds something to its search engine, everyone follows suit and extends the concept beyond the search engine. It is very probably that in the near future any button or any form on any web page will need to be “instant” to yield a competitive user experience.

Recent client side advances to give “instant” results include HTML5 Websockets and Google’s
v8 JavaScript engine (the latter has been optimized to render Ajax requests VERY quickly).

Server-side serving “instant” requests means serving more requests (which are usually pretty small) and serving them QUICKLY. Each user will be making loads of smallish requests and need them returned ASAP. The server-side challenge boils down to achieving Low Latency at High Concurrency, which is something of an engineering paradox. Recent advances to tackle what is often referred to as the c10K problem (web servers able to handle ten thousand clients simultaneously) include nginx and node.js.

Both nginx and node.js differ from traditional web servers by being single threaded, event driven, non-blocking, and asynchronous. They are coded to the reality that the network interface is the bottleneck in serving web pages. Under highly concurrent load they greatly out perform any web server built on a multi-threaded architecture. The multi-threaded architecture stress tested under high concurrency will bottleneck on context switches whereas the single threaded event driven architecture cannot context switch, so it bottlenecks on other things under MUCH (100x) higher concurrency.

A common mistake in web server benchmarking is to test the server under low concurrency. In the real world, this tests the case where a small set of users is pounding your web server w/ requests. The only users that match these benchmarks’ characteristics are web-spiders and denial-of-service attacks. Consequently such benchmarks’ results are not that meaningful. In the real world, web traffic comes from many concurrent users and likes to come in bursts. So concurrency (even in the non-“Instant” case) is vital in any web server benchmark.

Nginx serves only static webpages, but Node.js is capable of building dynamic web pages employing JavaScript SERVER side. Node.js is non blocking by nature and if it matures properly could be the platform of choice for “Instant” dynamic web pages.

There is, unfortunately, one piece missing in the full chain to build a “instant” dynamic web page platform: the database. All relational databases to date are multi-threaded. In the use case where many smallish requests are made from the browser (to provide the “Instant” experience), even if the web server is capable of delivering low latency web pages at high concurrency, the database will bottleneck, and the flow through the chain will develops kink and no progress over the multi threaded web server architecture will be evident.

The NOSQL movement has developed some event driven data stores. Of note is Redis, an event driven data store providing list, set, and hash table primitives. It is VERY fast. Pairing Redis w/ Node.js to serve dynamic web pages and using nginx to serve static web pages is an optimal platform for bleeding edge “Instant” web serving platforms as the entire chain has the same event driven architecture … i.e. no kinks.

But NOSQL is new and foreign to most, so the switch towards “Instant” web serving platforms is currently reserved only for the gutsy early adapter. What is missing is an event driven relational database that speaks SQL. When an Evented Relational Database is created, anyone w/ JavaScript and SQL knowledge will be able to create “Instant” web serving platforms (JavaScript on the server-side also provides a single language for client side and server side development).

Users demanding an “Instant” web experience will be the catalyst to move the event driven programming style into the mainstream. The means to accomplish the “Instant” web experience on the large scale can only be realised by simplifying the process of writing event driven programs. Javascript and SQL employed as an end to end solution accomplishes this: both are widely known and have low barriers to entry.

The “Instant” web requires Low Latency at High Concurrency and the tools to realize this are Ajax/Websockets, an Evented Web Server (Nginx, Node.js), and an Evented Relational Database … I will write a later post on the Evented Relational Database.

I am a big fan of the NOSQL datastore redis (

Redis is an astonishly fast datastore, but what people often overlook is that it performs amazingly well under concurrent loads.

I will write a later post on why being able to deliver low latency requests at high concurrency levels will be the defining metric for databases powering the next generation of webservers.

For now, here are some rather ugly bug accurate graphs

Redis 2.0.0 latency tests from 1-64K concurrent connections

Standard Hardware: Standard NIC (A780GM-A motherboard integrated), Phenom X4 CPU @3.0GHz, 8GB RAM PC3200.
Two machines connected via a standard 1GigE Netgear Switch.
OS: Ubuntu 9.10 64bit.
Tests are run using a single core for the client and a single core (on the other machine) for the server.
This is how the server performs in terms of requests per second as concurrency goes up. This is such an impressive graph:


COMMENTS: below 5 is bad, 10 is already max, then a beautifully slow and smooth degradation to 64K concurrent connections

Reqs/s vs Concurrency


NEXT: Latency will be analyzed as concurrency varies. Latency is measured in milliseconds, 100K requests are made, and the graphs will show the percentage of those 100K requests took how long

NOTES: For the following graphs, the axis are all the same:

  • Z-axis (to the right) is PERCENTAGE
  • X-axis (to the left) is LEVEL OF CONCURRENCY
  • Y-axis is MILLISECONDS
  • THE BIG PICTURE 1-64K concurrent connections

    COMMENTS: quick to see that even at high concurrency, if you are below 90%, you are ok



    COMMENTS: lots of L’s … there are always gonna be some anomolies, so this is very strong

    Concurrency 1 to 10K

    THE BULK: 0-90% latency

    COMMENTS: its all fantastic until maybe 30K, which is absurdly high. Also even in the very high ranges it is smooth in both the X and Z directions until 80%.

    less than 90% latency

    VERY GOOD: 90-99% latencies

    COMMENTS: everything is kosher until 30K AND under 99%

    90-99% Latency

    THE END: 99% latencies

    COMMENTS: Remember this is a closeup of the worst of the worst. The good news is we see the same L patterns at this resolution, until 30K. The 0-5K range is cluttered, so on to the next graph.

    99th Percentile

    THE END: Concurrency 1 to 1000 – 99% latencies

    COMMENTS: This is the SLA graph. @400 and approx: 99.5%, there is a hop. This holds true until 900. This latency jump is always from below 20 direct to about 220, and over the whole data set this hop hovers around the 99th percentile and mostly to pretty far right of it.

    1-100 concurrency 99th percentile

    FINAL COMMENTS: I am presenting this as neutrally as possible. I am pretty impressed with these latencies, and the thing that strikes me about the graphs is their smoothness in all directions. This smoothness to me is as important as the numbers, it means the software is predictable.


    1. The data for these graphs is in this directory:
    2. Two files were needed to create these benchmarks: redis-benchmark.c and
    3. additonal unix commands need to be done to allow greater than 28K concurrency, like: “ulimit -n 90000” , “echo 1024 65000 > /proc/sys/net/ipv4/ip_local_port_range”, “echo 1 > /proc/sys/net/ipv4/tcp_tw_recycle”, “”echo 1 > /proc/sys/net/ipv4/tcp_tw_reuse”
    4. in reds code, the following change must be made “ae.h:#define AE_SETSIZE (1024*10) … redefine to 1024*64” for concurrency higher than 28K

    I used gnu-plot for these graphs, and this is the way to look at them, cause you can rotate the axis in 3 dimensions
    Here are the instructions:
    1.) gnuplot> splot “FILE” u 1:2:3

    I have always not done a blog on purpose.

    I have always been of the opinion that while my opinions are indeed unique and interesting, I really only want to share the absolute best of them and blogs are notorious for people just going on about nothing. So I will do my best to keep the content intelligent and interesting, which will take time, which is the second reason I never did a blog, they take time.

    And with that, its started.