NGINX and NGINX Plus are commonly used as web content caches, ranging from individual websites to some of the largest content delivery networks (CDNs) in the world, such as MaxCDN and CloudFlare.

Microcaching is an effective method for accelerating the delivery of dynamic, non‑personalized content by caching it for very short periods of time. In this article, we’ll see how to accelerate a WordPress‑based application by up to 400x using the microcaching technique.

Why Cache Content?

The benefits of caching are two‑fold: caching improves web performance by delivering content more quickly, and it reduces the load on the origin servers. The effectiveness of caching depends on the cacheability of the content. For how long can we store the content, how do we check for updates, and how many users can we send the same cached content to?

Caching static content, such as images, JavaScript and CSS files, and web content that rarely changes is a relatively straightforward process. Cache updates can be handled by regular timeouts, conditional gets and, if necessary, cache‑busting techniques to change the URL of the referenced object.

Caching personalized content (that is, content customized for each user by the server application) is generally not possible, because the server’s response to each request for the same resource is different. Techniques such as server side includes (SSI) and edge side includes (ESI) can help to assemble the page, but they are complex to implement and do not necessarily improve performance.

In the middle is an interesting candidate for caching: dynamic content that can change unpredictably, but is not personalized for each user (or is personalized using JavaScript on the client device). This content can be expensive to generate, and serving an outdated version can cause a range of problems.

Examples of dynamic content suitable for caching include:

The front page of a busy news or blog site, where new articles are posted every few seconds

An RSS feed of recent information

The status page for a continuous integration (CI) or build platform

An inventory, status, or fundraising counter

Lottery results

Calendar data

Personalized dynamic content is generated on the client device, such as advertising content or data (‘Hello, your name’) that is calculated using cookie data

Microcaching of Dynamic Content

Microcaching is a caching technique whereby content is cached for a very short period of time, perhaps as little as 1 second. This effectively means that updates to the site are delayed by no more than a second, which in many cases is perfectly acceptable.

Does this kind of very brief caching make an appreciable difference in website performance? Let’s try it out!

Our Test Application

For this test, we’ll use a standard WordPress install, and populate it with some sample content.

Even with basic content, it’s clear that the WordPress server has a performance issue – it served only 5.53 requests per second when benchmarked with ab:

[email protected]:~# >ab -c 10 -t 30 -k http://nginx-server/ Requests per second: 5.53 [#/sec] (mean) Time per request: 1809.260 [ms] (mean) Time per request: 180.926 [ms] (mean, across all concurrent requests) Transfer rate: 319.74 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 0 0 0.3 0 3 Processing: 1430 1735 259.4 1580 2228 Waiting: 537 683 119.7 624 980 Total: 1430 1735 259.4 1580 2228

During the tests, vmstat indicated that the bottleneck was due to the CPU cost of generating the page using PHP (the values in the us column under cpu range from 96 to 98):

[email protected]:/var/www/html# vmstat 3 procs -----------memory---------- ---swap-- -----io---- -system-- ------cpu----- r b swpd free buff cache si so bi bo in cs us sy id wa st 10 0 0 136076 44944 585920 0 0 0 0 476 1665 96 4 0 0 0 10 0 0 140112 44952 585924 0 0 0 4 506 1773 98 2 0 0 0 10 0 0 136208 44952 585924 0 0 0 0 576 2057 97 3 0 0 0

The top utility showed the CPU was being consumed by 10 Apache httpd processes which were executing the PHP interpreters.

This setup is a problem – it limits the capacity of the website to no more than five requests per second, it makes it trivially easy to mount a denial‑of‑service attack, and addressing the problem by adding more CPU could add $1,000s to the annual hosting costs.

Simple Microcaching with NGINX

Accelerating this service with NGINX is a two‑step process.

Step 1: Proxy Through NGINX

Install NGINX or NGINX Plus on the WordPress server and configure it to receive incoming traffic and forward it internally to the WordPress server:

The NGINX proxy configuration is relatively straightforward:

server { listen external-ip:80; # External IP address location / { proxy_http_version 1.1; # Always upgrade to HTTP/1.1 proxy_set_header Connection ""; # Enable keepalives proxy_set_header Accept-Encoding ""; # Optimize encoding proxy_pass http://wordpress-upstreams; } status_zone wordpress; # NGINX Plus status monitoring } upstream wordpress-upstreams { zone wordpress 128k; keepalive 20; # Keepalive pool to upstream server localhost:80; }

We also modify the Apache configuration (listen ports and virtual servers) so that Apache is bound to localhost:80 only.

You might think that adding an additional proxying hop would have a negative effect on performance, but in fact, the performance change is negligible:

[email protected]:~# ab -c 10 -t 30 -k http://nginx-server/ Requests per second: 5.63 [#/sec] (mean) Time per request: 1774.708 [ms] (mean) Time per request: 177.471 [ms] (mean, across all concurrent requests) Transfer rate: 324.44 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 0 0 0.2 0 1 Processing: 1423 1709 341.3 1532 2794 Waiting: 554 703 165.0 608 1165 Total: 1423 1709 341.4 1532 2794

On a busier server (handling more concurrent requests), just the keepalive optimizations delivered by NGINX will bring a significant performance boost.

Step 2: Enable Short‑Term Caching

With addition of just two directives to our server configuration, NGINX or NGINX Plus caches all cacheable responses. Responses with a 200 OK status code are cached for just 1 second.

proxy_cache_path /tmp/cache keys_zone=cache:10m levels=1:2 inactive=600s max_size=100m; server { proxy_cache cache; proxy_cache_valid 200 1s; # ... }

When we rerun the benchmark test, we see a significant performance boost:

[email protected]:~# ab -c 10 -t 30 -k http://nginx-server/ Complete requests: 18022 Requests per second: 600.73 [#/sec] (mean) Time per request: 16.646 [ms] (mean) Time per request: 1.665 [ms] (mean, across all concurrent requests) Transfer rate: 33374.96 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 0 1 0.5 1 10 Processing: 0 16 141.5 3 2119 Waiting: 0 6 54.6 1 818 Total: 1 17 141.5 4 2121

That’s a 120‑fold performance improvement, from 5 requests per second to 600; this sounds great, but there’s a problem.

The caching is working perfectly, and we can verify that content is refreshed every second (so it’s never out of date), but there’s a something unexpected going on. You’ll observe that there’s a large standard deviation (141.5 ms) in the processing time. CPU utilization is still 100% (as measured by vmstat ), and top shows 10 active httpd processes.

We can get a further hint from the NGINX Plus live activity monitoring dashboard. Before the test:

After the test:

The dashboard reports that NGINX handled 18032 requests during the test (the 18022 reported by ab , and 10 requests that were outstanding when the benchmark ended at the 30‑second mark). However, NGINX forwarded 150 requests to the upstream, which is much more than we would expect if we’re caching the content for 1 second during the 30‑second test.

What’s going on? Why the high CPU utilization and the larger‑than‑expected number of cache refreshes?

It’s because each time a cached entry expires, NGINX stops using it. NGINX forwards all requests to the upstream WordPress server until it receives a response and can repopulate the cache with fresh content.

This causes a frequent spike of up to 10 requests to the WordPress servers. These requests consume CPU and have a much higher latency than requests served from the cache, explaining the high standard deviation in the results.

Optimized Microcaching with NGINX

The strategy we want is clear: we want to forward as few requests to the upstream origin server as are required to keep the cache up to date. While the cached content is being updated, we’re happy to serve stale responses (1 or 2 seconds old) from the cache. To achieve this, we add two directives:

proxy_cache_lock – Restricts the number of concurrent attempts to populate the cache, so that when a cached entry is being created, further requests for that resource are queued up in NGINX

– Restricts the number of concurrent attempts to populate the cache, so that when a cached entry is being created, further requests for that resource are queued up in NGINX proxy_cache_use_stale – Configures NGINX to serve stale (currently cached) content while a cached entry is being updated

Together with the caching directives we added previously, this give us the following server configuration:

server { proxy_cache one; proxy_cache_lock on; proxy_cache_valid 200 1s; proxy_cache_use_stale updating; # ... }

The change in benchmarking results is significant. The number of requests per second jumps from 600 to nearly 2200:

[email protected]:~# ab -c 10 -t 30 -n 100000 -k http://nginx-server/ Concurrency Level: 10 Time taken for tests: 30.001 seconds Complete requests: 65553 Failed requests: 0 Keep-Alive requests: 0 Total transferred: 3728905623 bytes HTML transferred: 3712974057 bytes Requests per second: 2185.03 [#/sec] (mean) Time per request: 4.577 [ms] (mean) Time per request: 0.458 [ms] (mean, across all concurrent requests) Transfer rate: 121379.72 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 0 1 0.3 1 5 Processing: 1 4 8.1 3 661 Waiting: 0 1 2.6 1 250 Total: 1 5 8.1 4 661

CPU utilization is much lower (note the idle time in the id column under cpu ):

[email protected]:/var/www/html# vmstat 3 procs -----------memory---------- ---swap-- -----io---- -system--- ------cpu----- r b swpd free buff cache si so bi bo in cs us sy id wa st 1 0 0 106512 53192 641116 0 0 0 37 11016 3727 19 45 36 0 0 1 0 0 105832 53192 641808 0 0 0 68 17116 3521 13 56 31 0 0 1 0 0 104624 53192 643132 0 0 0 64 14120 4487 15 51 33 0 0

The transfer rate (121379.72 Kbytes/sec, or 121 MBps) equates to 0.97 Gbps, so the test is network bound. With an average CPU utilization of 66%, the peak performance for this server would be approximately 2185/0.66 = 3300 requests/second:

Also observe the consistent response times reported by ab (the standard deviation is only 8.1 ms), and the small number of requests (16) forwarded to the upstream server during the 30‑second test reported by the dashboard:

Why just 16 requests? We know that the cache times out after 1 second, and that updates can take up to 0.661 seconds (from ab results), so we can predict that updates will occur no more frequently than every 1.66 seconds. Over a 30‑second period, you’d expect no more than 18 (30/1.66) requests.

Find Out More

This simple demonstration shows the potential benefit of caching dynamic content for very short periods of time, and the usefulness of NGINX Plus’ live activity monitoring data when tuning and diagnosing cache configurations. If you want to use microcaching in production, we recommend that you create and test a more sophisticated caching policy that both microcaches dynamic content and caches static content for longer periods of time.

NGINX Plus also includes a cache purge feature that can be used to remove specified content immediately from the NGINX cache. This capability can be employed programmatically if you want to cache content for longer periods of time, but need to update it immediately when you change the origin content.

For more information, check out the following resources: