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Re-architecting
the Government
with Lua and Hadoop
Nick Muerdter @nickblah
National Renewable Energy Laboratory
Gluecon
2016-05-26
★		★		★
★		★		★
Chapter 1
★		★		★
Origins
2010:
APIs!
API Management
2013:
💬
with other
agencies
Open Sourcing
API Umbrella
api.data.gov
Chapter 2
★		★		★
A Journey from
NodeJS to Lua
Custom sauce
(was NodeJS)
Internet
 nginx
API
nginx
Redis
NodeJS
NodeJS
NodeJS
API
…
Why Change?
•  Operational simplicity
•  Efficiency & speed
•  Benefits of hindsight
OpenResty
(nginx + LuaJIT)
Fun & useful things
about OpenResty
1. Async/event-driven
(but without
callbacks/promises)
NodeJS Callbacks
var	pg	=	require('pg');	
	
pg.connect({	database:	'mydb'	},	function(err,	client,	done)	{	
		client.query('INSERT	INTO	people(name)	VALUES('Jane')',	function(err,	res)	{	
				client.query('SELECT	first_name	FROM	people',	function(err,	res)	{	
						done();	
				});	
		});	
});
Lua
local	pgmoon	=	require("pgmoon")	
	
local	pg	=	pgmoon.new({	database	=	"mydb"	})	
pg:connect()	
	
local	res,	err	=	pg:query("INSERT	INTO	people(name)	VALUES('Jane')")	
local	res,	err	=	pg:query("SELECT	name	FROM	people")
2. Shared memory
dictionaries
3. The nginx we know
and love
4. It’s fast!
How to pull this off?
API Compatibility
💖
Integration Tests
Internet
 nginx
API
nginx
Redis
NodeJS
NodeJS
NodeJS
API
…
Internet
API
nginx + Lua
 API
…
Proxy Overhead
•  NodeJS: ~10ms
•  OpenResty: ~1ms
•  50% less CPU use
*Benchmarking caveats…
It may not all be 🍑s
Chapter 3
★		★		★
Growing with
Hadoop, Kylin, and SQL
Log Data & Analytics
request_at:	2016-05-25T20:02:53.752Z	
request_method:	GET	
request_scheme:	https	
request_host:	developer.nrel.gov	
request_path:	/alt-fuel-stations/v1.json	
request_query:	fuel_type=ELEC&limit=10	
user_id:	ad2d94b6-e0f8-4e26-b1a6-1bc6b12f3d76	
request_user_agent:	curl/7.33.0	
request_ip_country:	US	
request_ip_region:	CO	
request_ip_city:	Golden	
response_status:	200	
response_time:	40	
…	
…
ElasticSearch
Apache Kylin
Data Cubes
•  Pick the dimensions (columns) you
want to aggregate on.
•  Define measurements (counts,
sums, etc).
•  For every possible combination of
dimensions, measurements are pre-
aggregated.
Dimensions:	host	
Measurements:	count	
Raw	Rows:	150	
	
host																			count	
developer.nrel.gov					50	
api.nasa.gov											100
SELECT	host,	COUNT(*)	
FROM	logs	
GROUP	BY	host;
Dimensions:	host,	path	
Measurements:	count	
Raw	Rows:	150	
	
host																			path									count	
developer.nrel.gov					/alt-fuel				30	
developer.nrel.gov					/solar							20	
api.nasa.gov											/photos						40	
api.nasa.gov											/asteroids			50	
api.nasa.gov											/mars								10
SELECT	path,	COUNT(*)	
FROM	logs	
WHERE	host	=	'developer.nrel.gov'	
GROUP	BY	path;
SELECT	path,	COUNT(*)	
FROM	logs	
WHERE	user_agent	=	'curl'	
GROUP	BY	path;
ANSI SQL is a lovely thing
•  If Kylin can’t answer a query from its
pre-aggregations, we can fallback
to slower Hadoop queries against
the raw dataset.
•  Presto gives an ANSI SQL interface
against the raw dataset.
But I could
pre-aggregate
myself…
Query Performance
•  ElasticSearch: 5s, 10s, 60s, 2m…
•  Kylin: ~1s
*Benchmarking caveats…
It’s not all 🍑s
★		★		★
Epilogue
★		★		★
Simplifying your stack
Scaling efficiently
Find the right tools
Perhaps some other
things for your toolbelt:
OpenResty/Lua
Kylin
API Umbrella
Sleeping Material
•  Lua
– https://github.com/NREL/api-umbrella/issues/86
– https://github.com/NREL/api-umbrella/pull/183
•  Kylin
– https://github.com/18F/api.data.gov/issues/235
★		★		★
Questions?
★		★		★
Nick Muerdter
nick.muerdter@nrel.gov
@nickblah

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