Effect of connection pooling on node.js performance

Node.js is popularly known for its asynchronous, non-blocking and event-driven I/O model and the scalability it can achieve in executing I/O bound operations.

However, a developer must keep in mind that there are some things which can severely limit the scalability of the Node.js application. I have earlier already covered How DNS resolution is a blocking call in node and must be handled to achieve scalability

In this post, I am going to cover connection pooling. I have observed that connection pooling is a critical engineering decision which is easily ignored while developing node.js applications. While interacting with any external resource such as MySQL, PostgreSQL, Redis or MongoDB, each of it requires a connection pool for any sizeable node.js application.

To demonstrate this, I am going to compare two programs, one without pooling and one with pooling and a simple database query.

I have this simple function which makes a call to database.

function callback (){ process . exit (); } function hitQuery ( callback ) { var user_query = " select count(*) from user u, access_code uac, user_location_info uli where u.id = uac.user_id and u.id = uli.user_id " connection . query ( user_query , function ( err , rows , fields ) { if ( err ) throw err ; if ( rows . length == 0 ) { console . log ( " No device token found for user: " + 16182 ); callback ( null , null ); } else { var deviceToken = rows [ 0 ][ ' device_token ' ]; callback ( null , rows [ 0 ]); } }); } hitQuery ( callback );

If I execute this and time it, it takes an average of 1.5 - 2 seconds

$ time node paralleltest.js real 0m1.756s user 0m0.159s sys 0m0.017s

Lets, now run multiple of such queries in series first. I slightly change my program to this version:

async . series ([ function ( callback ) { hitQuery ( callback ); }, function ( callback ) { hitQuery ( callback ); }, function ( callback ) { hitQuery ( callback ); }, function ( callback ) { hitQuery ( callback ); }, function ( callback ) { hitQuery ( callback ); } ], function done ( err , results ) { console . log ( results ); process . exit () });

When I execute this version where I am making 5 calls in series, I get an average of 8 - 9 seconds

$ time node paralleltest.js real 0m8.579s user 0m0.178s sys 0m0.019s

Lets make this to parallel now, common sense says that the wall clock time should be much faster in case of parallel.

async . parallel ([ function ( callback ) { hitQuery ( callback ); }, function ( callback ) { hitQuery ( callback ); }, function ( callback ) { hitQuery ( callback ); }, function ( callback ) { hitQuery ( callback ); }, function ( callback ) { hitQuery ( callback ); } ], function done ( err , results ) { console . log ( results ); process . exit () });

Now, If I time this verison, where I am making 5 calls in parallel.

$ time node paralleltest.js real 0m8.168s user 0m0.165s sys 0m0.018s

I still get the almost same wall time clock. If you closely observe the time output, the bulk of the time is not even spent in our program. Bulk of the time is actually spent in waiting to get the connection back from mysql, since we are re-using the single connection again and again. Thus, there is literally no performance gain inspite of making the calls in “parallel”

To fix this, I am going to change the program to use a connection pool. I am creating a simple pool as follows:

var pool = mysql . createPool ({ connectionLimit : 100 , //important host : ' 127.0.0.1 ' , user : ' *** ' , password : ' *** ' , database : ' user ' , debug : false });

And change our function to use the connection pool:

function hitQuery ( callback ) { var user_query = " select count(*) from user u, access_code uac, user_location_info uli where u.id = uac.user_id and u.id = uli.user_id " pool . getConnection ( function ( err , connection ) { if ( err ) { connection . release (); res . json ({ " code " : 100 , " status " : " Error in connection database " }); return ; } connection . query ( user_query , function ( err , rows , fields ) { if ( err ) throw err ; if ( rows . length == 0 ) { console . log ( " No device token found for user: " + 16182 ); callback ( null , null ); } else { callback ( null , rows [ 0 ]); } }); }); }

Again, If I time the single execution time here. I get

$ time node paralleltest1.js real 0m1.763s user 0m0.163s sys 0m0.020s

Not much has changed, we got 1.756 while using without connection pooling. The benefit of connection pooling is only when we run multiple requests together. So now, lets execute in series of 5 queries as done earlier.

$ time node paralleltest1.js real 0m8.192s user 0m0.182s sys 0m0.019s

Again, no real benefit. What is happening here is that the second query is executed only after the first query is executed. Hence, this time is perfectly fine. Its just that bulk of time is wasted.

Let’s move this to 5 parallel queries.

$ time node paralleltest1.js real 0m2.311s user 0m0.175s sys 0m0.019s