vector-ranking-logs

README.md

Visualization of 900+ log files in one page

Hover mouse on boxes (e.g. versions, histogram) to view interactively. Something I put together in 7hrs, not well documented at all, hence a little explanation:

Problem

Search for top K most similar vectors from N of those in total with P threads/processes.

Structure

{(Version, N, P, K) => (9 performance metrics)}

Graphs

Left column

• Relative Speedup v. P (Show linearity of speedup of each version)
• P-K 2D histogram (x-axis:P y-axis:K; color scale:[Red to Green]=>[More time to less time])
• P-t K-t N-t trending

Right column

• Timeline
• Other metrics

analysis.js

var $ = $ || {}, d3 = d3 || {};
var analysis, dimensions;

function s2ia(s){
    return Array.from(s).map(function (x) { return parseInt(x, 10); });
}

function get_analysis(v, n, p, k, t){
	var key = [v,n,k,p].join('/');
	return (key in analysis)?analysis[key][t]:NaN;
}

function validate_dimensions(dim, dict){
    var r = [], total=0, miss=0;
    for(var v of dim.V)for(var n of dim.N)for(var p of dim.P)for(var k of dim.K){
        var key = [v,n,k,p].join('/');
        if(!(key in dict)) { r.push(key); ++miss; }
        ++total;
    }
    return {total: total, coverage: 1-(miss/total), result: r};
}

function get_speedup_v_p(){
	const n=8400, k=500;
	var r=[];
	for(var v of dimensions.V) {
		var base_t = get_analysis(v,n,1,k,'total_ms');
		var data_points = new Array();
		r.push(data_points);
		data_points.push({x:dimensions.P[0], y:1});
		for(var p of dimensions.P) if(p>1) {
			var _y = base_t/get_analysis(v,n,p,k,'total_ms');
			if(isNaN(_y)) data_points.push(null);
			else data_points.push({x:p, y:_y});
		}
	}
	return r;
}

$.getJSON("analysis.json" , function(r){
	analysis = r;
	var r_key = /(.+)\/(\d+)\/(\d+)\/(\d+)/, vnpk = [new Set, new Set, new Set, new Set];
	for(var i in analysis){
		var m = r_key.exec(i);
		for(var k=0;k<4;++k) vnpk[k].add(m[k+1]);
	}
	dimensions = {
		V: Array.from(vnpk[0]).sort(),
		N: s2ia(vnpk[1]).sort((a, b) => (a - b)),
		P: s2ia(vnpk[3]).sort((a, b) => (a - b)),
		K: s2ia(vnpk[2]).sort((a, b) => (a - b)),
	};
	console.log(dimensions);
	console.log(validate_dimensions(dimensions, analysis));
	
	$(render_plots);
});

function render_plots() {
	var view = { width: 1320, height: 750 };
	
	var svg = d3.select("body").append("svg")
		.attr("width", view.width)
		.attr("height", view.height);
	var h_hist, h_plot_pt, h_plot_kt, h_plot_nt, h_timeline, h_report; // plot handles
		    
	var color_scale_v = d3.scaleLinear()
	    .domain([0, 9])
	    .range(["steelblue", "red"])
	    .interpolate(d3.interpolateLab);
	var speedup_v_p = get_speedup_v_p();
	
	function render_plot0(margin){
	    var width = view.width - margin.left - margin.right,
	    	height = view.height - margin.top - margin.bottom;
		
		var x = d3.scaleLinear()
			.domain([0.5,9])
		    .range([0, width]);
		
		var y = d3.scaleLinear()
			.domain([0,8])
		    .range([height, 0]);
		
		var line = d3.line()
		    .defined(function(d) { return d; })
		    .x(function(d) { return x(d.x); })
		    .y(function(d) { return y(d.y); });
		
		var container = svg.append("g")
			.attr("transform", "translate(" + margin.left + "," + margin.top + ")");
		
		container.append("g")
		    .attr("class", "axis axis--x")
		    .attr("transform", "translate(0," + height + ")")
		    .call(d3.axisBottom().scale(x));
		
		container.append("g")
		    .attr("class", "axis axis--y")
		    .call(d3.axisLeft().scale(y));
		
		container.append("g").selectAll(".line")
		    .data(speedup_v_p)
			.enter().append("path")
		    .attr("class", "line")
		    .attr("stroke", function(d, i) { return color_scale_v(i); } )
		    .attr("d", line);
		
		for(var dots_v=0;dots_v<speedup_v_p.length;++dots_v){
			container.selectAll(".dot .dot"+dots_v)
			    .data(speedup_v_p[dots_v].filter(function(d) { return d; }))
				.enter().append("circle")
			    .attr("class", "dot")
			    .attr("stroke", function(d, i) { return color_scale_v(dots_v); } )
			    .attr("cx", line.x())
			    .attr("cy", line.y())
			    .attr("r", 3.5);
		}
		
		var group_legend = container.append("g");
	
		var group_legend_pointers = group_legend.append("text")
			.text("<<")
			.style("font-size","20px")
			.attr("x", function(d) { return x(8.2); });
		
		group_legend.selectAll(".legend")
			.data(dimensions.V)
			.enter().append("rect")
			.attr("class","square")
			.attr("x", function(d) { return 20; })
			.attr("y", function(d, i) { return 20*i; })
			.attr("width", function(d) { return 16; })
			.attr("height", function(d) { return 16; })
			.style("fill", function(d, i) { return color_scale_v(i); })
			.style("stroke", "#000")
			.style("stroke-width", ".5px")
			.on('mouseover', function(d, i) {
				// console.log(dimensions.V[i]);
				if(h_hist) h_hist.remove();
				group_legend_pointers.attr("y", y(speedup_v_p[i][4].y-0.2));
				h_hist = render_plot_hist({top: 350, right: 660, bottom: 40, left: 40}, dimensions.V[i], 8400);
			});
			
		group_legend.selectAll(".legend")
			.data(dimensions.V)
			.enter().append("text")
			.attr("x", function(d) { return 20+18; })
			.attr("y", function(d, i) { return 20*i+8; })
			.attr("width", function(d) { return 16; })
			.attr("height", function(d) { return 16; })
			.text(function(d) { return d; })
			.style("stroke", "#000")
			.style("stroke-width", ".5px");
	}
	
	function render_plot_hist(margin, version, n){
	    var width = view.width - margin.left - margin.right,
	    	height = view.height - margin.top - margin.bottom;
		
		var container = svg.append("g")
			.attr("transform", "translate(" + margin.left + "," + margin.top + ")");
		
		var gridData = new Array, cell_width = 32;
		var grid_xpos = 0, grid_ypos = 0;
		for(var k of dimensions.K){
			var rowData = new Array;
			grid_xpos = 0;
			for(var p of dimensions.P){
				rowData.push({
					val: get_analysis(version,n,p,k,"total_ms"),
					x: grid_xpos, y: grid_ypos,
					v: version, n:n, p:p, k:k,
					width: cell_width//p<=8?cell_width:12
				});
				grid_xpos+=cell_width;
			}
			gridData.push(rowData);
			grid_ypos+=cell_width;
		}

		var row = container.selectAll(".row")
			.data(gridData)
			.enter().append("g")
			.attr("class", "row");
			
		var color_scale_t = d3.scaleLinear()
		    .domain([0, 80])
		    .range(["green", "red"])
		    .interpolate(d3.interpolateLab);
			
		row.selectAll(".square")
			.data(function(d) { return d; })
			.enter().append("rect")
			.attr("class","square")
			.attr("x", function(d) { return d.x; })
			.attr("y", function(d) { return d.y; })
			.attr("width", function(d) { return d.width; })
			.attr("height", function(d) { return cell_width; })
			.style("fill", function(d){
				if(isNaN(d.val)) return "#fff"; 
				else return color_scale_t(d.val);
			})
			.style("stroke", "#000")
			.style("stroke-width", ".5px")
			.on('mouseover', function(d, i) {
				// console.log(d);
				if(h_plot_pt) h_plot_pt.remove();
				if(h_plot_kt) h_plot_kt.remove();
				if(h_plot_nt) h_plot_nt.remove();
				if(h_timeline) h_timeline.remove();
				if(h_report) h_report.remove();
				var top_right = {top: 340, right: 680, bottom: 240, left: 360},
					bottom_right = {top: 520, right: 680, bottom: 40, left: 360},
					bottom_left = {top: 520, right: 1000, bottom: 40, left: 40};
				h_plot_pt = render_plot_pt(top_right, d.v, d.n, d.k);
				h_plot_kt = render_plot_kt(bottom_left, d.v, d.n, d.p);
				h_plot_nt = render_plot_nt(bottom_right, d.v, d.p, d.k);
				h_timeline = render_timeline({top: 20, right: 40, bottom: 340, left: 720}, d.v, d.n, d.p, d.k);
				h_report = render_report({top: 460, right: 20, bottom: 20, left: 690}, d.v, d.n, d.p, d.k);
			});
		
		return container;
	}
	
	function render_plot_pt(margin, version, n, k){
		var width = view.width - margin.left - margin.right,
	    	height = view.height - margin.top - margin.bottom;
		var x = d3.scaleLinear()
			.domain([0.5,9])
		    .range([0, width]);
		var y = d3.scaleLinear()
			.domain([1,100])
		    .range([height, 0]);
		var container = svg.append("g")
			.attr("transform", "translate(" + margin.left + "," + margin.top + ")");
		container.append("g")
		    .attr("class", "axis axis--x")
		    .attr("transform", "translate(0," + height + ")")
		    .call(d3.axisBottom().scale(x));
		container.append("g")
		    .attr("class", "axis axis--y")
		    .call(d3.axisLeft().scale(y));
		
		var line = d3.line()
		    .defined(function(d) { return !isNaN(d.y); })
		    .x(function(d) { return x(d.x); })
		    .y(function(d) { return y(d.y); });
		var data_points = new Array;
		for(var p of dimensions.P)
			data_points.push({ x: p, y: get_analysis(version,n,p,k,"total_ms")});
		container.append("g").selectAll(".line")
		    .data([data_points])
			.enter().append("path")
		    .attr("class", "line")
		    .attr("stroke", "blue" )
		    .attr("d", line);
		   
		return container;
	}
	
	function render_plot_kt(margin, version, n, p){
		var width = view.width - margin.left - margin.right,
	    	height = view.height - margin.top - margin.bottom;
		var x = d3.scaleLinear()
			.domain([100,2000])
		    .range([0, width]);
		var y = d3.scaleLinear()
			.domain([1,100])
		    .range([height, 0]);
		var container = svg.append("g")
			.attr("transform", "translate(" + margin.left + "," + margin.top + ")");
		container.append("g")
		    .attr("class", "axis axis--x")
		    .attr("transform", "translate(0," + height + ")")
		    .call(d3.axisBottom().scale(x));
		container.append("g")
		    .attr("class", "axis axis--y")
		    .call(d3.axisLeft().scale(y));
		    
		var line = d3.line()
		    .defined(function(d) { return !isNaN(d.y); })
		    .x(function(d) { return x(d.x); })
		    .y(function(d) { return y(d.y); });
		var data_points = new Array;
		for(var k of dimensions.K)
			data_points.push({ x: k, y: get_analysis(version,n,p,k,"total_ms")});
		container.append("g").selectAll(".line")
		    .data([data_points])
			.enter().append("path")
		    .attr("class", "line")
		    .attr("stroke", "blue" )
		    .attr("d", line);
		    
		return container;
	}
	
	function render_plot_nt(margin, version, p, k){
		var width = view.width - margin.left - margin.right,
	    	height = view.height - margin.top - margin.bottom;
		var x = d3.scaleLinear()
			.domain([2000,8500])
		    .range([0, width]);
		var y = d3.scaleLinear()
			.domain([1,100])
		    .range([height, 0]);
		var container = svg.append("g")
			.attr("transform", "translate(" + margin.left + "," + margin.top + ")");
		container.append("g")
		    .attr("class", "axis axis--x")
		    .attr("transform", "translate(0," + height + ")")
		    .call(d3.axisBottom().scale(x));
		container.append("g")
		    .attr("class", "axis axis--y")
		    .call(d3.axisLeft().scale(y));
		    
		var line = d3.line()
		    .defined(function(d) { return !isNaN(d.y); })
		    .x(function(d) { return x(d.x); })
		    .y(function(d) { return y(d.y); });
		var data_points = new Array;
		for(var n of dimensions.N)
			data_points.push({ x: n, y: get_analysis(version,n,p,k,"total_ms")});
		container.append("g").selectAll(".line")
		    .data([data_points])
			.enter().append("path")
		    .attr("class", "line")
		    .attr("stroke", "blue" )
		    .attr("d", line);
		
		return container;
	}
	
	function render_timeline(margin, version, n, p, k){
		var width = view.width - margin.left - margin.right,
	    	height = view.height - margin.top - margin.bottom;
		var x = d3.scaleLinear()
			.domain([0,100])
		    .range([0, width]);
		function _child(n) {return "child["+n+"]";}
		var labels = ["partition"];
		for(var chldi=0;chldi<8;++chldi) labels.push(_child(chldi));
		labels.push("merge");
		var y = d3.scaleBand()
	      .domain(labels)
	      .rangeRound([0, height])
	      .padding(0.1);
		var container = svg.append("g")
			.attr("transform", "translate(" + margin.left + "," + margin.top + ")");
		container.append("g")
		    .attr("class", "axis axis--x")
		    .attr("transform", "translate(0," + height + ")")
		    .call(d3.axisBottom().scale(x));
		container.append("g")
		    .attr("class", "axis axis--y")
		    .call(d3.axisLeft().scale(y));
		function _get(t){return get_analysis(version, n, p, k, t);}
		var _children_start = _get("children_start"), _children_end = _get("children_end");
		var data_points = [
			{s:_get("partition_start"),e:_get("partition_end"),label:"partition"},
			{s:_children_start[0],e:_children_end[0],label:_child(0)},
			{s:_children_start[1],e:_children_end[1],label:_child(1)},
			{s:_children_start[2],e:_children_end[2],label:_child(2)},
			{s:_children_start[3],e:_children_end[3],label:_child(3)},
			{s:_children_start[4],e:_children_end[4],label:_child(4)},
			{s:_children_start[5],e:_children_end[5],label:_child(5)},
			{s:_children_start[6],e:_children_end[6],label:_child(6)},
			{s:_children_start[7],e:_children_end[7],label:_child(7)},
			{s:_get("merge_start"),e:_get("merge_end"),label:"merge"},
		].filter(function(d){return !isNaN(d.s)});
		container.append("g").selectAll("rect")
	      .data(data_points).enter().append("rect")
	      .attr("class", "rect")
	      .attr("y", function(d) { return y(d.label); })
	      .attr("x", function(d) { return x(d.s); })
	      .attr("width", function(d) { return x(d.e) - x(d.s); })
	      .attr("height", y.bandwidth());
      
		return container;
	}
	
	function render_report(margin, version, n, p, k){
		var width = view.width - margin.left - margin.right,
	    	height = view.height - margin.top - margin.bottom;
		var container = svg.append("g")
			.attr("transform", "translate(" + margin.left + "," + margin.top + ")");
		
		container.append("text")
			.text("version:"+version+" n:"+n+" p:"+p+" k:"+k)
			.style("font-size","20px");
			
		function _get(t){return get_analysis(version, n, p, k, t);}
		container.append("text")
			.text("parse:"+_get("parse_s")+"s partition:"+_get("partition_ms")+"ms merge:"+_get("merge_ms")+"ms total:"+_get("total_ms"))
			.attr("y",30)
			.style("font-size","20px");
			
		container.append("text")
			.text("user:"+_get("user_s")+"s sys:"+_get("sys_s")+"s cpu:"+_get("cpu_%")+"% mem:"+_get("mem_kbytes")+"kB")
			.attr("y",60)
			.style("font-size","20px");
			
		container.append("text")
			.text("context switch:"+_get("cs_i")+"(involuntary) "+_get("cs_v")+"(voluntary)")
			.attr("y",90)
			.style("font-size","20px");
			
			
			
			/*
			
			parse_s partition_ms merge_ms total_ms
			user_s sys_s cpu_% mem_kbytes
			cs_i cs_v
			
			*/
			
			
			
		// container.append("rect")
	 //     .attr("class", "rect")
	 //     .attr("y", 0)
	 //     .attr("x", 0)
	 //     .attr("width", width)
	 //     .attr("height", height);
		return container;
	}
	
	render_plot0({top: 20, right: 660, bottom: 440, left: 40});
}

analysis.json

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LICENSE

MIT License

Copyright (c) 2016 Alex Yang 

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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