Cost based optimization 2 slides
This commit is contained in:
parent
5c8e187858
commit
e8870d34dc
|
|
@ -159,7 +159,7 @@
|
|||
</tr>
|
||||
<tr class="fragment" data-fragment-index="6">
|
||||
<td>Union</td>
|
||||
<td>$R \cup S$</td>
|
||||
<td>$R \uplus S$</td>
|
||||
<td>$0$</td>
|
||||
<td>$O(1)$</td>
|
||||
</tr>
|
||||
|
|
|
|||
573
slides/cse4562sp2018/2018-03-05-CostBasedOptimization2.html
Normal file
573
slides/cse4562sp2018/2018-03-05-CostBasedOptimization2.html
Normal file
|
|
@ -0,0 +1,573 @@
|
|||
<!doctype html>
|
||||
<html lang="en">
|
||||
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
|
||||
<title>CSE 4/562 - Spring 2018</title>
|
||||
|
||||
<meta name="description" content="CSE 4/562 - Spring 2018">
|
||||
<meta name="author" content="Oliver Kennedy">
|
||||
|
||||
<meta name="apple-mobile-web-app-capable" content="yes" />
|
||||
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" />
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
|
||||
|
||||
<link rel="stylesheet" href="../reveal.js-3.6.0/css/reveal.css">
|
||||
<link rel="stylesheet" href="ubodin.css" id="theme">
|
||||
|
||||
<!-- Code syntax highlighting -->
|
||||
<link rel="stylesheet" href="../reveal.js-3.6.0/lib/css/zenburn.css">
|
||||
|
||||
<!-- Printing and PDF exports -->
|
||||
<script>
|
||||
var link = document.createElement( 'link' );
|
||||
link.rel = 'stylesheet';
|
||||
link.type = 'text/css';
|
||||
link.href = window.location.search.match( /print-pdf/gi ) ? '../reveal.js-3.6.0/css/print/pdf.css' : '../reveal.js-3.6.0/css/print/paper.css';
|
||||
document.getElementsByTagName( 'head' )[0].appendChild( link );
|
||||
</script>
|
||||
|
||||
<script src="../reveal.js-3.6.0/lib/js/head.min.js"></script>
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="../reveal.js-3.6.0/lib/js/html5shiv.js"></script>
|
||||
<![endif]-->
|
||||
</head>
|
||||
|
||||
<body>
|
||||
|
||||
<div class="reveal">
|
||||
<!-- Any section element inside of this container is displayed as a slide -->
|
||||
|
||||
<div class="header">
|
||||
<!-- Any Talk-Specific Header Content Goes Here -->
|
||||
CSE 4/562 - Database Systems
|
||||
</div>
|
||||
|
||||
<div class="slides">
|
||||
|
||||
<section>
|
||||
<h1>Cost Based Optimization</h1>
|
||||
<h3>CSE 4/562 – Database Systems</h3>
|
||||
<h5>February 28, 2018</h5>
|
||||
</section>
|
||||
<!-- ============================================ -->
|
||||
|
||||
<section>
|
||||
<section>
|
||||
<h3>Remember the Real Goals</h3>
|
||||
<ol>
|
||||
<li>Accurately <b>rank</b> the plans.</li>
|
||||
<li>Don't spend more time optimizing than you get back.</li>
|
||||
<li>Don't pick a plan that uses more memory than you have.</li>
|
||||
</ol>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h3>Accounting</h3>
|
||||
<p style="margin-top: 50px;">Figure out the cost of each <b>individual</b> operator.</p>
|
||||
<p style="margin-top: 50px;">Only count the number of IOs <b>added</b> by each operator.</p>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<table style="font-size: 70%">
|
||||
<tr><th>Operation</th><th>RA</th><th>IOs Added (#pages)</th><th>Memory (#tuples)</th></tr>
|
||||
<tr>
|
||||
<td>Table Scan</td>
|
||||
<td>$R$</td>
|
||||
<td>$\frac{|R|}{\mathcal P}$</td>
|
||||
<td>$O(1)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Projection</td>
|
||||
<td>$\pi(R)$</td>
|
||||
<td>$0$</td>
|
||||
<td>$O(1)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Selection</td>
|
||||
<td>$\sigma(R)$</td>
|
||||
<td>$0$</td>
|
||||
<td>$O(1)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Union</td>
|
||||
<td>$R \cup S$</td>
|
||||
<td>$0$</td>
|
||||
<td>$O(1)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="vertical-align: middle;">Sort <span>(In-Mem)</span></td>
|
||||
<td style="vertical-align: middle;">$\tau(R)$</td>
|
||||
<td>$0$</td>
|
||||
<td>$O(|R|)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Sort (On-Disk)</td>
|
||||
<td>$\tau(R)$</td>
|
||||
<td>$\frac{2 \cdot \lfloor log_{\mathcal B}(|R|) \rfloor}{\mathcal P}$</td>
|
||||
<td>$O(\mathcal B)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><span>(B+Tree)</span> Index Scan</td>
|
||||
<td>$Index(R, c)$</td>
|
||||
<td>$\log_{\mathcal I}(|R|) + \frac{|\sigma_c(R)|}{\mathcal P}$</td>
|
||||
<td>$O(1)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>(Hash) Index Scan</td>
|
||||
<td>$Index(R, c)$</td>
|
||||
<td>$1$</td>
|
||||
<td>$O(1)$</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<ol style="font-size: 50%; margin-top: 50px;">
|
||||
<li>Tuples per Page ($\mathcal P$) <span>– Normally defined per-schema</span></li>
|
||||
<li>Size of $R$ ($|R|$)</li>
|
||||
<li>Pages of Buffer ($\mathcal B$)</li>
|
||||
<li>Keys per Index Page ($\mathcal I$)</li>
|
||||
</ol>
|
||||
</section>
|
||||
<section>
|
||||
<table style="font-size: 70%">
|
||||
<tr><th width="300px">Operation</th><th>RA</th><th>IOs Added (#pages)</th><th>Memory (#tuples)</th></tr>
|
||||
<tr>
|
||||
<td style="font-size: 60%">Nested Loop Join <span>(Buffer $S$ in mem)</span></td>
|
||||
<td>$R \times S$</td>
|
||||
<td>$0$</td>
|
||||
<td>$O(|S|)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="font-size: 60%">Nested Loop Join (Buffer $S$ on disk)</td>
|
||||
<td>$R \times_{disk} S$</td>
|
||||
<td>$(1+ |R|) \cdot \frac{|S|}{\mathcal P}$</td>
|
||||
<td>$O(1)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>1-Pass Hash Join</td>
|
||||
<td>$R \bowtie_{1PH, c} S$</td>
|
||||
<td>$0$</td>
|
||||
<td>$O(|S|)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>2-Pass Hash Join</td>
|
||||
<td>$R \bowtie_{2PH, c} S$</td>
|
||||
<td>$\frac{2|R| + 2|S|}{\mathcal P}$</td>
|
||||
<td>$O(1)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Sort-Merge Join </td>
|
||||
<td>$R \bowtie_{SM, c} S$</td>
|
||||
<td>[Sort]</td>
|
||||
<td>[Sort]</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><span>(Tree)</span> Index NLJ</td>
|
||||
<td>$R \bowtie_{INL, c}$</td>
|
||||
<td>$|R| \cdot (\log_{\mathcal I}(|S|) + \frac{|\sigma_c(S)|}{\mathcal P})$</td>
|
||||
<td>$O(1)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>(Hash) Index NLJ</td>
|
||||
<td>$R \bowtie_{INL, c}$</td>
|
||||
<td>$|R| \cdot 1$</td>
|
||||
<td>$O(1)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><span>(In-Mem)</span> Aggregate</td>
|
||||
<td>$\gamma_A(R)$</td>
|
||||
<td>$0$</td>
|
||||
<td>$adom(A)$</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="font-size: 90%">(Sort/Merge) Aggregate</td>
|
||||
<td>$\gamma_A(R)$</td>
|
||||
<td>[Sort]</td>
|
||||
<td>[Sort]</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<ol style="font-size: 50%;">
|
||||
<li>Tuples per Page ($\mathcal P$) <span>– Normally defined per-schema</span></li>
|
||||
<li>Size of $R$ ($|R|$)</li>
|
||||
<li>Pages of Buffer ($\mathcal B$)</li>
|
||||
<li>Keys per Index Page ($\mathcal I$)</li>
|
||||
<li>Number of distinct values of $A$ ($adom(A)$)</li>
|
||||
</ol>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
<!-- ============================================ -->
|
||||
|
||||
<section>
|
||||
<section>
|
||||
<p>Estimating IOs requires Estimating $|Q(R)|$</p>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h3>Cardinality Estimation</h3>
|
||||
<p class="fragment">Unlike estimating IOs, cardinality estimation doesn't care about the algorithm, so we'll just be working with raw RA.</p>
|
||||
|
||||
<p class="fragment">Also unlike estimating IOs, we care about the cardinality of $|Q(R)|$ as a whole, rather than the contribution of each individual operator.</p>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<table style="font-size: 70%">
|
||||
<tr>
|
||||
<th>Operator</th>
|
||||
<th>RA</th>
|
||||
<th>Estimated Size</th>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td>Table</td>
|
||||
<td>$R$</td>
|
||||
<td class="fragment" data-fragment-index="1">$|R|$</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td>Projection</td>
|
||||
<td>$\pi(Q)$</td>
|
||||
<td class="fragment" data-fragment-index="2">$|Q|$</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td>Union</td>
|
||||
<td>$Q_1 \uplus Q_2$</td>
|
||||
<td class="fragment" data-fragment-index="3">$|Q_1| + |Q_2|$</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td>Cross Product</td>
|
||||
<td>$Q_1 \times Q_2$</td>
|
||||
<td class="fragment" data-fragment-index="4">$|Q_1| \times |Q_2|$</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td>Sort</td>
|
||||
<td>$\tau(Q)$</td>
|
||||
<td class="fragment" data-fragment-index="5">$|Q|$</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td>Limit</td>
|
||||
<td>$\texttt{LIMIT}_N(Q)$</td>
|
||||
<td class="fragment" data-fragment-index="6">$N$</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td>Selection</td>
|
||||
<td>$\sigma_c(Q)$</td>
|
||||
<td class="fragment" data-fragment-index="8">$|Q| \times \texttt{SEL}(c, Q)$</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td>Join</td>
|
||||
<td>$Q_1 \bowtie_c Q_2$</td>
|
||||
<td class="fragment" data-fragment-index="9">$|Q_1| \times |Q_2| \times \texttt{SEL}(c, Q_1\times Q_2)$</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td>Distinct</td>
|
||||
<td>$\delta_A(Q)$</td>
|
||||
<td class="fragment" data-fragment-index="11">$\texttt{UNIQ}(A, Q)$</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td>Aggregate</td>
|
||||
<td>$\gamma_{A, B \leftarrow \Sigma}(Q)$</td>
|
||||
<td class="fragment" data-fragment-index="12">$\texttt{UNIQ}(A, Q)$</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<ul style="font-size: 50%; margin-top: 20px">
|
||||
<li class="fragment" data-fragment-index="7">$\texttt{SEL}(c, Q)$: Selectivity of $c$ on $Q$, or $\frac{|\sigma_c(Q)|}{|Q|}$</li>
|
||||
<li class="fragment" data-fragment-index="10">$\texttt{UNIQ}(A, Q)$: # of distinct values of $A$ in $Q$.
|
||||
</ul>
|
||||
</section>
|
||||
|
||||
<!-- 2018 by OK:
|
||||
Things to cover:
|
||||
- Defaults: The 10% rule
|
||||
- Basic Assumptions:
|
||||
- Selectivity: MIN/MAX+COUNT, Uniform distribution, No correlations
|
||||
- Unique Values: COUNT DISTINCT, No correlations
|
||||
- Histograms: Nonuniform distributions
|
||||
- Constraints: Keys, FDs, FKey (implications for Joins)
|
||||
-->
|
||||
|
||||
<section>
|
||||
<h3>Cardinality Estimation</h3>
|
||||
<h4>(The Hard Parts)</h4>
|
||||
|
||||
<dl>
|
||||
<dt style="margin-top: 50px;">$\sigma_c(Q)$ (Cardinality Estimation)</dt>
|
||||
<dd>How many tuples will a condition $c$ allow to pass?</dd>
|
||||
|
||||
<dt style="margin-top: 50px;">$\delta_A(Q)$ (Distinct Values Estimation)</dt>
|
||||
<dd>How many distinct values of attribute(s) $A$ exist?</dd>
|
||||
</dl>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h3>Remember the Real Goals</h3>
|
||||
<ol>
|
||||
<li>Accurately <b>rank</b> the plans.</li>
|
||||
<li>Don't spend more time optimizing than you get back.</li>
|
||||
</ol>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h3>(Some) Estimation Techniques</h3>
|
||||
|
||||
<dl style="font-size: 80%">
|
||||
<div class="fragment">
|
||||
<dt>Guess Randomly</dt>
|
||||
<dd>Rules of thumb if you have no other options...</dd>
|
||||
</div>
|
||||
|
||||
<div class="fragment">
|
||||
<dt>Uniform Prior</dt>
|
||||
<dd>Use basic statistics to make a very rough guess.</dd>
|
||||
</div>
|
||||
|
||||
<div class="fragment">
|
||||
<dt>Sampling / History</dt>
|
||||
<dd>Small, Quick Sampling Runs (or prior executions of the query).</dd>
|
||||
</div>
|
||||
|
||||
<div class="fragment">
|
||||
<dt>Histograms</dt>
|
||||
<dd>Using more detailed statistics for improved guesses.</dd>
|
||||
</div>
|
||||
|
||||
<div class="fragment">
|
||||
<dt>Constraints</dt>
|
||||
<dd>Using rules about the data for improved guesses.</dd>
|
||||
</div>
|
||||
</dl>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
<!-- ============================================ -->
|
||||
|
||||
<section>
|
||||
<section>
|
||||
<h3>(Some) Estimation Techniques</h3>
|
||||
|
||||
<dl style="font-size: 80%">
|
||||
<dt>Guess Randomly</dt>
|
||||
<dd>Rules of thumb if you have no other options...</dd>
|
||||
|
||||
<dt style="color: grey;">Uniform Prior</dt>
|
||||
<dd style="color: grey;">Use basic statistics to make a very rough guess.</dd>
|
||||
|
||||
<dt style="color: grey;">Sampling / History</dt>
|
||||
<dd style="color: grey;">Small, Quick Sampling Runs (or prior executions of the query).</dd>
|
||||
|
||||
<dt style="color: grey;">Histograms</dt>
|
||||
<dd style="color: grey;">Using more detailed statistics for improved guesses.</dd>
|
||||
|
||||
<dt style="color: grey;">Constraints</dt>
|
||||
<dd style="color: grey;">Using rules about the data for improved guesses.</dd>
|
||||
</dl>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h3>The 10% Selectivity Rule</h3>
|
||||
|
||||
<p>Every select or distinct operator passes 10% of all rows.</p>
|
||||
|
||||
<div class="fragment">
|
||||
$$\sigma_{A = 1 \wedge B = 2}(R)$$
|
||||
</div>
|
||||
<div class="fragment">
|
||||
$$|\sigma_{A = 1 \wedge B = 2}(R)| = 0.1 \cdot |R|$$
|
||||
</div>
|
||||
|
||||
<div class="fragment" style="margin-top: 50px;">
|
||||
$$\sigma_{A = 1}(\sigma_{B = 2}(R))$$
|
||||
</div>
|
||||
<div class="fragment">
|
||||
$$|\sigma_{A = 1}(\sigma_{B = 2}(R))| = 0.1 \cdot |\sigma_{B = 2}(R)| = 0.1 \cdot 0.1 \cdot |R|$$
|
||||
</div>
|
||||
|
||||
<p class="fragment" style="font-size: 80%; font-weight: bold; margin-top: 50px;">(Queries are typically standardized first)</p>
|
||||
|
||||
<p class="fragment" style="font-size: 80%; font-weight: bold; margin-top: 20px;">(The specific % varies by DBMS. E.g., Teradata uses 10% for the first <code>AND</code> clause, and 75% for every subsequent clause)</p>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<p>The 10% rule is a fallback when everything else fails. <br/> Usually, databases collect statistics...</p>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
<!-- ============================================ -->
|
||||
|
||||
<section>
|
||||
<section>
|
||||
<h3>(Some) Estimation Techniques</h3>
|
||||
|
||||
<dl style="font-size: 80%">
|
||||
<dt style="color: grey;">Guess Randomly</dt>
|
||||
<dd style="color: grey;">Rules of thumb if you have no other options...</dd>
|
||||
|
||||
<dt>Uniform Prior</dt>
|
||||
<dd>Use basic statistics to make a very rough guess.</dd>
|
||||
|
||||
<dt style="color: grey;">Sampling / History</dt>
|
||||
<dd style="color: grey;">Small, Quick Sampling Runs (or prior executions of the query).</dd>
|
||||
|
||||
<dt style="color: grey;">Histograms</dt>
|
||||
<dd style="color: grey;">Using more detailed statistics for improved guesses.</dd>
|
||||
|
||||
<dt style="color: grey;">Constraints</dt>
|
||||
<dd style="color: grey;">Using rules about the data for improved guesses.</dd>
|
||||
</dl>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h3>Uniform Prior</h3>
|
||||
|
||||
<p style="text-align: left; margin-bottom: 0px; font-weight: bold;">We assume that for $\sigma_c(Q)$...</p>
|
||||
<ol>
|
||||
<li>Basic statistics are known about $Q$: <ul>
|
||||
<li style="margin-top: 0px;"><code>COUNT(*)</code></li>
|
||||
<li style="margin-top: 0px;"><code>COUNT(DISTINCT A)</code> (for each A)</li>
|
||||
<li style="margin-top: 0px;"><code>MIN(A)</code>, <code>MAX(A)</code> (for each numeric A)</li>
|
||||
</ul></li>
|
||||
<li>Attribute values are uniformly distributed.</li>
|
||||
<li>No inter-attribute correlations.</li>
|
||||
</ol>
|
||||
<p class="fragment" style="font-size: 80%; font-weight: bold; margin-top: 20px;">
|
||||
If (1) fails, fall back to the 10% rule.
|
||||
</p>
|
||||
<p class="fragment" style="font-size: 80%; font-weight: bold; margin-top: 0px;">
|
||||
If (2) or (3) fails, it'll often still be a <i>good enough</i> estimate.
|
||||
</p>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h3>Some Conditions</h3>
|
||||
|
||||
<p>Selectivity is a probability ($\texttt{SEL}(c, Q) = P(c)$)</p>
|
||||
<table style="font-size: 85%">
|
||||
<tr class="fragment">
|
||||
<td>$P(A = x_1)$</td>
|
||||
<td>$=$</td>
|
||||
<td class="fragment">$\frac{1}{\texttt{COUNT(DISTINCT A)}}$</td>
|
||||
</tr>
|
||||
|
||||
<tr class="fragment">
|
||||
<td>$P(A \in (x_1, x_2, \ldots, x_N))$</td>
|
||||
<td>$=$</td>
|
||||
<td class="fragment">$\frac{N}{\texttt{COUNT(DISTINCT A)}}$</td>
|
||||
</tr>
|
||||
|
||||
<tr class="fragment">
|
||||
<td>$P(A \leq x_1)$</td>
|
||||
<td>$=$</td>
|
||||
<td class="fragment">$\frac{x_1 - \texttt{MIN(A)}}{\texttt{MAX(A)} - \texttt{MIN(A)}}$</td>
|
||||
</tr>
|
||||
|
||||
<tr class="fragment">
|
||||
<td>$P(x_1 \leq A \leq x_2)$</td>
|
||||
<td>$=$</td>
|
||||
<td class="fragment">$\frac{x_2 - x_1}{\texttt{MAX(A)} - \texttt{MIN(A)}}$</td>
|
||||
</tr>
|
||||
|
||||
<tr class="fragment">
|
||||
<td>$P(A = B)$</td>
|
||||
<td>$=$</td>
|
||||
<td class="fragment" style="font-size: 60%">$\textbf{min}\left( \frac{1}{\texttt{COUNT(DISTINCT A)}}, \frac{1}{\texttt{COUNT(DISTINCT B)}} \right)$</td>
|
||||
</tr>
|
||||
|
||||
<tr class="fragment">
|
||||
<td>$P(c_1 \wedge c_2)$</td>
|
||||
<td>$=$</td>
|
||||
<td class="fragment" >$P(c_1) \cdot P(c_2)$</td>
|
||||
</tr>
|
||||
|
||||
<tr class="fragment">
|
||||
<td>$P(c_1 \vee c_2)$</td>
|
||||
<td>$=$</td>
|
||||
<td class="fragment" >$1 - (1 - P(c_1)) \cdot (1 - P(c_2))$</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<p style="font-size: 60%">(With constants $x_1$, $x_2$, ...)</p>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
</div></div>
|
||||
|
||||
<script src="../reveal.js-3.6.0/js/reveal.js"></script>
|
||||
|
||||
<script>
|
||||
|
||||
// Full list of configuration options available at:
|
||||
// https://github.com/hakimel/../reveal.js#configuration
|
||||
Reveal.initialize({
|
||||
controls: false,
|
||||
progress: true,
|
||||
history: true,
|
||||
center: true,
|
||||
slideNumber: true,
|
||||
|
||||
transition: 'fade', // none/fade/slide/convex/concave/zoom
|
||||
|
||||
chart: {
|
||||
defaults: {
|
||||
global: {
|
||||
title: { fontColor: "#333", fontSize: 24 },
|
||||
legend: {
|
||||
labels: { fontColor: "#333", fontSize: 20 },
|
||||
},
|
||||
responsiveness: true
|
||||
},
|
||||
scale: {
|
||||
scaleLabel: { fontColor: "#333", fontSize: 20 },
|
||||
gridLines: { color: "#333", zeroLineColor: "#333" },
|
||||
ticks: { fontColor: "#333", fontSize: 16 },
|
||||
}
|
||||
},
|
||||
line: { borderColor: [ "rgba(20,220,220,.8)" , "rgba(220,120,120,.8)", "rgba(20,120,220,.8)" ], "borderDash": [ [5,10], [0,0] ]},
|
||||
bar: { backgroundColor: [
|
||||
"rgba(220,220,220,0.8)",
|
||||
"rgba(151,187,205,0.8)",
|
||||
"rgba(205,151,187,0.8)",
|
||||
"rgba(187,205,151,0.8)"
|
||||
]
|
||||
},
|
||||
pie: { backgroundColor: [ ["rgba(0,0,0,.8)" , "rgba(220,20,20,.8)", "rgba(20,220,20,.8)", "rgba(220,220,20,.8)", "rgba(20,20,220,.8)"] ]},
|
||||
radar: { borderColor: [ "rgba(20,220,220,.8)" , "rgba(220,120,120,.8)", "rgba(20,120,220,.8)" ]},
|
||||
},
|
||||
|
||||
// Optional ../reveal.js plugins
|
||||
dependencies: [
|
||||
{ src: '../reveal.js-3.6.0/lib/js/classList.js', condition: function() { return !document.body.classList; } },
|
||||
{ src: '../reveal.js-3.6.0/plugin/math/math.js',
|
||||
condition: function() { return true; },
|
||||
mathjax: '../reveal.js-3.6.0/js/MathJax.js'
|
||||
},
|
||||
{ src: '../reveal.js-3.6.0/plugin/markdown/marked.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } },
|
||||
{ src: '../reveal.js-3.6.0/plugin/markdown/markdown.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } },
|
||||
{ src: '../reveal.js-3.6.0/plugin/highlight/highlight.js', async: true, condition: function() { return !!document.querySelector( 'pre code' ); }, callback: function() { hljs.initHighlightingOnLoad(); } },
|
||||
{ src: '../reveal.js-3.6.0/plugin/zoom-js/zoom.js', async: true },
|
||||
{ src: '../reveal.js-3.6.0/plugin/notes/notes.js', async: true },
|
||||
// Chart.min.js
|
||||
{ src: '../reveal.js-3.6.0/plugin/chart/Chart.min.js'},
|
||||
// the plugin
|
||||
{ src: '../reveal.js-3.6.0/plugin/chart/csv2chart.js'},
|
||||
{ src: '../reveal.js-3.6.0/plugin/svginline/es6-promise.auto.js', async: false },
|
||||
{ src: '../reveal.js-3.6.0/plugin/svginline/data-src-svg.js', async: false }
|
||||
]
|
||||
});
|
||||
|
||||
</script>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
Loading…
Reference in a new issue