update graphs

zhaofanfan2019 · Sep 2, 2014 · 869141d · 869141d
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diff --git a/300_Aggregations/30_histogram.asciidoc b/300_Aggregations/30_histogram.asciidoc
@@ -104,7 +104,7 @@ means `0-20,000`, the key `20000` means `20,000-40,000`, etc.
 Graphically, you could represent the above data in a histogram like this:
 
 [[barcharts-histo1]]
-image::images/300_30_histo1.png["Histogram of top makes per price range"]
+image::images/300_30_histo1.svg["Histogram of top makes per price range"]
 
 Of course, you can build bar charts with any aggregation which emits categories
 and statistics, not just the `histogram` bucket.  Let's build a bar chart of
@@ -145,7 +145,7 @@ std_err = std_deviation / count
 Which will allow us to build a chart like this:
 
 [[barcharts-bar1]]
-image::images/300_30_bar1.png["Barchart of average price per make, with error bars"]
+image::images/300_30_bar1.svg["Barchart of average price per make, with error bars"]
 
 
 
diff --git a/300_Aggregations/35_date_histogram.asciidoc b/300_Aggregations/35_date_histogram.asciidoc
@@ -176,7 +176,7 @@ you'll get a response that is easy to plug straight into your graphing libraries
 and give you a graph like this:
 
 [[date-histo-ts1]]
-image::images/300_35_ts1.png["Line chart of cars sold per month"]
+image::images/300_35_ts1.svg["Line chart of cars sold per month"]
 
 === Extended Example
 
@@ -270,4 +270,4 @@ We can take this response and put it into a graph, showing a line chart for
 total sale price, and a bar chart for each individual make (per quarter):
 
 [[date-histo-ts2]]
-image::images/300_35_ts2.png["Line chart of cars sold per month"]
+image::images/300_35_ts2.svg["Line chart of cars sold per month"]
diff --git a/300_Aggregations/65_percentiles.asciidoc b/300_Aggregations/65_percentiles.asciidoc
@@ -23,14 +23,14 @@ metric is easily skewed by just a single outlier.
 This graph visualizes the problem.  If you rely on simple metrics like mean or median, you might see a graph that looks like this:
 
 [[percentile-mean-median]]
-image::images/300_65_percentile1.png["Assessing website latency using mean/median"]
+image::images/300_65_percentile1.svg["Assessing website latency using mean/median"]
 
 Everything looks fine.  There is a slight bump, but nothing to be concerned about.
 But if we load up the 99th percentile (the value which accounts for the slowest 1%
 of latencies), we see an entirely different story:
 
 [[percentile-mean-median-percentile]]
-image::images/300_65_percentile2.png["Assessing website latency using percentiles"]
+image::images/300_65_percentile2.svg["Assessing website latency using percentiles"]
 
 Woah!  At 9:30 AM, the mean is only 75ms.  As a system administrator, you wouldn't
 look at this value twice.  Everything normal!  But the 99th percentile is telling

diff --git a/images/300_30_bar1.png b/images/300_30_bar1.png
diff --git a/images/300_30_bar1.svg b/images/300_30_bar1.svg
diff --git a/images/300_30_histo1.png b/images/300_30_histo1.png
diff --git a/images/300_30_histo1.svg b/images/300_30_histo1.svg
diff --git a/images/300_35_ts1.png b/images/300_35_ts1.png
diff --git a/images/300_35_ts1.svg b/images/300_35_ts1.svg
diff --git a/images/300_35_ts2.png b/images/300_35_ts2.png
diff --git a/images/300_35_ts2.svg b/images/300_35_ts2.svg
diff --git a/images/300_65_percentile1.png b/images/300_65_percentile1.png
diff --git a/images/300_65_percentile1.svg b/images/300_65_percentile1.svg
diff --git a/images/300_65_percentile2.png b/images/300_65_percentile2.png
diff --git a/images/300_65_percentile2.svg b/images/300_65_percentile2.svg