#IstioCon How HP set up secure and wise platform with Istio John Zheng/ john.zheng@hp.com #IstioCon Agenda ➢ HP Horizon platform design with Istio ➢ Secure Platform ➢ Wise Platform ➢ Excellent Observability access this api based on its role => Version 2: Envoyfilter ext_authz #IstioCon Wise Platform #IstioCon Wise Platform Using envoy filter to handle things from platform level, reduce workload of certain fields, add specific filters, or even add entirely new listeners, clusters, etc. #IstioCon Wise Platform K8s custom resource definition HTTP filters Network filters UDP listener filters … Match

the default behavior is to align the Series index on the DataFrame columns, thus broadcasting row-wise. For example: In [78]: df - df.iloc[0] Out[78]: A B C D 0 0.0000 0.0000 0.0000 0.0000 1 2.3859 with time series data, and the DataFrame index also contains dates, the broadcasting will be column-wise: In [79]: index = pd.date_range('1/1/2000', periods=8) In [80]: df = pd.DataFrame(np.random.randn(8 Unfortunately Panel, being less commonly used than Series and DataFrame, has been slightly neglected feature- wise. A number of methods and options available in DataFrame are not available in Panel. This will get

Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458 10.6.2 Row or Column-wise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . 459 10.6.3 Applying elementwise union() and .difference() methods), and is now disabled. When possible, + and - are now used for element-wise operations, for example for concatenating strings or subtracting datetimes (GH8227, GH14127). Previous perform element-wise addition: In [129]: pd.Index(['a', 'b']) + pd.Index(['a', 'c']) Out[129]: Index([u'aa', u'bc'], dtype='object') Note that numeric Index objects already performed element-wise operations

Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 10.6.2 Row or Column-wise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . 461 10.6.3 Applying elementwise union() and .difference() methods), and is now disabled. When possible, + and - are now used for element-wise operations, for example for concatenating strings or subtracting datetimes (GH8227, GH14127). Previous perform element-wise addition: In [129]: pd.Index(['a', 'b']) + pd.Index(['a', 'c']) Out[129]: Index([u'aa', u'bc'], dtype='object') Note that numeric Index objects already performed element-wise operations

Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 9.6.2 Row or Column-wise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . 512 9.6.3 Aggregation API union() and .difference() methods), and is now disabled. When possible, + and - are now used for element-wise operations, for example for concatenating strings or subtracting datetimes (GH8227, GH14127). Previous perform element-wise addition: In [129]: pd.Index(['a', 'b']) + pd.Index(['a', 'c']) Out[129]: Index(['aa', 'bc'], dtype='object') Note that numeric Index objects already performed element-wise operations

Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 9.6.2 Row or Column-wise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . 510 9.6.3 Aggregation API union() and .difference() methods), and is now disabled. When possible, + and - are now used for element-wise operations, for example for concatenating strings or subtracting datetimes (GH8227, GH14127). Previous perform element-wise addition: In [129]: pd.Index(['a', 'b']) + pd.Index(['a', 'c']) Out[129]: Index(['aa', 'bc'], dtype='object') Note that numeric Index objects already performed element-wise operations

Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537 9.6.2 Row or Column-wise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . 538 9.6.3 Aggregation API objects and other list-likes with DataFrame leading to operations being performed row-wise instead of column-wise (GH17901) 1.2.7.8 Numeric • Bug in .clip() with axis=1 and a list-like for threshold union() and .difference() methods), and is now disabled. When possible, + and - are now used for element-wise operations, for example for concatenating strings or subtracting datetimes (GH8227, GH14127). Previous

ValueError instead of an Exception (GH21770) • Index subtraction will attempt to operate element-wise instead of raising TypeError (GH19369) • pandas.io.formats.style.Styler supports a number-format background_gradient() now also supports tablewise application (in addition to rowwise and column- wise) with axis=None (GH15204) • bar() now also supports tablewise application (in addition to rowwise sort_index()) \\\\\\\\\\\\\\\Out[68]: True Comparing array-like objects You can conveniently perform element-wise comparisons when comparing a pandas data structure with a scalar value: In [69]: pd.Series(['foo'

sort_index()) \\\\\\\\\\\\\\\Out[64]: True Comparing array-like objects You can conveniently perform element-wise comparisons when comparing a pandas data structure with a scalar value: In [65]: pd.Series(['foo' \\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[66]: array([ True, False, ˓→False]) Pandas also handles element-wise comparisons between different array-like objects of the same length: In [67]: pd.Series(['foo', 'bar' operate on an entire DataFrame or Series, row- or column-wise, or elementwise. 1. Tablewise Function Application: pipe() 2. Row or Column-wise Function Application: apply() 3. Aggregation API: agg()

sort_index()) \\\\\\\\\\\\\\\Out[64]: True Comparing array-like objects You can conveniently perform element-wise comparisons when comparing a pandas data structure with a scalar value: In [65]: pd.Series(['foo' \\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[66]: array([ True, False, ˓→False]) Pandas also handles element-wise comparisons between different array-like objects of the same length: In [67]: pd.Series(['foo', 'bar' operate on an entire DataFrame or Series, row- or column-wise, or elementwise. 1. Tablewise Function Application: pipe() 2. Row or Column-wise Function Application: apply() 3. Aggregation API: agg()