Mesh? 为了将基础设施的运维管理从应用代码中剥离，我们需要七层的流量管 理能力： ● Routing based on layer-7 header ○ Load balancing at requet level ○ HTTP host/header/url/method, ○ Thrift service name/method name ○ Dubbo Interface/method/attachment ... IP Data IP Header TCP Data TCP Header Layer-7 Header Data #IstioCon What Do We Get From Istio? IP Data IP Header TCP Data TCP Header Layer-7 Header Data Traffic Management Application - AwesomeRPC ProductPage Reviews v1 AwesomeRPC (header: user != Jason) AwesomeRPC (header: user = Jason) AwesomeRPC (header: user = XXX) Reviews v2 Let’s say that we’re running

Destination Rule 外部请求 内部客户端 Service2 Service1 网格内部 定义网格入口 • 服务端口 • Host • TLS 配置 • 路由配置 • 根据 Host 路由 • 根据 Header • 根据 URI 路由 目的地流量策略配置 • LB 策略 • 连接池配置 • 断路器配置 • TLS 配置 Gateway External Service 统一网格出口 • 出口地址（Gateway 的处理进行聚合，而不是为每一个服务创建一个 Listener？ • 降低 Listener 数量和配置大小，减少资源占用 • 兼容 headless 和虚机服务，避免 Listener 配置频繁更新 • 采用七层 header 进行路由，请求原始目的 IP 不应影响路由结果 入向请求配置 出向请求配置 0.0.0.0_9080 0.0.0.0_15001 0.0.0.0_15006 Pilot (ADS Server) LB、基于四层链接错误的 Retries 和 Circuit Breaker – 基于四层的路由（IP + Port） – 基于四层的 Metrics（TCP收发包数量等） IP Header TCP Header Layer 7 Protocol Header Layer 7 Protocol Data Istio 支持的七层协议非常有限：HTTP 1.1、 HTTP2、 gRPC 其余协议只能在四层进行处理（Thrift、Redis

tgz.Extract() does not sanitise file paths which may lead to writing to arbitrary file paths. A header.Name containing patterns such as .. could traverse the file system and perform out of bounds file Errorf("create gzip reader: %v", err) } tarReader := tar.NewReader(uncompressedStream) for { header, err := tarReader.Next() if err == io.EOF { break } if err != nil { 20 Istio Security Audit, 114 115 116 return fmt.Errorf("next: %v", err) } dest := filepath.Join(destination, header.Name) switch header.Typeflag { case tar.TypeDir: if _, err := os.Stat(dest); err != nil { if err := os.Mkdir(dest

GH3876, GH3867, GH1305) • MultiIndex column support for reading and writing csv format files – The header option in read_csv now accepts a list of the rows from which to read the index. – The option, tupleize_cols R2C2 R_l0_g3,R_l1_g3,R3C0,R3C1,R3C2 R_l0_g4,R_l1_g4,R4C0,R4C1,R4C2 In [24]: pd.read_csv(’mi.csv’,header=[0,1,2,3],index_col=[0,1],tupleize_cols=False) C0 C_l0_g0 C_l0_g1 C_l0_g2 1.1. v0.12.0 (July 24 parts of the computation and is often unnec- essary. • The default column names for a file with no header have been changed to the integers 0 through N - 1. This is to create consistency with the DataFrame

v0.12.0 (July 24, 2013) 37 pandas: powerful Python data analysis toolkit, Release 0.13.1 – The header option in read_csv now accepts a list of the rows from which to read the index. – The option, tupleize_cols R2C2 R_l0_g3,R_l1_g3,R3C0,R3C1,R3C2 R_l0_g4,R_l1_g4,R4C0,R4C1,R4C2 In [24]: pd.read_csv(’mi.csv’,header=[0,1,2,3],index_col=[0,1],tupleize_cols=False) Out[24]: C0 C_l0_g0 C_l0_g1 C_l0_g2 C1 C_l1_g0 C_l1_g1 parts of the computation and is often unnec- essary. • The default column names for a file with no header have been changed to the integers 0 through N - 1. This is to create consistency with the DataFrame

values needed interpolating (GH7173). • Bug where col_space was ignored in DataFrame.to_string() when header=False (GH8230). • Bug with DatetimeIndex.asof incorrectly matching partial strings and returning values needed interpolating (GH7173). • Bug where col_space was ignored in DataFrame.to_string() when header=False (GH8230). • Bug in to_clipboard that would clip long column data (GH8305) • Bug in DataFrame dataframe to HTML it used to return Empty DataFrame. This special case has been removed, instead a header with the column names is returned (GH6062). • Series and Index now internall share more common operations

values needed interpolating (GH7173). • Bug where col_space was ignored in DataFrame.to_string() when header=False (GH8230). • Bug with DatetimeIndex.asof incorrectly matching partial strings and returning values needed interpolating (GH7173). • Bug where col_space was ignored in DataFrame.to_string() when header=False (GH8230). • Bug in to_clipboard that would clip long column data (GH8305) • Bug in DataFrame dataframe to HTML it used to return Empty DataFrame. This special case has been removed, instead a header with the column names is returned (GH6062). • Series and Index now internall share more common operations

dataframe to HTML it used to return Empty DataFrame. This special case has been removed, instead a header with the column names is returned (GH6062). • Series and Index now internall share more common operations wasn’t renamed to the group name • Bug in DataFrame.to_csv where setting index=False ignored the header kwarg (GH6186) • Bug in DataFrame.plot and Series.plot, where the legend behave inconsistently when and \r-delimited lines • Bug in python parser with explicit multi-index in row following column header (GH6893) • Bug in Series.rank and DataFrame.rank that caused small floats (<1e-13) to all receive

Van den Bossche • MeeseeksMachine • Tom Augspurger • Will Ayd • William Ayd • jbrockmendel {{ header }} 4 Chapter 1. Whats new in 0.25.2 (October 15, 2019) CHAPTER TWO INSTALLATION The easiest pandas: powerful Python data analysis toolkit, Release 0.25.3 {{ header }} 10 Chapter 2. Installation CHAPTER THREE GETTING STARTED {{ header }} 3.1 Package overview pandas is a Python package providing IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. {{ header }} 3.2 10 minutes to pandas This is a short introduction to pandas, geared mainly for new users

made only in major releases (except for experimental features) See Version Policy for more. {{ header }} 1.2 Enhancements 1.2.1 Using Numba in rolling.apply and expanding.apply We’ve added an engine + {{ header }} 1.10. Contributors 41 pandas: powerful Python data analysis toolkit, Release 1.0.0 42 Chapter 1. What’s new in 1.0.0 (January 29, 2020) CHAPTER TWO GETTING STARTED {{ header }} 2 gotchas. It explains issues surrounding the installa- tion and usage of the above three libraries. {{ header }} 2.1. Installation 47 pandas: powerful Python data analysis toolkit, Release 1.0.0 2.2 Package