D-Streams：大规模流处理的高效容错模型

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"Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters." 在大数据处理领域，实时处理不断流入的数据是许多关键应用的核心需求。然而，现有的分布式流处理编程模型相对较低级，往往需要用户关注系统中的状态一致性以及故障恢复问题。此外，那些提供故障恢复功能的模型通常成本较高，需要热备份或长时间的恢复过程。本文提出了一种新的编程模型——离散化流（Discretized Streams，简称D-Streams），它提供了高级别的函数式编程API，确保了强一致性，并实现了高效的故障恢复。 D-Streams通过引入一种新的恢复机制，提高了效率，超越了传统流数据库中的复制和上游备份解决方案。这种并行恢复机制可以在集群中并行恢复丢失的状态，从而显著提升了性能。D-Streams的设计目标是让用户能够在处理实时数据流时，无需过多关注底层的复杂性和容错性问题，而是专注于业务逻辑。 为了实现这一概念，研究者们在Spark集群计算框架的基础上开发了一个名为Spark Streaming的扩展，它使用户能够轻松地利用D-Streams进行流处理。Spark Streaming允许用户以批处理的方式来处理连续的数据流，从而简化了编程模型，同时也保持了实时处理的能力。 D-Streams的关键特性包括： 1. 高级函数式编程API：D-Streams提供了简洁且强大的编程接口，用户可以使用高级语言来描述数据流的转换和操作，无需关心底层的并发控制和容错细节。 2. 强一致性：通过设计保证了在处理实时数据时，系统状态的一致性，避免了数据不一致的问题。 3. 效率的故障恢复：D-Streams的并行恢复机制能够在出现故障时快速恢复，减少了系统的停机时间，提高了服务的可用性。 4. 容错性：在大规模集群环境中，D-Streams能够优雅地处理节点故障，确保系统的健壮性。 在Spark Streaming中，D-Streams被划分为微批次（micro-batches），这样既能实现近实时处理，又保留了Spark批处理的优点，如高效的内存管理和并行计算能力。这种方式使得D-Streams成为处理大规模实时数据的理想选择，尤其适用于需要高吞吐量和低延迟的场景。 总结来说，Discretized Streams是一种革新性的流处理模型，它结合了高级编程模型、强一致性保证和高效的故障恢复机制，旨在解决现有分布式流处理的挑战。通过在Spark框架上的实现，D-Streams为开发者提供了更强大、更易用的工具，以应对日益增长的大规模实时数据处理需求。

[Google 论文] MillWheel: Fault-Tolerant Stream Processing at Internet Scale

2013-08-27 上传
MillWheel is a framework for building low-latency data-processing applications that is widely used at Google. Users specify a directed computation graph and application code for individual nodes, and the system manages persistent state and the continuous flow of records, all within the envelope of the framework’s fault-tolerance guarantees.

Discretized Streams _ A Fault-Tolerant Model for Scalable Stream Processing

2018-10-07 上传
### Discretized Streams：一种容错的可扩展流处理模型 #### 概述 随着大数据技术的迅猛发展，“实时数据处理”成为众多应用的核心需求之一。然而，在分布式流处理领域，现有的编程模型往往较为底层，这导致用户...

Computational electromagnetics: the finite-difference time-domain method

2023-04-02 上传
Overall, the FDTD method is a powerful tool for analyzing electromagnetic phenomena and has found widespread use in a variety of fields, including telecommunications, radar, and electromagnetic ...

Discretize the numeric data using 10-bins method

2023-05-20 上传
2. Calculate the bin width: Calculate the difference between the maximum and minimum values, and divide it by the number of bins you want to create. In this case, we want to create 10 bins, so divide ...

Here are the detail information provided in PPTs:The option is an exotic partial barrier option written on an FX rate. The current value of underlying FX rate S0 = 1.5 (i.e. 1.5 units of domestic buys 1 unit of foreign). It matures in one year, i.e. T = 1. The option knocks out, if the FX rate:1 is greater than an upper level U in the period between between 1 month’s time and 6 month’s time; or,2 is less than a lower level L in the period between 8th month and 11th month; or,3 lies outside the interval [1.3, 1.8] in the final month up to the end of year.If it has not been knocked out at the end of year, the owner has the option to buy 1 unit of foreign for X units of domestic, say X = 1.4, then, the payoff is max{0, ST − X }.We assume that, FX rate follows a geometric Brownian motion dSt = μSt dt + σSt dWt , (20) where under risk-neutrality μ = r − rf = 0.03 and σ = 0.12.To simulate path, we divide the time period [0, T ] into N small intervals of length ∆t = T /N, and discretize the SDE above by Euler approximation St +∆t − St = μSt ∆t + σSt √∆tZt , Zt ∼ N (0, 1). (21) The algorithm for pricing this barrier option by Monte Carlo simulation is as described as follows:1 Initialize S0;2 Take Si∆t as known, calculate S(i+1)∆t using equation the discretized SDE as above;3 If Si+1 hits any barrier, then set payoff to be 0 and stop iteration, otherwise, set payoff at time T to max{0, ST − X };4 Repeat the above steps for M times and get M payoffs;5 Calculate the average of M payoffs and discount at rate μ;6 Calculate the standard deviation of M payoffs.

2023-06-02 上传
Based on the information provided in the PPTs, here is the Python code to simulate and price the partial barrier option using Monte Carlo simulation: ```python import numpy as np from scipy.stats ...

matlab如何通过等频法和等宽法将连续数据离散化为0,1类型数据

2023-06-07 上传
x_discretized = zeros(length(x), numBins); for i = 1:numBins x_discretized(:, i) = bin == i; end ``` 等宽法离散化： ```matlab % 生成随机数据 x = randn(100,1); % 设定分为5个区间 numBins = 5; % ...

什么是Discretized Stream

2023-04-28 上传
Discretized Stream（离散化流）是Apache Spark Streaming中的一个概念，它是由连续的数据流通过一定的时间间隔切割成一小段小的批次数据，并存储在RDD（Resilient Distributed Datasets）中。这些小批次数据可以被...

import pandas as pd import openpyxl # import matplotlib.pyplot as plt import numpy as np from sklearn.ensemble import AdaBoostClassifier from sklearn.model_selection import train_test_split # 打开Excel文件 wb = openpyxl.load_workbook('./处理过的训练集/987027.xlsx') # 选择需要读取的工作表 ws = wb['Sheet1'] # 读取第一列第二行之后的数据 data = [] for row in ws.iter_rows(min_row=2, min_col=1, values_only=True): data.append(row[0]) # 打印读取的数据 # print(data) # # 将浮点型数据按照等宽离散化的方法转化为离散型数据 # bin_edges = np.linspace(min(data), max(data), num=10) # discretized_data = np.digitize(data, bin_edges) # # 打印转化后的数据 # print(discretized_data) # 假设数据共有N个点，采样周期为0.25秒 N = len(data) t = np.arange(N) * 0.25 # labels2 = pd.cut(t, bins=10, labels=False) #组合时间序列和采样值 data1 = np.column_stack((t,data)) print(data1[:10]) # 打印前10行数据 # train_test_split函数用于将数据集划分为训练集和测试集，其中test_size参数指定了测试集所占的比例， # random_state参数指定了随机种子，以保证每次划分的结果相同。 X_train, X_test, y_train, y_test = train_test_split(data1[:, :-1], data1[:, -1], test_size=0.2, random_state=42) clf = AdaBoostClassifier(n_estimators=100, random_state=0) clf.fit(X_train, y_train) clf.predict([[0,0,0,0]]) clf.score(X_train, y_train)报错ValueError: X has 2 features, but AdaBoostClassifier is expecting 1 features as input.

2023-06-01 上传
from sklearn.model_selection import train_test_split # 打开Excel文件 wb = openpyxl.load_workbook('./处理过的训练集/987027.xlsx') # 选择需要读取的工作表 ws = wb['Sheet1'] # 读取第一列第二行之后的...

learned image compression with discretized gaussian mixture likelihoods and

2023-04-29 上传
学习了通过离散高斯混合似然值进行图像压缩 这是一种通过学习和利用离散高斯混合模型来进行图像压缩的方法。人们可以通过将图像中的像素颜色值转化为一个混合分布来对其进行表示。然后，使用迭代 algorithm 来确定...

MATLAB 神经网络输出离散值代码

2023-05-21 上传
disp(['Discretized value: ', num2str(discreteVal)]); ``` 在这个示例中，我们首先定义了离散化参数，包括要将输出值分成的离散化桶的数量（`numBins`）、最小值（`minVal`）、最大值（`maxVal`）以及每个桶的...
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