Automatically Detecting and Fixing Concurrency Bugs in Go Software Systems Extended Abstract
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ACID Compliant Distributed Key-Value Store
ACID Compliant Distributed Key-Value Store #1 #2 #3 Lakshmi Narasimhan Seshan , Rajesh Jalisatgi , Vijaeendra Simha G A # 1l [email protected] # 2r [email protected] #3v [email protected] Abstract thought that would be easier to implement. All Built a fault-tolerant and strongly-consistent key/value storage components run http and grpc endpoints. Http endpoints service using the existing RAFT implementation. Add ACID are for debugging/configuration. Grpc Endpoints are transaction capability to the service using a 2PC variant. Build used for communication between components. on the raftexample[1] (available as part of etcd) to add atomic Replica Manager (RM): RM is the service discovery transactions. This supports sharding of keys and concurrent transactions on sharded KV Store. By Implementing a part of the system. RM is initiated with each of the transactional distributed KV store, we gain working knowledge Replica Servers available in the system. Users have to of different protocols and complexities to make it work together. instantiate the RM with the number of shards that the user wants the key to be split into. Currently we cannot Keywords— ACID, Key-Value, 2PC, sharding, 2PL Transaction dynamically change while RM is running. New Replica Servers cannot be added or removed from once RM is I. INTRODUCTION up and running. However Replica Servers can go down Distributed KV stores have become a norm with the and come back and RM can detect this. Each Shard advent of microservices architecture and the NoSql Leader updates the information to RM, while the DTM revolution. Initially KV Store discarded ACID properties leader updates its information to the RM. -
Advanced Model Deployments with Tensorflow Serving Presentation.Pdf
Most models don’t get deployed. Hi, I’m Hannes. An inefficient model deployment import json from flask import Flask from keras.models import load_model from utils import preprocess model = load_model('model.h5') app = Flask(__name__) @app.route('/classify', methods=['POST']) def classify(): review = request.form["review"] preprocessed_review = preprocess(review) prediction = model.predict_classes([preprocessed_review])[0] return json.dumps({"score": int(prediction)}) Simple Deployments @app.route('/classify', methods=['POST']) Why Flask is insufficient def classify(): review = request.form["review"] ● No consistent APIs ● No consistent payloads preprocessed_review = preprocess(review) ● No model versioning prediction = model.predict_classes( ● No mini-batching support [preprocessed_review])[0] ● Inefficient for large models return json.dumps({"score": int(prediction)}) Image: Martijn Baudoin, Unsplash TensorFlow Serving TensorFlow Serving Production ready Model Serving ● Part of the TensorFlow Extended Ecosystem ● Used internally at Google ● Highly scalable model serving solution ● Works well for large models up to 2GB TensorFlow 2.0 ready! * * With small exceptions Deploy your models in 90s ... Export your Model import tensorflow as tf TensorFlow 2.0 Export tf.saved_model.save( ● Consistent model export model, ● Using Protobuf format export_dir="/tmp/saved_model", ● Export of graphs and signatures=None estimators possible ) $ tree saved_models/ Export your Model saved_models/ └── 1555875926 ● Exported model as Protobuf ├── assets (Saved_model.pb) -
An Evaluation of Tensorflow As a Programming Framework for HPC Applications
DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 An Evaluation of TensorFlow as a Programming Framework for HPC Applications WEI DER CHIEN KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE An Evaluation of TensorFlow as a Programming Framework for HPC Applications WEI DER CHIEN Master in Computer Science Date: August 28, 2018 Supervisor: Stefano Markidis Examiner: Erwin Laure Swedish title: En undersökning av TensorFlow som ett utvecklingsramverk för högpresterande datorsystem School of Electrical Engineering and Computer Science iii Abstract In recent years, deep-learning, a branch of machine learning gained increasing popularity due to their extensive applications and perfor- mance. At the core of these application is dense matrix-matrix multipli- cation. Graphics Processing Units (GPUs) are commonly used in the training process due to their massively parallel computation capabili- ties. In addition, specialized low-precision accelerators have emerged to specifically address Tensor operations. Software frameworks, such as TensorFlow have also emerged to increase the expressiveness of neural network model development. In TensorFlow computation problems are expressed as Computation Graphs where nodes of a graph denote operation and edges denote data movement between operations. With increasing number of heterogeneous accelerators which might co-exist on the same cluster system, it became increasingly difficult for users to program efficient and scalable applications. TensorFlow provides a high level of abstraction and it is possible to place operations of a computation graph on a device easily through a high level API. In this work, the usability of TensorFlow as a programming framework for HPC application is reviewed. -
From XML to Flat Buffers: Markup in the Twenty-Teens Warning! the Contenders
Elliotte Rusty Harold [email protected] August 2018 From XML to Flat Buffers: Markup in the Twenty-teens Warning! The Contenders ● XML ● JSON ● YAML ● EXI ● Protobufs ● Flat Protobufs XML JSON YAML EXI Protobuf Flat Buffers App Engine X X Standard Java App Engine X Flex What Uses What Kubernetes X X From technology, tools, and systems Eclipse X I use frequently. There are many others. Maven X Ant X Google X X X X X “APIs” Publishing X XML XML ● Very well defined standard ● By far the most general format: ○ Mixed content ○ Attributes and elements ● By far the best tool support. Nothing else is close: ○ XSLT ○ XPath ○ Many schema languages: ■ W3C XSD ■ RELAX NG More Reasons to Choose XML ● Most composable for mixing and matching markup; e.g. MathML+SVG in HTML ● Does not require a schema. ● Streaming support: very large documents ● Better for interchange amongst unrelated parties ● The deeper your needs the more likely you’ll end up here. Why Not XML? ● Relatively complex for simple tasks ● Limited to no support for non-string programming types: ○ Numbers, booleans, dates, money, etc. ○ Lists, maps, sets ○ You can encode all these but APIs don’t necessarily recognize or support them. ● Lots of sharp edges to surprise the non-expert: ○ 9/10 are namespace related ○ Attribute value normalization ○ White space ● Some security issues if you’re not careful (Billion laughs) JSON ● Simple for object serialization and program data. If your data is a few basic types (int, string, boolean, float) and data structures (list, map) this works well. ● More or less standard (7-8 of them in fact) ● Consumption libraries for essentially all significant languages Why Not JSON? ● It is surprising how fast needs grow past a few basic types and data structures. -
Grpc - a Solution for Rpcs by Google Distributed Systems Seminar at Charles University in Prague, Nov 2016 Jan Tattermusch - Grpc Software Engineer
gRPC - A solution for RPCs by Google Distributed Systems Seminar at Charles University in Prague, Nov 2016 Jan Tattermusch - gRPC Software Engineer About me ● Software Engineer at Google (since 2013) ● Working on gRPC since Q4 2014 ● Graduated from Charles University (2010) Contacts ● jtattermusch on GitHub ● Feedback to [email protected] @grpcio Motivation: gRPC Google has an internal RPC system, called Stubby ● All production applications use RPCs ● Over 1010 RPCs per second in total ● 4 generations over 13 years (since 2003) ● APIs for C++, Java, Python, Go What's missing ● Not suitable for external use (tight coupling with internal tools & infrastructure) ● Limited language & platform support ● Proprietary protocol and security ● No mobile support @grpcio What's gRPC ● HTTP/2 based RPC framework ● Secure, Performant, Multiplatform, Open Multiplatform ● Idiomatic APIs in popular languages (C++, Go, Java, C#, Node.js, Ruby, PHP, Python) ● Supports mobile devices (Android Java, iOS Obj-C) ● Linux, Windows, Mac OS X ● (web browser support in development) OpenSource ● developed fully in open on GitHub: https://github.com/grpc/ @grpcio Use Cases Build distributed services (microservices) Service 1 Service 3 ● In public/private cloud ● Google's own services Service 2 Service 4 Client-server communication ● Mobile ● Web ● Also: Desktop, embedded devices, IoT Access APIs (Google, OSS) @grpcio Key Features ● Streaming, Bidirectional streaming ● Built-in security and authentication ○ SSL/TLS, OAuth, JWT access ● Layering on top of HTTP/2 -
Trifacta Data Preparation for Amazon Redshift and S3 Must Be Deployed Into an Existing Virtual Private Cloud (VPC)
Install Guide for Data Preparation for Amazon Redshift and S3 Version: 7.1 Doc Build Date: 05/26/2020 Copyright © Trifacta Inc. 2020 - All Rights Reserved. CONFIDENTIAL These materials (the “Documentation”) are the confidential and proprietary information of Trifacta Inc. and may not be reproduced, modified, or distributed without the prior written permission of Trifacta Inc. EXCEPT AS OTHERWISE PROVIDED IN AN EXPRESS WRITTEN AGREEMENT, TRIFACTA INC. PROVIDES THIS DOCUMENTATION AS-IS AND WITHOUT WARRANTY AND TRIFACTA INC. DISCLAIMS ALL EXPRESS AND IMPLIED WARRANTIES TO THE EXTENT PERMITTED, INCLUDING WITHOUT LIMITATION THE IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND FITNESS FOR A PARTICULAR PURPOSE AND UNDER NO CIRCUMSTANCES WILL TRIFACTA INC. BE LIABLE FOR ANY AMOUNT GREATER THAN ONE HUNDRED DOLLARS ($100) BASED ON ANY USE OF THE DOCUMENTATION. For third-party license information, please select About Trifacta from the Help menu. 1. Quick Start . 4 1.1 Install from AWS Marketplace . 4 1.2 Upgrade for AWS Marketplace . 7 2. Configure . 8 2.1 Configure for AWS . 8 2.1.1 Configure for EC2 Role-Based Authentication . 14 2.1.2 Enable S3 Access . 16 2.1.2.1 Create Redshift Connections 28 3. Contact Support . 30 4. Legal 31 4.1 Third-Party License Information . 31 Page #3 Quick Start Install from AWS Marketplace Contents: Product Limitations Internet access Install Desktop Requirements Pre-requisites Install Steps - CloudFormation template SSH Access Troubleshooting SELinux Upgrade Documentation Related Topics This guide steps through the requirements and process for installing Trifacta® Data Preparation for Amazon Redshift and S3 through the AWS Marketplace. -
Database Software Market: Billy Fitzsimmons +1 312 364 5112
Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions -
Implementing and Testing Cockroachdb on Kubernetes
Implementing and testing CockroachDB on Kubernetes Albert Kasperi Iho Supervisors: Ignacio Coterillo Coz, Ricardo Brito Da Rocha Abstract This report will describe implementing CockroachDB on Kubernetes, a sum- mer student project done in 2019. The report addresses the implementation, monitoring, testing and results of the project. In the implementation and mon- itoring parts, a closer look to the full pipeline will be provided, with the intro- duction of all the frameworks used and the biggest problems that were faced during the project. After that, the report describes the testing process of Cock- roachDB, what tools were used in the testing and why. The results of testing will be presented after testing with the conclusion of results. Keywords CockroachDB; Kubernetes; benchmark. Contents 1 Project specification . 2 2 Implementation . 2 2.1 Kubernetes . 2 2.2 CockroachDB . 3 2.3 Setting up Kubernetes in CERN environment . 3 3 Monitoring . 3 3.1 Prometheus . 4 3.2 Prometheus Operator . 4 3.3 InfluxDB . 4 3.4 Grafana . 4 3.5 Scraping and forwarding metrics from the Kubernetes cluster . 4 4 Testing CockroachDB on Kubernetes . 5 4.1 SQLAlchemy . 5 4.2 Pgbench . 5 4.3 CockroachDB workload . 6 4.4 Benchmarking CockroachDB . 6 5 Test results . 6 5.1 SQLAlchemy . 6 5.2 Pgbench . 6 5.3 CockroachDB workload . 7 5.4 Conclusion . 7 6 Acknowledgements . 7 1 Project specification The core goal of the project was taking a look into implementing a database framework on top of Kuber- netes, the challenges in implementation and automation possibilities. Final project pipeline included a CockroachDB cluster running in Kubernetes with Prometheus monitoring both of them. -
Cloud Native Communication Patterns with Grpc
Cloud Native Communication Patterns with gRPC Kasun Indrasiri Author “gRPC Up and Running” and “Microservices for Enterprise” About Me ● Author “gRPC Up & Running”, “Microservices for Enterprise” ● Product Manager/Senior Director at WSO2. ● Committer and PMC member at Apache Software Foundation. ● Founder “Bay area Microservices, APIs and Integration” meetup group. What is gRPC? ● Modern Inter-process communication technology. ● Invoking remote functions as easy as making a local function invocation. ● Contract-first. ● Binary messaging on the wire on top of HTTP2 ● Polyglot. Fundamentals of gRPC - Service Definition syntax = "proto3"; ● Defines the business capabilities of package ecommerce; your service. service ProductInfo { rpc addProduct(Product) returns (ProductID); ● Protocol Buffers used as the IDL for rpc getProduct(ProductID) returns (Product); define services. } message Product { ● Protocol Buffers : string id = 1; ○ A language-agnostic, platform-neutral, string name = 2; extensible mechanism to serializing string description = 3; float price = 4; structured data. } ● Defines service, remote methods, and message ProductID { data types. string value = 1; } ProductInfo.proto Fundamentals of gRPC - gRPC Service // AddProduct implements ecommerce.AddProduct ● gRPC service implements the func (s *server) AddProduct(ctx context.Context, in *pb.Product) (*pb.ProductID, business logic. error) { ● Generate server side skeleton from // Business logic } service definition. // GetProduct implements ecommerce.GetProduct func (s *server) GetProduct(ctx -
Integrating R with the Go Programming Language Using Interprocess Communication
Integrating R with the Go programming language using interprocess communication Christoph Best, Karl Millar, Google Inc. [email protected] Statistical software in practice & production ● Production environments !!!= R development environment ○ Scale: machines, people, tools, lines of code… ● “discipline of software engineering” ○ Maintainable code, common standards and processes ○ Central problem: The programming language to use ● How do you integrate statistical software in production? ○ Rewrite everything in your canonical language? ○ Patch things together with scripts, dedicated servers, ... ? Everybody should just write Java! Programming language diversity ● Programming language diversity is hard … ○ Friction, maintenance, tooling, bugs, … ● … but sometimes you need to have it ○ Many statistics problems can “only” be solved in R* ● How do you integrate R code with production code? ○ without breaking production *though my colleagues keep pointing out that any Turing-complete language can solve any problem The Go programming language ● Open-source language, developed by small team at Google ● Aims to put the fun back in (systems) programming ● Fast compilation and development cycle, little “baggage” ● Made to feel like C (before C++) ● Made not to feel like Java or C++ (enterprise languages) ● Growing user base (inside and outside Google) Integration: Intra-process vs inter-process ● Intra-process: Link different languages through C ABI ○ smallest common denominator ○ issues: stability, ABI evolution, memory management, threads, … Can we do better? Or at least differently? ● Idea: Sick of crashes? Execute R in a separate process ○ Runs alongside main process, closely integrated: “lamprey” ● Provide communication layer between R and host process ○ A well-defined compact interface surface Integration: Intra-process vs inter-process C runtime Go R RPC client C++ runtime IPC Messages Java (library) Python RPC server .. -
How Financial Service Companies Can Successfully Migrate Critical
How Financial Service Companies Can Successfully Migrate Critical Applications to the Cloud Chapter 1: Why every bank should aspire to be the Google of FinServe PAGE 3 Chapter 2: Reducing latency and other delays that are detrimental to FSIs PAGE 6 Chapter 3: A look to the future of transaction services in the face of modernization PAGE 9 Copyright © 2021 RTInsights. All rights reserved. All other trademarks are the property of their respective companies. The information contained in this publication has been obtained from sources believed to be reliable. NACG LLC and RTInsights disclaim all warranties as to the accuracy, completeness, or adequacy of such information and shall have no liability for errors, omissions or inadequacies in such information. The information expressed herein is subject to change without notice. Copyright (©) 2021 RTInsights How Financial Service Companies Can Successfully Migrate Critical Applications to the Cloud 2 Chapter 1: Why every bank should aspire to be the Google of FinServe Introduction Fintech companies are disrupting the staid world of traditional banking. Financial services institutions (FSIs) are trying to keep pace, be competitive, and become relevant by modernizing operations by leveraging the same technologies as the aggressive fintech startups. Such efforts include a move to cloud and the increased use of data to derive real-time insights for decision-making and automation. While businesses across most industries are making similar moves, FSIs have different requirements than most companies and thus must address special challenges. For example, keeping pace with changing customer desires and market conditions is driving the need for more flexible and highly scalable options when it comes to developing, deploying, maintaining, and updating applications. -
SESSION 2 YANG, Openconfig, and Gnmi
SESSION 2 YANG, OpenConfig, and gNMI Copyright © 2019 - Open Networking Foundation Session 2 Overview 1. YANG: Configuration Modeling Language 2. OpenConfig: Configuration and telemetry model instances 3. gNMI: Runtime Configuration and Monitoring Interface 4. gNOI: Runtime Operations Interface Copyright © 2019 - Open Networking Foundation YANG Overview YANG is a data modeling language for network configuration ● Used to express the structure of data, NOT the data itself ● Instances of data can be expressed in XML, JSON, Protobuf, etc. and are considered valid if they adhere to the YANG data model (schema) From a YANG model, we care about 2 things: 1. Data tree organization (from which we get the paths and leaf data types) 2. Semantics of the leaf nodes (from the description field, usually in English) History: YANG was originally designed as a data modeling language for NETCONF. It borrows the syntactic structure and base types from SMIng, which is an evolution of SMI, the data modeling language for SNMP used for MIBs. Copyright © 2019 - Open Networking Foundation YANG Module A module is a self-contained tree of nodes. Modules are the smallest unit that can be “compiled” by YANG tools. // A module is a self-contained tree of nodes module demo-port { A module contains: // YANG Boilerplate ● boilerplate, like a namespace, yang-version "1"; prefix for reference in other namespace "https://opennetworking.org/yang/demo"; modules, description, version / prefix "demo-port"; description "Demo model for managing ports"; revision history, etc. revision "2019-09-10" { ● identities and derived types description "Initial version"; ● modular groupings reference "1.0.0"; } ● a top-level container that defines tree of data nodes // ..