-Introduction to sample problem statement
-Which Graph database is used and why
-Installing Titan
-Titan with Cassandra
-The Gremlin Cassandra script: A way to store data in cassandra from Titan Gremlin
-Accessing Titan with Spark
Optimizing Terascale Machine Learning Pipelines with Keystone MLSpark Summit
The document describes KeystoneML, an open source software framework for building scalable machine learning pipelines on Apache Spark. It discusses standard machine learning pipelines and examples of more complex pipelines for image classification, text classification, and recommender systems. It covers features of KeystoneML like transformers, estimators, and chaining estimators and transformers. It also discusses optimizing pipelines by choosing solvers, caching intermediate data, and operator selection. Benchmark results show KeystoneML achieves state-of-the-art accuracy on large datasets faster than other systems through end-to-end pipeline optimizations.
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...MLconf
Spark and GraphX in the Netflix Recommender System: We at Netflix strive to deliver maximum enjoyment and entertainment to our millions of members across the world. We do so by having great content and by constantly innovating on our product. A key strategy to optimize both is to follow a data-driven method. Data allows us to find optimal approaches to applications such as content buying or our renowned personalization algorithms. But, in order to learn from this data, we need to be smart about the algorithms we use, how we apply them, and how we can scale them to our volume of data (over 50 million members and 5 billion hours streamed over three months). In this talk we describe how Spark and GraphX can be leveraged to address some of our scale challenges. In particular, we share insights and lessons learned on how to run large probabilistic clustering and graph diffusion algorithms on top of GraphX, making it possible to apply them at Netflix scale.
Spark Streaming: Pushing the throughput limits by Francois Garillot and Gerar...Spark Summit
This document discusses Spark Streaming and how it can push throughput limits in a reactive way. It describes how Spark Streaming works by breaking streams into micro-batches and processing them through Spark. It also discusses how Spark Streaming can be made more reactive by incorporating principles from Reactive Streams, including composable back pressure. The document concludes by discussing challenges like data locality and providing resources for further information.
This document summarizes a presentation about productionizing streaming jobs with Spark Streaming. It discusses:
1. The lifecycle of a Spark streaming application including how data is received in batches and processed through transformations.
2. Best practices for aggregations including reducing over windows, incremental aggregation, and checkpointing.
3. How to achieve high throughput by increasing parallelism through more receivers and partitions.
4. Tips for debugging streaming jobs using the Spark UI and ensuring processing time is less than the batch interval.
Strava Labs: Exploring a Billion Activity Dataset from Athletes with Apache S...Databricks
At Strava we have extensively leveraged Apache Spark to explore our data of over a billion activities, from tens of millions of athletes. This talk will be a survey of the more unique and exciting applications: A Global Heatmap gives a ~2 meter resolution density map of one billion runs, rides, and other activities consisting of three trillion GPS points from 17 billion miles of exercise data. The heatmap was rewritten from a non-scalable system into a highly scalable Spark job enabling great gains in speed, cost, and quality. Locally sensitive hashing for GPS traces was used to efficiently cluster 1 billion activities. Additional processes categorize and extract data from each cluster, such as names and statistics. Clustering gives an automated process to extract worldwide geographical patterns of athletes.
Applications include route discovery, recommendation systems, and detection of events and races. A coarse spatiotemporal index of all activity data is stored in Apache Cassandra. Spark streaming jobs maintain this index and compute all space-time intersections (“flybys”) of activities in this index. Intersecting activity pairs are then checked for spatiotemporal correlation, indicated by connected components in the graph of highly correlated pairs form “Group Activities”, creating a social graph of shared activities and workout partners. Data from several hundred thousand runners was used to build an improved model of the relationship between running difficulty and elevation gradient (Grade Adjusted Pace).
Structuring Spark: DataFrames, Datasets, and Streaming by Michael ArmbrustSpark Summit
This document summarizes Spark's structured APIs including SQL, DataFrames, and Datasets. It discusses how structuring computation in Spark enables optimizations by limiting what can be expressed. The structured APIs provide type safety, avoid errors, and share an optimization and execution pipeline. Functions allow expressing complex logic on columns. Encoders map between objects and Spark's internal data format. Structured streaming provides a high-level API to continuously query streaming data similar to batch queries.
This document discusses using Apache Spark and Cassandra for IoT applications. Cassandra is a distributed database that is highly available, horizontally scalable, and supports multiple datacenters with no single point of failure. It is well-suited for storing time series sensor data. Spark can be used for both batch and stream processing of data in Cassandra. The Spark Cassandra Connector allows Cassandra tables to be accessed as Spark RDDs. Real-time sensor data can be ingested using Spark Streaming and stored in Cassandra. Common use cases with this architecture include real-time analytics on streaming data and batch analytics on historical sensor data.
Data processing platforms architectures with Spark, Mesos, Akka, Cassandra an...Anton Kirillov
This talk is about architecture designs for data processing platforms based on SMACK stack which stands for Spark, Mesos, Akka, Cassandra and Kafka. The main topics of the talk are:
- SMACK stack overview
- storage layer layout
- fixing NoSQL limitations (joins and group by)
- cluster resource management and dynamic allocation
- reliable scheduling and execution at scale
- different options for getting the data into your system
- preparing for failures with proper backup and patching strategies
Data Pipelines & Integrating Real-time Web Services w/ Storm : Improving on t...Brian O'Neill
This presentation covers our use of Storm and the connectors we've built. It also proposes a design for integrating Storm with real-time web services by embedding parts of topologies directly into the web services layer.
Real-time Machine Learning Analytics Using Structured Streaming and Kinesis F...Databricks
Persisting data from Amazon Kinesis using Amazon Kinesis Firehose is a popular pattern for streaming projects. However, building real-time analytics on these data introduces challenges, including managing the format, size and frequency of the files created.
This session will present an end-to-end use case for deploying machine learning streaming analytics at-scale using Structured Streaming on Databricks. We will deploy a high-volume Kinesis producer, persist the data to S3 using Kinesis Firehose, partition and write the data using Parquet, create a machine learning model and, finally, query and visualize the data in real time.
Key takeaways include:
– Create a Kinesis producer
– Persist to S3 using Kinesis Firehose
– ETL, machine learning, and exploratory data analysis using Structured Streaming
Expanding Apache Spark Use Cases in 2.2 and Beyond with Matei Zaharia and dem...Databricks
2017 continues to be an exciting year for big data and Apache Spark. I will talk about two major initiatives that Databricks has been building: Structured Streaming, the new high-level API for stream processing, and new libraries that we are developing for machine learning. These initiatives can provide order of magnitude performance improvements over current open source systems while making stream processing and machine learning more accessible than ever before.
Analyzing Time Series Data with Apache Spark and CassandraPatrick McFadin
You have collected a lot of time series data so now what? It's not going to be useful unless you can analyze what you have. Apache Spark has become the heir apparent to Map Reduce but did you know you don't need Hadoop? Apache Cassandra is a great data source for Spark jobs! Let me show you how it works, how to get useful information and the best part, storing analyzed data back into Cassandra. That's right. Kiss your ETL jobs goodbye and let's get to analyzing. This is going to be an action packed hour of theory, code and examples so caffeine up and let's go.
A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...Spark Summit
Clustering is often an essential first step in datamining intended to reduce redundancy, or define data categories. Hierarchical clustering, a widely used clustering technique, can
offer a richer representation by suggesting the potential group
structures. However, parallelization of such an algorithm is challenging as it exhibits inherent data dependency during the hierarchical tree construction. In this paper, we design a
parallel implementation of Single-linkage Hierarchical Clustering by formulating it as a Minimum Spanning Tree problem. We further show that Spark is a natural fit for the parallelization of
single-linkage clustering algorithm due to its natural expression
of iterative process. Our algorithm can be deployed easily in
Amazon’s cloud environment. And a thorough performance
evaluation in Amazon’s EC2 verifies that the scalability of our
algorithm sustains when the datasets scale up.
Sketching Data with T-Digest In Apache Spark: Spark Summit East talk by Erik ...Spark Summit
Algorithms for sketching probability distributions from large data sets are a fundamental building block of modern data science. Sketching plays a role in diverse applications ranging from visualization, optimizing data encodings, estimating quantiles, data synthesis and imputation. The T-Digest is a versatile sketching data structure. It operates on any numeric data, models tricky distribution tails with high fidelity, and most crucially it works smoothly with aggregators and map-reduce.
T-Digest is a perfect fit for Apache Spark; it is single-pass and intermediate results can be aggregated across partitions in batch jobs or aggregated across windows in streaming jobs. In this talk I will describe a native Scala implementation of the T-Digest sketching algorithm and demonstrate its use in Spark applications for visualization, quantile estimations and data synthesis.
Attendees of this talk will leave with an understanding of data sketching with T-Digest sketches, and insights about how to apply T-Digest to their own data analysis applications.
Deep Dive Into Catalyst: Apache Spark 2.0’s OptimizerDatabricks
Catalyst is becoming one of the most important components in Apache Spark, as it underpins all the major new APIs in Spark 2.0, from DataFrames, Datasets, to streaming. At its core, Catalyst is a general library for manipulating trees. Based on this library, we have built a modular compiler frontend for Spark, including a query analyzer, optimizer, and an execution planner. In this talk, I will first introduce the concepts of Catalyst trees, followed by major features that were added in order to support Spark’s powerful API abstractions. Audience will walk away with a deeper understanding of how Spark 2.0 works under the hood.
A Deep Dive into Stateful Stream Processing in Structured Streaming with Tath...Databricks
Stateful processing is one of the most challenging aspects of distributed, fault-tolerant stream processing. The DataFrame APIs in Structured Streaming make it very easy for the developer to express their stateful logic, either implicitly (streaming aggregations) or explicitly (mapGroupsWithState). However, there are a number of moving parts under the hood which makes all the magic possible. In this talk, I am going to dive deeper into how stateful processing works in Structured Streaming.
In particular, I’m going to discuss the following.
• Different stateful operations in Structured Streaming
• How state data is stored in a distributed, fault-tolerant manner using State Stores
• How you can write custom State Stores for saving state to external storage systems.
Chris Hillman – Beyond Mapreduce Scientific Data Processing in Real-timeFlink Forward
This document discusses processing scientific mass spectrometry data in real-time using parallel and distributed computing techniques. It describes how a mass spectrometry experiment produces terabytes of data that currently takes over 24 hours to fully process. The document proposes using MapReduce and Apache Flink to parallelize the data processing across clusters to help speed it up towards real-time analysis. Initial tests show Flink can process the data 2-3 times faster than traditional Hadoop MapReduce. Finally, it discusses simulating real-time streaming of the data using Kafka and Flink Streaming to enable processing results within 10 seconds of the experiment completing.
Accumulo Summit 2015: Using D4M for rapid prototyping of analytics for Apache...Accumulo Summit
D4M is a software tool that connects scientists with big data technologies like Apache Accumulo. The D4M-Accumulo binding provides high performance connectivity to Accumulo for quick analytic prototyping. Current research looks to implement GraphBLAS server-side iterators and operators on Accumulo tables to support high performance graph analytics.
Building a Graph of all US Businesses Using Spark Technologies by Alexis RoosSpark Summit
This document discusses building a graph of U.S. businesses using Spark technologies. It describes how Radius Intelligence builds a comprehensive business graph from multiple data sources by acquiring and preparing raw data, clustering records, and constructing the graph by linking business and location vertices and attributes through techniques like connected components analysis. Key lessons learned include that GraphX scales well, graph construction and updates are easy using RDD operations, and connected components analysis is an expensive graph operation.
Apache Cassandra for Timeseries- and Graph-DataGuido Schmutz
Apache Cassandra has proven to be one of the best solutions for storing and retrieving data at high velocity and high volume.
In the first part of the talk we will discuss how the storage model of Cassandra is ideal for time series use cases, which are often of high velocity and high volume. Time series data is everywhere today: Internet of Things, sensor data, transactional data, social media streams. We go over examples of how to best build data models.
We will also cover pairing Apache Spark with Apache Cassandra to create a real time data analytics platform.
The second part of the talk will present Titan:db, an open source distributed graph database build on top of Cassandra that can power real-time applications with thousands of concurrent users over graphs with billions of edges. It exposes a property graph data model directly atop Cassandra which makes storing and querying relationship data fast, easy, and scalable to huge graphs. This talk demonstrates how Titan's features enable complex, multi-relational databases in Cassandra and discusses how Titan:db has been used in a customer case to store social network data.
Graphs are everywhere! Distributed graph computing with Spark GraphXAndrea Iacono
This document discusses GraphX, a graph processing system built on Apache Spark. It defines what graphs are, including vertices and edges. It explains that GraphX uses Resilient Distributed Datasets (RDDs) to keep data in memory for iterative graph algorithms. GraphX implements the Pregel computational model where each vertex can modify its state, receive and send messages to neighbors each superstep until halting. The document provides examples of graph algorithms and notes when GraphX is well-suited versus a graph database.
Big Graph Analytics on Neo4j with Apache SparkKenny Bastani
In this talk I will introduce you to a Docker container that provides you an easy way to do distributed graph processing using Apache Spark GraphX and a Neo4j graph database. You'll learn how to analyze big data graphs that are exported from Neo4j and consequently updated from the results of a Spark GraphX analysis. The types of analysis I will be talking about are PageRank, connected components, triangle counting, and community detection.
Database technologies have evolved to be able to store big data, but are largely inflexible. For complex graph data models stored in a relational database there may be tedious transformations and shuffling around of data to perform large scale analysis.
Fast and scalable analysis of big data has become a critical competitive advantage for companies. There are open source tools like Apache Hadoop and Apache Spark that are providing opportunities for companies to solve these big data problems in a scalable way. Platforms like these have become the foundation of the big data analysis movement.
Speakers
Graph analytics can be used to analyze a social graph constructed from email messages on the Spark user mailing list. Key metrics like PageRank, in-degrees, and strongly connected components can be computed using the GraphX API in Spark. For example, PageRank was computed on the 4Q2014 email graph, identifying the top contributors to the mailing list.
Building a Location Based Social Graph in Spark at InMobi-(Seinjuti Chatterje...Spark Summit
This document discusses building location-based social groups using Spark at InMobi. It begins with an overview of InMobi and their data collection through mobile ads. It then discusses using location data, point of interest classification, and connected components analysis in Spark to identify frequent co-visitations between locations and group them into location-based social graphs. Examples of identified groups include university students, business travelers at airports, and people who frequently visit stores and coffee shops near their work. The document concludes by noting InMobi's migration to using Spark for more of their data processing needs.
This document discusses temporal graphs, which are graphs where nodes and edges are active for specific time instances. It provides examples of temporal graphs and compares them to non-temporal graphs. It then covers temporal graph traversal methods like depth-first search and breadth-first search, accounting for temporal constraints. Various path types in temporal graphs are defined, such as foremost paths, latest-departure paths, and fastest paths. Algorithms for finding these paths using a stream representation or graph transformation approach are outlined.
This document discusses websockets and web workers for building low latency interactive web applications. It covers the problems with traditional techniques like polling and describes how websockets enable full-duplex communication in a scalable way without overhead. Web workers allow running JavaScript in a non-blocking way by using multiple threads to improve performance of computationally intensive tasks.
By Mr. Praveen R
Content
-What are we solving?
-Money weighted rate of
return (MWRR)
-MWRR Example
-Newton-Raphson Solver
-Demo
-Spark
-Apache Common Solvers
This document discusses using Mesos and Spark together to build a fault tolerant data pipeline. It outlines the architecture of connecting data sources to the pipeline, implementing multiple pipelines, ensuring high availability through Mesos, and performance monitoring. Learnings and a conclusion are also presented.
Building high scalable distributed framework on apache mesosSigmoid
By Mr. Rahul Kumar
Content
-Mesos Intro
-Software Projects Built on Mesos
-Create own Framework
-Why Mesos
-Protocol Buffer
-The Scheduler
-The Executor
-Mesos Endpoints
This document discusses best practices for productionizing Spark applications including ensuring stability, maintainability, and scalability. It recommends approaches for fault tolerance such as auto-healing connection pools and output monitoring. For stability, it advocates testing and avoiding outages. For maintainability, it stresses modularity, pulling logic out of data pipelines, using monoids for aggregation, and separating concerns into reusable components.
Failsafe Hadoop Infrastructure and the way they workSigmoid
Impact
Different Kinds Of HA Configurations
HDFS HA - Necessary Hardware Resources
HDFS HA Architecture Using The Quorum Journal Manager
RM HA -Necessary Hardware Resources
Resource Manager HA Architecture
RM Failover
Spark and Spark Streaming internals allow for low latency, fault tolerance, and diverse workloads. Spark uses a Resilient Distributed Dataset (RDD) model where data is partitioned across a cluster. A directed acyclic graph (DAG) is used to schedule tasks across stages in an optimized way. Spark Streaming runs streaming computations as small deterministic batch jobs by chopping live streams into batches and processing them using RDD transformations and actions.
Sparkstreaming with kafka and h base at scale (1)Sigmoid
This document discusses Spark streaming with Kafka and HBase integration. It provides an overview of Spark, Spark Streaming, and how to receive streaming data using Spark Streaming with Kafka. It also discusses tips for creating a scalable pipeline and how to integrate HBase, including reading and writing data from and to HBase.
GRAPH 101- GETTING STARTED WITH TITAN AND CASSANDRAShaunak Das
This document provides an introduction to working with graphs using Titan and Cassandra. It discusses what a graph database is, provides an example of an Amazon product graph schema, and demonstrates how to get started with Titan by loading sample data into a Cassandra backend. It also discusses potential graph queries that could be performed on the sample Amazon data and outlines topics for future sessions, such as defining a graph schema, using Hadoop/Spark for querying and loading graphs, and taking suggestions for additional topics.
Cassandra is a distributed key-value database inspired by Amazon's Dynamo and Google's Bigtable. It uses a gossip-based protocol for node communication and consistent hashing to partition and replicate data across nodes. Cassandra stores data in memory (memtables) and on disk (SSTables), uses commit logs for crash recovery, and is highly available with tunable consistency.
The document discusses Cassandra's data model and how it replaces HDFS services. It describes:
1) Two column families - "inode" and "sblocks" - that replace the HDFS NameNode and DataNode services respectively, with "inode" storing metadata and "sblocks" storing file blocks.
2) CFS reads involve reading the "inode" info to find the block and subblock, then directly accessing the data from the Cassandra SSTable file on the node where it is stored.
3) Keyspaces are containers for column families in Cassandra, and the NetworkTopologyStrategy places replicas across data centers to enable local reads and survive failures.
This presentation explains how to get started with Apache Cassandra to provide a scale out, fault tolerant backend for inventory storage on OpenSimulator.
The document discusses Apache Cassandra, a distributed database management system designed to handle large amounts of data across many commodity servers. It was developed at Facebook and modeled after Google's Bigtable. The summary discusses key concepts like its use of consistent hashing to distribute data, support for tunable consistency levels, and focus on scalability and availability over traditional SQL features. It also provides an overview of how Cassandra differs from relational databases by not supporting joins, having an optional schema, and using a prematerialized and transaction-less model.
P. Taylor Goetz gave a presentation on using Storm and Cassandra at Health Market Science. He discussed how HMS uses Cassandra for master data management and real-time analytics. He then provided an overview of Storm and how it can be used to build high throughput data processing pipelines. Goetz demonstrated how the storm-cassandra library allows writing and reading Storm tuples from Cassandra in real-time. He closed by discussing future plans to support CQL and enhance Trident integration.
C*ollege Credit: CEP Distribtued Processing on Cassandra with StormDataStax
Cassandra provides facilities to integrate with Hadoop. This is sufficient for distributed batch processing, but doesn’t address CEP distributed processing. This webinar will demonstrate use of Cassandra in Storm. Storm provides a data flow and processing layer that can be used to integrate Cassandra with other external persistences mechanisms (e.g. Elastic Search) or calculate dimensional counts for reporting and dashboards. We’ll dive into a sample Storm topology that reads and writes from Cassandra using storm-cassandra bolts.
Cassandra - A decentralized storage systemArunit Gupta
Cassandra uses consistent hashing to partition and distribute data across nodes in the cluster. Each node is assigned a random position on a ring based on the hash value of the partition key. This allows data to be evenly distributed when nodes join or leave. Cassandra replicates data across multiple nodes for fault tolerance and high availability. It supports different replication policies like rack-aware and datacenter-aware replication to ensure replicas are not co-located. Membership and failure detection in Cassandra uses a gossip protocol and scuttlebutt reconciliation to efficiently discover nodes and detect failures in the distributed system.
This document provides an overview of Cassandra, including:
- Cassandra is a distributed, column-oriented database that is highly scalable and has no single point of failure.
- It compares Cassandra to relational databases, noting Cassandra's flexible schema and lack of joins.
- The architecture includes keyspaces, tables and columns, with replication specified at the keyspace level.
- Queries in Cassandra Query Language (CQL) have limitations compared to other databases.
Cassandra is a scalable, decentralized NoSQL database. It resolves issues with relational databases like scaling limitations, single points of failure, and poor performance under high loads. Cassandra uses a peer-to-peer distributed architecture and replication across multiple data centers to provide high availability and linear scalability without performance degradation. It uses a column-oriented data model with no schema enforcement, allowing dynamic columns per row.
Presented to the Dublin Cassandra User Group by Niall Milton of DigBigData. This presentation is on Cassandra and its use with other technologies such as Storm, Spark, Hadoop, ElasticSearch and Redis. This presentation should act as a solid foundation to explore some of the mentioned technologies in more depth.
Running Presto and Spark on the Netflix Big Data PlatformEva Tse
This document summarizes Netflix's big data platform, which uses Presto and Spark on Amazon EMR and S3. Key points:
- Netflix processes over 50 billion hours of streaming per quarter from 65+ million members across over 1000 devices.
- Their data warehouse contains over 25PB stored on S3. They read 10% daily and write 10% of reads.
- They use Presto for interactive queries and Spark for both batch and iterative jobs.
- They have customized Presto and Spark for better performance on S3 and Parquet, and contributed code back to open source projects.
- Their architecture leverages dynamic EMR clusters with Presto and Spark deployed via bootstrap actions for scalability.
This document provides an overview of NoSQL databases, including:
- NoSQL databases are non-relational and do not require fixed schemas like SQL databases.
- They are useful for large, unstructured datasets and provide high scalability and availability.
- Cassandra is a popular open-source NoSQL database that uses a column-oriented data model and eventual consistency.
- Hector is a Java client that provides an API for Cassandra and handles connection pooling.
- NoSQL databases sacrifice features like joins and ACID transactions in exchange for scalability and high availability.
(BDT303) Running Spark and Presto on the Netflix Big Data PlatformAmazon Web Services
In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage Amazon EMR as a generic framework for data-processing cluster management.
Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above.
This document provides an overview of Cassandra, including its data model, APIs, architecture, partitioning, replication, consistency, failure handling, and local persistence. Cassandra is a distributed database modeled after Amazon's Dynamo and Google's Bigtable. It uses a gossip-based protocol for cluster management and provides tunable consistency levels.
Time series data monitoring at 99acres.comRavi Raj
The document describes the current single box setup for 99acres.com monitoring which includes Carbon, Whisper, and Graphite Web. Carbon receives metrics and flushes them to Whisper. Whisper is a flat-file database that stores each metric in a separate file. Graphite Web is a Django UI that queries Carbon and Whisper to return and graph metrics data. The proposed final approach adds a Carbon-Relay box and dedicated Graphite Web box for load balancing and fault tolerance across multiple Graphite storage nodes.
Maximum Overdrive: Tuning the Spark Cassandra Connector (Russell Spitzer, Dat...DataStax
Worried that you aren't taking full advantage of your Spark and Cassandra integration? Well worry no more! In this talk we'll take a deep dive into all of the available configuration options and see how they affect Cassandra and Spark performance. Concerned about throughput? Learn to adjust batching parameters and gain a boost in speed. Always running out of memory? We'll take a look at the various causes of OOM errors and how we can circumvent them. Want to take advantage of Cassandra's natural partitioning in Spark? Find out about the recent developments that let you perform shuffle-less joins on Cassandra-partitioned data! Come with your questions and problems and leave with answers and solutions!
About the Speaker
Russell Spitzer Software Engineer, DataStax
Russell Spitzer received a Ph.D in Bio-Informatics before finding his deep passion for distributed software. He found the perfect outlet for this passion at DataStax where he began on the Automation and Test Engineering team. He recently moved from finding bugs to making bugs as part of the Analytics team where he works on integration between Cassandra and Spark as well as other tools.
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Real-Time Stock Market Analysis using Spark StreamingSigmoid
This document proposes using support vector machines (SVMs) to model high-frequency limit order book dynamics and predict metrics like mid-price movement and price spread crossing. It describes representing each limit order book entry as a vector of attributes, then using multi-class SVMs to build models for each metric. Experiments on real data show the selected features are effective for short-term price forecasts. The document provides background on SVMs, describing how they find an optimal separating hyperplane to classify data points into labels.
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This document discusses text analysis of news documents. It outlines the architecture used, which includes crawling documents, preprocessing the text, identifying concepts and relations to build a knowledge graph. The pipeline involves two phases - identifying concepts in phase I and relations between concepts in phase II. The knowledge graph represents concepts as vertices and relations as edges. This is used to build a news explorer application that allows users to search for and explore topics and related concepts.
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This document provides an agenda for a dashboard design workshop. It will cover Gestalt principles of visual perception that inform effective dashboard design, such as proximity, closure, and similarity. It will also discuss key considerations for dashboard design like arranging data by importance, maintaining a high data-to-ink ratio, using appropriate display methods, and ensuring aesthetic appeal. Finally, it includes an exercise where participants will design a sample dashboard based on a given user objective and available data.
Introduction to Spark R with R studio - Mr. Pragith Sigmoid
R is a programming language and software environment for statistical computing and graphics.
The R language is widely used among statisticians and data miners for developing statistical
software and data analysis.
RStudio IDE is a powerful and productive user interface for R.
It’s free and open source, and available on Windows, Mac, and Linux.
• Distributed datasets loaded into named columns (similar to relational DBs or
Python DataFrames).
• Can be constructed from existing RDDs or external data sources.
• Can scale from small datasets to TBs/PBs on multi-node Spark clusters.
• APIs available in Python, Java, Scala and R.
• Bytecode generation and optimization using Catalyst Optimizer.
• Simpler DSL to perform complex and data heavy operations.
• Faster runtime performance than vanilla RDDs.
1. Spark Streaming uses Kafka receivers to process data from Kafka in batches at regular intervals. The receivers divide the streams into blocks and write them to Spark's block manager.
2. When processing batches, Spark retrieves the blocks from the block manager and runs jobs on the RDDs created from the blocks.
3. Reliable receivers are needed to handle failures of receivers or the driver, to prevent data loss. The Spark package provides a low-level Kafka receiver implementation that reliably handles offsets and failures.
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Real Time search using Spark and ElasticsearchSigmoid
This document discusses using Spark Streaming and Elasticsearch to enable real-time search and analysis of streaming data. Spark Streaming processes and enriches streaming data and stores it in Elasticsearch for low-latency search and alerts. The elasticsearch-hadoop connector allows Spark jobs to read from and write to Elasticsearch, integrating the batch processing of Spark with the real-time search of Elasticsearch.
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Amazon DocumentDB(MongoDB와 호환됨)는 빠르고 안정적이며 완전 관리형 데이터베이스 서비스입니다. Amazon DocumentDB를 사용하면 클라우드에서 MongoDB 호환 데이터베이스를 쉽게 설치, 운영 및 규모를 조정할 수 있습니다. Amazon DocumentDB를 사용하면 MongoDB에서 사용하는 것과 동일한 애플리케이션 코드를 실행하고 동일한 드라이버와 도구를 사용하는 것을 실습합니다.
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2. Content
Introduction to sample problem statement
Which Graph database is used and why
Installing Titan
Titan with Cassandra
The Gremlin Cassandra script: A way to store data in cassandra from Titan
Gremlin
Accessing Titan with Spark
3. Introducing which kind of time-series
problems that can be solved using
graph analytics and introducing the
problem statement which I have been
working on solving.
4. The dynamics of a complex system is usually
recorded in the form of time series. In recent
years, the visibility graph algorithm and the
horizontal visibility graph algorithm have been
recently introduced as the mapping between
time series and complex networks.
Transforming time series into the graphs, the
algorithms allows applying the methods of
graph theoretical tools for characterizing time
series, opening the possibility of building
fruitful connections between time series
analysis, nonlinear dynamics, and graph theory.
5. The problem statement which I have been working on:
Our initial goal was finding anomalies in the given timeseries. We started
with slicing the data into small meaningfull parts so that the data becomes
smaller and contiguous data points having the same property get clubbed
together.
Later we created a graph with these parts and tried to find the parts having
similar properties. Doing this will single out anomalies which was the goal.
6. Which graph database I used and why
i.e. Difference between graphX, neo4j
and titan , why we used titan
7. Titan vs graphX
The fundamental difference between Titan and GraphX lies in how they
persistence data and how they process data, Titan by default persists data
(vertices and edges and properties) to a distributed data store in the form of
tables bound to a specific schema. This schema can be stored in Berkley DB
tables, Cassandra tables or Hbase tables.
In the case of Titan the graph is stored as vertices in a vertex table and edges
in an edge table.
GraphX has no real persistence layer (yet?), yes it can persist to HDFS files, but
it cannot persist to a distributed datastore in a common schema like form as in
Titan’s case.
A graph only exists in GraphX when it is loaded into memory off raw data and
interpreted as Graph RDDs, Titan stores the graph permanently.
The other major difference between the two solutions is how they process
graph data.
By default, GraphX solves queries via distributed processing on many nodes in
parallel where possible as opposed to Titan processing pipelines on a single
node. Titan can also take advantage of parallel processing via Faunus/HDFS if
necessary.
8. Neo4j vs titan
The primary difference between Titan and Neo4j is scalability: Titan can distribute the graph
across multiple machines (using either Cassandra or Hbase as the storage backend) which
does three things:
1) It allows Titan to store really, really large graphs by scaling out
2) It allows Titan to handle massive write loads
3) No single point of failure for very high availability. While Neo4j's HA setup gives you
redundancy through replication, death of the master in such a setup leads to temporary
service interruption while a new master is elected.
This allows Titan to scale to large deployments with thousands of concurrent users as
demonstrated in the benchmark:
http://thinkaurelius.com/2012/08/06/titan-provides-real-time-big-graph-data/
Neo4j has been around much longer and is therefore more "mature" in some regards:
1) Integrated with an external lucene index gives it more sophisticated search capabilities
2) More integration points into other programming languages and development frameworks
(e.g. spring)
3) Has been used by more people
Titan is a native blueprints implementation and benefits from all the features that come with
the Tinkerpop stack. Titan does not support cypher but only the gremlin standard.
9. Intro to Titan graph db and how to
use it with cassandra & spark
10. Installing Titan
Downloaded the latest prebuilt version (0.9.0-M2) of Titan at
s3.thinkaurelius.com/downloads/titan/titan-0.9.0-M2-hadoop1.zip.
Carry out the following steps to ensure that Titan is installed on each node
in the cluster:
11. Now, use the Linux su (switch user) command to change to the root
account, and move the install to the /usr/local/ location. Change the file
and group membership of the install to the hadoop user, and create a
symbolic link called titan so that the current Titan release can be referred
to as the simplified path called /usr/local/titan:
12. Titan with Cassandra
In this section, the Cassandra NoSQL database will be used as a storage
mechanism for Titan. Although it does not use Hadoop, it is a large-scale,
cluster-based database in its own right, and can scale to very large cluster
sizes. A graph will be created, and stored in Cassandra using the Titan
Gremlin shell. It will then be checked using Gremlin, and the stored data
will be checked in Cassandra. The raw Titan Cassandra graph-based data
will then be accessed from Spark. The first step then will be to install
Cassandra on each node in the cluster.
13. Install Cassandra on all the nodes
Set up the Cassandra configuration under /etc/cassandra/conf by altering
the cassandra.yaml file:
Install Cassandra on all the nodes
Set up the Cassandra configuration under /etc/cassandra/conf by altering
the cassandra.yaml file:
14. Log files can be found under /var/log/cassandra, and the data is stored
under /var/lib/cassandra. The nodetool command can be used on any
Cassandra node to check the status of the Cassandra cluster:
The Cassandra CQL shell command called cqlsh can be used to access the
cluster, and create objects. The shell is invoked next, and it shows that
Cassandra version 2.0.13 is installed:
15. The Cassandra query language next shows a key space called keyspace1
that is being created and used via the CQL shell:
16. The Gremlin Cassandra script
The interactive Titan Gremlin shell can be found within the bin directory of
the Titan install, as shown here. Once started, it offers a Gremlin prompt:
17. The following script will be entered using the Gremlin shell. The first
section of the script defines the configuration in terms of the storage
(Cassandra), the port number, and the keyspace name that is to be used:
18. Next define the generic vertex properties' name and age for the graph to
be created using the Management System. It then commits the
management system changes:
19. Now, six vertices are added to the graph. Each one is given a numeric label
to represent its identity. Each vertex is given an age and name value:
20. Finally, the graph edges are added to join the vertices together. Each edge
has a relationship value. Once created, the changes are committed to store
them to Titan, and therefore Cassandra:
21. This results in a simple person-based graph, shown in the following figure
22. This graph can then be tested in Titan via the Gremlin shell using a similar
script to the previous one. Just enter the following script at the gremlin>
prompt, as was shown previously. It uses the same initial six lines to create
the titanGraph configuration, but it then creates a graph traversal variable
g.
The graph traversal variable can be used to check the graph contents.
Using the ValueMap option, it is possible to search for the graph nodes
called Mike and Flo. They have been successfully found here:
23. Using the Cassandra CQL shell, and the Titan keyspace, it can be seen that
a number of Titan tables have been created in Cassandra:
It can also be seen that the data exists in the edgestore table within
Cassandra:
This assures us that a Titan graph has been created in the Gremlin shell,
and is stored in Cassandra. Now, I will try to access the data from Spark.
24. Accessing Titan with Spark
So far, Titan 0.9.0-M2 has been installed, and the graphs have successfully
been created using Cassandra as backend storage options. These graphs
have been created using Gremlin-based scripts. In this section, a properties
file will be used via a Gremlin script to process a Titan-based graph using
Apache Spark. Cassandra will be used with Titan as backend storage
option.
26. Let us examine a properties file that can be used to connect to Cassandra
as a storage backend for Titan. It contains sections for Cassandra, Apache
Spark, and the Hadoop Gremlin configuration. My Cassandra properties
file is called cassandra.properties, and it looks like this
27. Into Titan code
The following necessary TinkerPop and Aurelius classes that will be used: