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HDInsight for Architects
- 1. @ashishth
- 2. • The most trusted and compliant platform A secure and managed Apache Hadoop and Spark platform for building data lakes in the Cloud @ashishth
- 4. Devices & Sensors Speed Layer Data Lake Store Gen 2 Blob Storage Corporate Data SaaS Data Web Data Streaming/Real- Time/ Application Advanced Analytics & Data Science Machine Learning R, Python, APIs Analytics Data Exploration Corporate Reporting Self-Service BI ETL Serving LayerStorage Hive LLAP @ashishth
- 5. Devices & Sensors Speed Layer Data Lake Store Gen 2 Blob Storage Corporate Data SaaS Data Web Data Streaming/Real- Time/ Application Advanced Analytics & Data Science Machine Learning R, Python, APIs Analytics Data Exploration Corporate Reporting Self-Service BI ETL Serving LayerStorage Hive LLAP ? ? ? ? @ashishth
- 6. @ashishth
- 7. Spark Pig Hive Designed for ETL ETL Data warehousing Adoption High, increasing Low, decreasing Stable Number of connectors Highest High High Languages Python, R, Scala, Java, SQL Pig SQL Performance High Medium Medium @ashishth
- 8. Spark Structured Streaming Storm Adoption High, increasing Decreasing Event processing guarantee Exactly once At least once Throughput High Low Processing Model Micro Batch Real-Time Latency High Low Event time support Yes Yes Languages Python, R, Scala, Java, SQL Java @ashishth
- 9. Capability Hive LLAP Spark SQL Presto Interactive Query Speed High High Medium Scale High High Low Caching Yes Yes Early Support Result Caching Yes No No Intelligent Cache Eviction Yes No No Materialized Views Yes No No Complex Fact to Fact Joins Yes Yes No Transactions Yes No No Query Concurrency High Low Low Row , Column level security Yes [Apache Ranger+ AAD] Medium Medium Rich end user Tools Yes Yes Yes Language Support SQL, UDF SQL, Scala, Python SQL Data Source Connector Support Storage Handlers Data Sources High number of connectors @ashishth
- 10. Spark Metadata Hive Metadata Spark Metadata Hive Metadata Azure HDInsight 3.6 with Hadoop 2.6 Azure HDInsight 4.0 with Hadoop 3.x Hive Metastore migration tool: https://azure.microsoft.com/en-us/blog/hdinsight-metastore-migration-tool- open-source-release-now-available/
- 11. ADF Airflow Oozie Service management Azure PaaS IaaS VM HDInsight Code JSON Python Java GUI ADF V2 has great UX Good UX Below Average UX Community Microsoft Growing (10893 Stars) Declining (454 Stars) On-demand clusters Yes No, but extensible No Extensibility Custom action-only Full, graph + actions Custom action-only Pipeline definition JSON/UX Python/ UX XML/UX Devops-first design Yes Yes Yes Pipeline monitoring Yes Yes Yes Scheduling Event, Time Event Event, Time @ashishth
- 12. Motivation and benefits Architecture best practices Infrastructure best practices Storage best practices Data migration best practices Security and DevOps best practices https://azure.microsoft.com/en-us/blog/migrating-on-premises-hadoop-infrastructure-to-azure-hdinsight/ @ashishth
- 13. Data Sources Apps Sensors and devices Data Ingestion Advanced Analytics BI/ Visualization People Automated Systems Apps Web Mobile Bots Data catalog/ Governance/ Lineage Connectors: JDBC, ODBC Productivity Tools Enterprise grade add-ons (hybrid, backup, DR, security, performance) Data Prep/ Management @ashishth
- 14. @ashishth
- 16. Data Qty Network Bandwidth 45 Mbps (T3) 100 Mbps 1 Gbps 1 TB 2 days 1 day 2 hours 10 TB 22 days 10 days 1 day 35 TB 76 days 34 days 3 days 80 TB 173 days 78 days 8 days 100 TB 216 days 97 days 10 days 200 TB 1 year 194 days 19 days 500 TB 3 years 1 year 49 days 1 PB 6 years 3 years 97 days 2 PB 12 years 5 years 194 days @ashishth
- 17. Network Transfer with TLS • Over Internet • Express Route • Databox online Transfer Shipping data offline • Import / Export service • Data Box offline data transfer @ashishth
- 19. @ashishth
- 20. Type Latency Consistency Workloads Bandwidth Key Benefits ADLS Gen 1 Hierarchical 10-100ms Low HDInsight 3.6( No HBase) High Atomic Rename, File Folder level ACL’s ADLS Gen 2 Hierarchical 10-50ms Medium HDInsight 3.6 & 4.0 Unconstrained Atomic Rename, File Folder level ACL’s Standard BLOB Object Store 10-50ms Medium HDInsight 3.6 & 4.0 Unconstrained Mature Premium BLOB Object Store ~5ms High HBase in Preview Unconstrained Fast Premium Managed Disks Hierarchical ~5ms High Kafka, HBase in preview Based on disk Consistent @ashishth
- 21. Scenario Supported Workaround HDInsight 3.6 & 4.0 with Standard Blob as Primary and/ or secondary Yes HDInsight 3.6 & 4.0 with ADLS Gen2 as primary Yes HDInsight 3.6 & 4.0 with ADLS Gen2 as primary & Blob as additional Yes HDInsight 3.6 & 4.0 with Blob as primary & ADLS Gen2 as additional No HDInsight 3.6 with multiple ADLS Gen2 accounts Yes HDInsight 3.6 & 4.0 with ADLS Gen1 and ADLS Gen 2 No Distcp across two clusters HDInsight 4.0 with ADLS Gen 1 No Distcp across two clusters @ashishth
- 22. @ashishth
- 23. @ashishth
- 24. RegionServer Region Region Region WASB Client -Put -Delete -Get Log Flusher Store File HFile Store File HFile Store File HFile Store File HFile Store File HFile Store File HFile
- 25. RegionServer WASB Client -Put -Update -Get -Delete Log Flusher Write path challenges with Write Ahead Log Insert Update Get Delete Sync Operation • Inconsistent Latencies • High latencies @ashishth
- 26. RegionServer Premium Managed Disk(s) Client -Put -Update -Get -Delete Log Flusher Insert Update Get Delete Sync Operation Introducing Premium Managed disk for WAL • Consistent Latencies • Low latencies • Data Durability @ashishth
- 27. RegionServer Region Region Region WASB Client -Put -Delete -Get Log Flusher Store File HFile Store File HFile Store File HFile Store File HFile Store File HFile Store File HFile Premium Managed Disk(s) @ashishth
- 28. @ashishth
- 29. RegionServer Region Region Region PremiumBlob Client -Put -Delete -Get Log Flusher Store File HFile Store File HFile Store File HFile Store File HFile Store File HFile Store File HFile Premium Managed Disk(s) Local SSD +DRA M @ashishth
- 30. Cluster Type Operation Row Size # ops #Region Servers Region Server Node Size #Clients Throughput Avg Latency (ms) Run Time (min) Standard Write 1KB 107,374,182 4 Standard_D4_V2 2 37,958 0.417 47 Premium WAL Write 1KB 107,374,182 4 Standard_D4_V2 2 57,812 0..271 31 Standard Small Write 100 Bytes 1,073,741,824 4 Standard_DS4_V2 2 84,910 0..186 210 Premium WAL Small Write 100 Bytes 1,073,741,824 4 Standard_DS4_V2 2 701,234 0.016 25 Standard Read 100 Bytes 925,075 4 Standard_D4_V2 2 256 62 60 Premium WAL & Premium Blob Read 100 Bytes 33,503,676 4 Standard_D4_V2 2 9,306 1.7 60 Standard Large Read 1K 945,682 4 Standard_D4_V2 262 61 60 Premium WAL & Premium Blob Large Read 1K 24,846,209 4 Standard_D4_V2 2 6901 2.3 60
- 31. @ashishth
- 32. Workload Caching Options Key benefits Spark Spark IO Cache Up to ~8 to 10x perf improvements HBase & Phoenix Bucket cache Up 5-10x perf gains on recently read or written data Hive + LLAP LLAP Intelligent cache/Result Cache Up to ~4-100X gain on cached data
- 33. Azure Data Lake Storage INSTANCE CORE RAM TEMP SSD D1 v2 1 3.50 GiB 50 GiB D2 v2 2 7.00 GiB 100 GiB D3 v2 4 14.00 GiB 200 GiB D4 v2 8 28.00 GiB 400 GiB D5 v2 16 56.00 GiB 800 GiB • Significant Spark performance speed up with IO cache (up to 9X perf gains) • Automatic cache resource management • DRAM + Temp SSD makes large cache pool @ashishth
- 34. @ashishth
- 35. HDInsight Cluster Gateways Head Node 1 Head Node 2 Worker Node Worker Node Worker Node Worker Node Zookeeper1 Zookeeper1 Zookeeper1 Hive Metastore YARN https://cluster.azurehdinsight.net/APIs @ashishth
- 36. Workload DR Option Spark / Hive Manual, Partner solution HBase HBase replication, Snapshot export, Import Export, Copy Tables Kafka Mirror Maker @ashishth
- 38. Ingest Process Publish Active Cold standby Replication RPO RTO Cost Medium Medium High @ashishth
- 43. @ashishth
- 44. Virtual Network (10.1.0.0/16) HDInsight Cluster in Subnet (10.1.1.0/24) Gateways Head Node 1 Head Node 2 Worker Node Worker Node Worker Node Worker Node Allow VNet (10.1.0.0/16) Allow VNet (10.1.0.0/16) Hive Metastore @ashishth
- 45. @ashishth
- 46. @ashishth
- 47. Scenario Authorizing Component Yarn: Submit-App Apache Ranger: Yarn Plugin Hive Operations: Create, Select, Update, Drop, index, Lock, Read, Write, Masking, Row level filter on Hive Database, Table & Columns Apache Ranger: Hive Plugin Create/ Alter Table with storage location reference Apache Ranger + ADLS Gen 2 ACL’s Spark SQL access with Hive Metastore Apache Ranger: Hive Plugin HBase Access Policies Apache Ranger/ HBase plugin Kafka Access Policies Apache ranger/ Kafka Plugin Access Azure Data Lake Storage Gen2 using the Spark DataFrame API ADLS Gen 2 ACLs Access Azure Data Lake Storage Gen2 using the RDD API ADLS Gen 2 ACLs HDFS operations: Mkdir, ls, put, copyFromLocal, get, cat, mv, cp etc ADLS Gen 2 ACLs Running Map Reduce jobs ADLS Gen 2 ACLs @ashishth
- 48. @ashishth
- 49. Apache Ambari Azure Log Analytics IntegrationHDInsight Cluster Metrics
- 50. @ashishth
- 51. INSTANCE VCPU RAM TEMPORARY STORAGE PAYG D14 v2 16 112.00 GiB 800 GiB $1.196/hour E16 v3 16 128.00 GiB 400 GiB $1.064/hour 12.5% 11% @ashishth
- 52. @ashishth
Editor's Notes
- Azure HDInsight is a secure and managed platform for building data lakes on Azure based on the Apache Hadoop and Spark frameworks. So, what all does HDInsight have to offer? Reliable Open Source analytics with an Industry leading SLA HDInsight allows you to easily spin up open source cluster types guaranteed with the industry’s best 99.9% SLA and 24/7 support. We guarantee this SLA for the entire big data solution, not just the VM instances. HDInsight is architected for full redundancy and high availability including head node replication, data geo-replication, and built-in standby NameNode making HDInsight resilient to critical failures not addressed in standard Hadoop implementations. Azure also offers cluster monitoring and 24x7 enterprise support backed by Microsoft and Hortonworks with 37 combined committers for Hadoop core, more than all other managed cloud providers combined to support your deployment and the ability to fix and commit code back to Hadoop. Enterprise Grade Security & Monitoring HDInsight protects your data assets and easily extends your on-premise security and governance controls to the cloud. We feature single sign-on (SSO), multi-factor authentication and seamless management of millions of identities through Azure Active Directory. You can authorize users and groups with fine-grained access control policies over all your enterprise data with Apache Ranger. HDInsight meets HIPAA, PCI, SOC compliance, ensuring your enterprise data assets are always protected with the highest security and regulatory compliance. To ensure the highest level of business continuity, HDInsight extends capabilities for alerting, monitoring, defining pre-emptive actions, and enhanced workload protection through native integration with Azure Operations Management Suite (OMS). Most Productive platform for developers and scientists HDInsight offers developers tailored experiences through rich productivity suites for Hadoop & Spark with integrated development environments using Visual Studio, Eclipse, and IntelliJ supporting Scala, Python, R, Java, and .Net. HDInsight gives data scientists the ability to create narratives that combine code, statistical equations, and visualizations that tell a story about the data through integration to the two most popular notebooks: Jupyter and Zeppelin. HDInsight is also the only managed cloud Hadoop solution with integration to Microsoft R Server. Multi-threaded math libraries and transparent parallelization in R Server means handling up to 1000x more data and up to 50x faster speeds than open source R—helping you train more accurate models for better predictions than previously possible. Cost effective cloud scale HDInsight has decoupled compute and storage, enabling you to cost-effectively scale workloads up or down, independent of storage. Local storage can still be used for caching and fast I/O. Spark and interactive Hive users can choose SSD memory for interactive performance; while Kafka users can retain all streaming data in premium managed disks. You only pay for the compute and storage you use and are given the ability to choose any Azure VM types that enables the best utilization of resources. A recent study showed HDInsight delivering 63% lower TCO than deploying Hadoop on premises over 5 years.* Integration with leading Productivity Applications In the broader ecosystem for Hadoop, there is a thriving market of independent software vendors (ISVs) who provide value added solutions. Through a unique design where every cluster is extended with edge nodes and script action, HDInsight lets customers spin up Hadoop and Spark clusters pre-integrated and pre-tuned with any ISV application out-of-the-box. Datameer, Cask, AtScale, StreamSets are few such applications, which are very popular on the HDInsight platform today. Easy for administrators to manage With HDInsight, administrators can deploy Hadoop in the cloud without buying new hardware or incurring other up-front costs. There’s also no time-consuming installation or set up. There is also no need to patch the operating system or upgrade the Hadoop versions. Azure does it for you. Launch your first cluster in minutes.
- Build 2015
- The new world of HDInsight 4.0 with Hadoop 3.0, brings the Spark and Hive worlds closer together. Lets see, how… Before Hadoop 3.0, the Spark executors would directly access the Hive metastore. While, on the surface, this seems like a fine thing to do, it is rife with problems. The new architecture instead requires explicit registration of Hive transactional tables as Spark external tables through Hive Warehouse Connector. While it adds one extra step during configuration, this approach greatly increases the reliability of data access. Hive Warehouse Connector supports efficient predicate pushdown and Apache Arrow-based communication between Spark executors and Hive LLAP daemons. This results in overall small overhead of communication between two systems. With Hive Warehouse Connector, Apache Spark on HDInsight 4.0 gets mature transactional capabilities. The new integration between Apache Spark and Hive LLAP in HDInsight 4.0 delivers new capabilities for business analysts, data scientists, and data engineers. Business analysts get a performant SQL engine in the form of Hive LLAP (Interactive Query) while data scientists and data engineers get a great platform for ML experimentation and ETL with Apache Spark over transactional data in Hive tables.
- Reference https://azure.microsoft.com/en-us/blog/deploying-apache-airflow-in-azure-to-build-and-run-data-pipelines/
- Build 2015
- Transfer data over network with TLS Over internet - You can transfer data to Azure storage over a regular internet connection using any one of several tools such as: Azure Storage Explorer, AzCopy, Azure Powershell, and Azure CLI. See Moving data to and from Azure Storage for more information. Express Route - ExpressRoute is an Azure service that lets you create private connections between Microsoft datacenters and infrastructure that’s on your premises or in a colocation facility. ExpressRoute connections do not go over the public Internet, and offer higher security, reliability, and speeds with lower latencies than typical connections over the Internet. For more information, see Create and modify an ExpressRoute circuit. Data Box online data transfer - Data Box Edge and Data Box Gateway are online data transfer products that act as network storage gateways to manage data between your site and Azure. Data Box Edge, an on-premises network device, transfers data to and from Azure and uses artificial intelligence (AI)-enabled edge compute to process data. Data Box Gateway is a virtual appliance with storage gateway capabilities. For more information, see Azure Data Box Documentation - Online Transfer. Shipping data Offline Import / Export service - you can send physical disks to Azure and they will be uploaded for you. For more information, see What is Azure Import/Export service?. Data Box offline data transfer - Data Box, Data Box Disk, and Data Box Heavy devices help you transfer large amounts of data to Azure when the network isn’t an option. These offline data transfer devices are shipped between your organization and the Azure datacenter. They use AES encryption to help protect your data in transit, and they undergo a thorough post-upload sanitization process to delete your data from the device. For more information, see Azure Data Box Documentation - Offline Transfer.
- Before I describe specific capabilities and value propositions of HDInsight, let us take a quick look at the architecture of a HDInsight cluster. We will build upon this when we talk about security later on in the presentation. First off, a key difference between an on-premise Hadoop cluster and a HDInsight cluster is that with HDInsight, the storage and compute layers are separated. This allows for storage and compute to be scaled independently of each other. We have seen in numerous customer cases, that trying to combine storage and compute on to a single cluster often leads to underutilization of one or the other or both. With HDInsight, you can keep loading data in to Azure Storage Gen1 or Gen2 or in WASB. And you can create small or large clusters as and when needed. Each HDInsight cluster comes with 2 gateway nodes, 2 head nodes and 3 ZooKeeper nodes. In most cases, these are free of charge. As we will discuss later, we provision multiple of these nodes to ensure high availability. Each HDInsight cluster lives within a VNET. The gateway nodes are the ONLY public endpoints accessible from outside the VNET. As we will see later, this architecture allows you to securely lock down your HDInsight cluster.
- Let’s start with network security. Previously you could have injected a HDInsight cluster within a VNET and secured access to it from the public internet using NSG firewalls. Now you can ensure that any resources that the cluster needs to accesss e.g. Azure Storage accounts, Hive metastores etc. can themselves be secured. With the new service endpoint capability, Azure resources such as Azure Storage, Azure DB, Cosmos DB etc. can be secured via service endpoints. HDInsight now integrates with this capability. Let me walk you through how this would work. [WALK THROUGH THE ANIMATION]