Shark:SQL and Rich Analytics at Scale Presentaed By Kirti Dighe Drushti Gawade

What is Shark? Built on the top of the RDD and spark A new data analysis system Built on the top of the RDD and spark Compatible with Apache Hive data, metastores, and queries(HiveQL, UDFs, etc) Similar speedups of up to 100x Supports low-latency, interactive queries through in-memory computation Supports both SQL and complex analytics such as machine learning

Shark Architecture Diagram of Architecture Used to query an existing Hive warehouse returns result much faster without modification Diagram of Architecture

Spark Support partial DAG execution Optimization of joint algorithm Features of shark Supports general computation Provides in-memory storage abstraction-RDD Engine is optimized for low latency

RDD Sparks main abstraction-RDD Collection stored in external storage system or derived data set Contains arbitrary data types Benefits of RDD’s Return at the speed of DRAM Use of lineage Speedy recovery Immutable-foundation for relational processing.

Fault tolerance guarantees Shark can tolerate the loss of any set of worker nodes. Recovery is parallelized across the cluster. The deterministic nature of RDDs also enables straggler mitigation Recovery works even in queries that combine SQL and machine learning UDFs

Executing sql over RDDs Process of executing sql queries which includes Query parsing Logical plan generation Physical plan generation

Engine extension Partial DAG execution(PDE) Static query optimization Dynamic query optimization Modification of statistics Example of statistics Partition size record count List of “heavy hitters” Approximate histogram

Join Optimization Skew handling and degree parallelism Task scheduling overhead

Columnar Memory Store Simply catching records as JVM objects is insuffiecient Shark employs column oriented storage , a partition of columns is one MaoReduce “record” Benefits: compact representation, cpu efficient compression, cache locality

Machine learning support Shark supports machine learning-first class citizen Programming model design to express machine learning algorithm: 1. Language Integration Shark allows queries to perform logistic regression over a user database. Ex: Data analysis pipeline that performs logistic regression over database.

2. Execution Engine Integration Common abstraction allows machine learning computation and SQl queries to share workers and cached data. Enables end to end fault tolerance

Implementation Minimize tail latency CPU cost processing of each How to improve Query Processing Speed Minimize tail latency CPU cost processing of each Memory-based shuffle Temporary object creation Bytecode compilation of expression evaluation

Experiments Evaluation of the shark using database Pavlo et al. Benchmark: 2.1 TB of data reproducing Pavlo et al.’s comparison of MapReduce vs. analytical DBMSs [25]. TPC-H Dataset: 100 GB and 1 TB datasets generated by the DBGEN program [29]. Real Hive Warehouse: 1.7 TB of sampled Hive warehouse data from an early industrial user of Shark. Machine Learning Dataset: 100 GB synthetic dataset to measure the performance of machine learning algorithms. Shark perform 100x faster than hive

Methodology and cluster setup Amazon EC2 with 100m2.4xlarge nodes 8 virtual code 68 GB of memory 1.6 TB of local storage Pavlo etal. Benchmarks 1 GB/node ranking table 20 GB/node uservisits table Selection Query (cluster index) SELECT pageURL, pageRank FROM rankings WHERE pageRank > X;

Aggregation Queries SELECT sourceIP, SUM(adRevenue) FROM uservisits GROUP BY sourceIP; SELECT SUBSTR(sourceIP, 1, 7), SUM(adRevenue) FROM uservisits GROUP BY SUBSTR(sourceIP, 1, 7);

Join Query SELECT INTO Temp sourceIP, AVG(pageRank), SUM(adRevenue) as totalRevenue FROM rankings AS R, uservisits AS UV WHERE R.pageURL = UV.destURL AND UV.visitDate BETWEEN Date(’2000-01-15’) AND Date(’2000-01-22’) GROUP BY UV.sourceIP; Join query runtime from Join stategies Pavlo Benchmark chosen by optimizers

Data Loading Micro-Benchmarks To query data in HDFS directly,which means its data ingress rate is at least as fast as Hadoop’s. Micro-Benchmarks Aggregation performance SELECT [GROUP_BY_COLUMN], COUNT(*) FROM lineitem GROUP BY [GROUP_BY_COLUMN]

Join selection at runtime Fault tolerence Measuring sharks performance in presence of node failures –simulate failures and measure query performance, before,during and after failure recovery.

Real hive warehouse 1. Query 1 computes summary statistics in 12 dimensions for users of a specific customer on a specific day. 2. Query 2 counts the number of sessions and distinct customer/client combination grouped by countries with filter cates on eight columns. 3. Query 3 counts the number of sessions and distinct users for all but 2 countries. 4. Query 4 computes summary statistics in 7 dimensions grouping by a column, and showing the top groups sorted in descending order.

Machine learning Algorithms Compare performance of shark running the same work flow in Hive and Hadoop Workflow consisted of three steps: 1)Selecting the data of interesr from the warehouse using SQL 2)Extracting Features 3)Applying Iterartive Algorithms Logistic Regresion K-Means Clustering

Logistic Regression,pre-iterarion runtime(seconds) K-means Cllustering,pre-iteration algorithm

Conclusion Warehouse combining relational queries and complex analytics Generalizes map reduce using both Traditional Databse Techniques Novel Partial DAG Execution Shark faster than Hive and Hadoop