Shark:SQL and Rich Analytics at Scale

Presentaed By

Kirti Dighe Drushti Gawade

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

Shark Architecture Used to query an existing Hive warehouse

returns result much faster without modification Diagram of Architecture

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

Spark

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.

RDD

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

Fault tolerance guarantees

Executing sql over RDDs

Process of executing sql queries which includes

Query parsing Logical plan generation Physical plan generation

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

Engine extension

Join Optimization

Skew handling and degree parallelismTask scheduling overhead

Columnar Memory StoreSimply 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

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.

Machine learning support

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

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

Implementation

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

Experiments

Methodology and cluster setupAmazon EC2 with 100m2.4xlarge nodes8 virtual code68 GB of memory1.6 TB of local storage

Pavlo etal. Benchmarks1 GB/node ranking table20 GB/node uservisits table

Selection Query (cluster index)

SELECT pageURL, pageRankFROM rankings WHERE pageRank > X;

Aggregation QueriesSELECT 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 LoadingTo query data in HDFS directly,which means its

data ingress rate is at least as fast as Hadoop’s.Micro-Benchmarks Aggregation performanceSELECT [GROUP_BY_COLUMN], COUNT(*)

FROM lineitem GROUP BY [GROUP_BY_COLUMN]

Join selection at runtime

Fault tolerenceMeasuring 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 forall but 2 countries.4. Query 4 computes summary statistics in 7 dimensions groupingby a column, and showing the top groups sorted in descendingorder.

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 SQL2)Extracting Features3)Applying Iterartive Algorithms Logistic Regresion K-Means Clustering

Logistic Regression,pre-iterarion runtime(seconds)

K-means Cllustering,pre-iteration algorithm

Warehouse combining relational queries and complex analytics

Generalizes map reduce using both1. Traditional Databse Techniques 2. Novel Partial DAG Execution Shark faster than Hive and Hadoop

Conclusion