Development of Hybrid SQL/NoSQL PanDA Metadata

Storage

<Big/Mega>PanDA/ CERN IT-SDC meetingDec 02, 2014

Marina Golosova and Maria GrigorievaBigData Technologies

for mega-science project Laboratory, NRC KI

2

• BigData Technologies for mega-science projects

• Metadata Hybrid storage

Overview

02/12/2014

BigData Technologies for mega-science projects

• The project is supported by the Russian Federation Government grant

• Scientific program is tightly coupled with LHC experiments priorities and address challenges we will meet in 2-3 years.

• Project objective: Development of the novel Workload and Data Management System for Big Data, based on PanDA (MegaPanDA)

MegaPanda features:

• Support for large-scale data handling • HPC support• Cloud and web-based computing

services support

3A. Klimentov , Russian «MegaProject» https://indico.cern.ch/event/276497/session/0/contribution/10/material/slides/1.pdf

PanDA: metadata storage challenges

• Archive: 900 M jobs (since 2006)

• Current rate: ~2M jobs per day

• RDBMS: Response time increases as the volume of stored metadata grows up

• Dividing metadata:– actual (read-write part): for the

most recent and changing records (ATLAS_PANDA)

– archive (read-only part): for all records since 2006 (ATLAS_PANDAARCH)

• Oracle

• 2015 (Run-2): current rate x5• 2020 (Run-3): current rate x10

• …?

Completed Jobs 2009 – 2014 years

402/12/2014

RDBMS (SQL) Storage

METADATA SQL Storage

SQL standard : ACID

AtomicityConsistencyIsolationDurability

ActualAccess type:Read / Write

ArchiveAccess type:

Read

Applications

PanDA Server

PanDA Monitor

…502/12/2014

…

SQL standard : ACID NoSQL standard : BASE

AtomicityConsistencyIsolationDurability

Basic AvailabilitySoft-stateEventual consistency

NoSQL: not only SQL storage

METADATA Hybrid Storage

ActualAccess type:Read / Write

ArchiveAccess type:

Read

Applications

PanDA Server

PanDA Monitor

Storage API

NoSQL

602/12/2014

7

Objective:Architecture and implementation of storage and access to PanDA metadata.

Stage 1: Subject area research.

Stage 2: Technology research. NoSQL

Stage 3: Storage schema.

o Stage 4: Storage software.

o Stage 5: PanDA adaptation.7

Hybrid Storage project

Design o Implementation o Testing

PanDA metadata structure

PanDA DB architecture

Design Implementation Testing

02/12/2014

TypeColumn-oriented (Java)

Document-based (C++) Column-oriented (Java)

Point of failure

single point of failure – namemode (HDFS)

Database sharding mechanism

Peer-to-peer architecture;no-single-point-of-failure architecture

Storage Engine

HDFS

B-tree based storage engine;per database write lock makes writes problematic

locally-managed storage;storage engine only appends updated data;SSD & mixed SSD and HDD support

Read/Writes

optimized for reads, single-write master Well suited for doing range based scans

only one writer may modify a given database at a time - even a small number of writes can produce stalls in read performance

constant-time writesuses advanced concurrent structures to provide row-level isolation without locking

Analytical Capabilities

uses the Hadoop Infrastructure

custom map/reduce implementation

CFS (HDFS compatible Cassandra File System)

Cassandra v 2.1 improvements

Faster reads and writes & Improved row cache

Incremental repair  Off-heap memtables,

reducing memory pressure on the Java heap

More performant implementation of counters 

CQL improvements: collection indexes and user-defined types

Post-compaction read performance

Improved Hadoop support Improvements to

bootstrapping a node that ensure data consistency

OUR CHOISE - Cassandra:

Scale out without explicit partitioning/sharding Time-based data (log file analysis, time series) Low-latency application backend

Stage 2: NoSQL compare

802/12/2014

1) Main table – JOBS 2) Helper tables for most popular queries

Jobs (~90 columns) (model #1)

Stage 3: Data model for

PandaID assignedPriority atlasRelease …

2038679208 1000 Atlas-17.2.7 …

2033030636 … … …

Primary key (Jobs)• Partition key: PandaID• Clustering keys: ---

Task (10-15 columns)(model #1)

TaskID JobStatus ModificationTime PandaID …

769 failed 2014-01-06 2037208385 …

2037208386 …

… …

finished 2014-01-01 2032493594 …

2014-01-06 2037208384 …

… … … … …

Primary key (Task #1)• Partition key: TaskID • Clustering keys: JobStatus,

ModificationTime, PandaID

A…Z

A…Z

902/12/2014

Primary key (Task #2)• Partition key: (TaskID , JobStatus)• Clustering keys: ModificationTime, PandaID

TaskID JobStatus ModificationTime PandaID …

769 failed 2014-01-06 2037208385 …

2037208386 …

… …

769 finished 2014-01-01 2032493594 …

2014-01-06 2037208384 …

… … … … …

A…Z

Task (~90 columns) (model #2)

First testing

QUERY conditions Data Model #1 Data Model #2

pandaID 35.3 13.5 53.8

taskID 17.0 5.0 4.2

JEDItaskID 6.1 3.1 4.1

taskID + jobStatus ++ modificationTime

(interval)13.2 6.2 ---

taskID ++ modificationTime (interval)

11.0 16.9 ---

Stage 3: Test Results

Single query average response time (ms)

1002/12/2014

Cassandra: 2 nodes• CPU: 2.40 GHz, 4 cores• Memory: 6 GB• Disk: 500 GB

Oracle: 1 node• CPU: 3.00 GHz, 4 cores• Memory: 4 GB• Disk: 1 TB

Stage 4: Storage architecture

1102/12/2014

12

Development of NoSQL schema Creating test bed for schema testing Loading a two weeks slice of ATLAS archive

data into both Cassandra cluster and Oracle DB

NoSQL schema testing

Storage software design Basic functionality implementation:

• wrappers: Cassandra, Oracle, MySQL• data export (Oracle)• data import (Cassandra)• full copy (export-import) from SQL to

NoSQL

Storage

NoSQL

Cassandra

SQL

MySQL

Oracle

utils

interaction

SQLtoNoSQL

Hybrid Storage: current status

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1302/12/2014

Acknowledgements

Gancho Dimitrov,Jaroslava Schovancova,Eygene Ryabinkin,Maxim Potekhin,Michail Borodin