setting up a big data platform at kelkoo

Setting up a Big Data Platform atKelkooData Platform

Fabrice dos Santos1st of Sep. 2015

Kelkoo DataPlatform / Big Data ?• “Football is a simple game. Twenty-two men chase a ball for 90 minutes and at

the end, the Germans always win” • And why do they win ?

– Because they use big data !– German team partnered with German software giant SAP AG to create a custom

match analysis tool that collects and analyzes massive amounts of player performance data.

• Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, andinformation privacy.

• http://blogs.wsj.com/cio/2014/07/10/germanys-12th-man-at-the-world-cup-big-data/

• https://www.youtube.com/watch?v=JX5NLUViIMc• http://www.lesechos.fr/idees-debats/cercle/cercle-111048-le-monde-nouveau-du-

big-data-1047390.php

Gary Lineker

Kelkoo DataPlatform transitioninng ::: AGENDA & Goal

FlumeData collection and

aggregation

HDFSDistributed storage

•Name node / Datanodes

•HDFS INPUTS: LOGS

•HDFS OUTPUT: REPORTS

Spark on Yarn

Distributedprocessing

• ResourceManager / Nodemanager

• Spark applications

Hive / SparkSQL

Query dataRead and analyse

• GOAL

• give you the core concepts of hadoop platform @ Kelkoo

• understand dataflow

• starts getting used with vocabulary

1/ Kelkoo DataPlatform :: Flume

Flume Agent

Acheminent des données

Stockage des données

• Name node / Datanodes

• HDFS INPUTS: LOGS

• HDFS OUTPUT: REPORTS

Spark on Yarn

Analyse et traitement des données

• Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data store.

FLUME AGENT (kelkoo_a1)

Flume:: Core concepts

External

client (ECS, KLS

etc…)

Push even

Source(rImp, rLead)

Channel (cImp,

cLead)

Sink(sImp,sLe

Read events

• Event : unit of data transported by Flume• [ Header (timestamp, hostname …) | Body

(data)]

• Client (ECS,KLS): point of origin of eventsthat deliver them to a Flume agent

• Flume agent (kelkooFlume_a?) jvm process:

• Source: consume events and hands itover to the channel

• Channel: buffers incoming events until a sink drains them for further transport => reliability

• Sink: remove events from a channel and transmit it to next agent (HDFS sinkhere)

• Channel periodically writes a backup check point out to disk => recoverability

• HDFS storage : terminal repository

Checkpoint/opt/kookel/data

/kelkooFlume

Flume @ Kelkoo

• Source type– We use Avro, which is a data serialization format: compact and fast binary data format– Other type of sources: memory, exec (tail –f /opt/… )

• Flume = transactional design = each event is treated as a transaction– The events are removed from a channel only after they are stored in the channel of the next

agent or in the terminal repository, thus maintaining a queue of current events until the storage confirmation is received

• Distributed and scalable system : 4 agents in Kelkoo to spread the load installed on 2 servers

• Channel type: file– The File Channel is Flume’s persistent channel. – Writes out all events to disk : no data loss on process or machine shutdown or crash. – The File Channel ensures that any events committed into the channel are removed from the

channel only when a sink takes the events and commits the transaction, even if the machine or agent crashed and was restarted.

– Designed to be highly concurrent and to handle several sources and sinks at the same time.

Flume monitoring

• Agent has a json servlet

– http://haddop-server:34545/metrics

– Returns a json output easily managable for monitoring purpose, using a simple shell script with jq extension

2 / KelkooDataPlatform :: HDFS distributed storage

aggregation

Spark on YarnDistributedprocessing

Hive / SparkSQL

::: HDFS definition

• HDFS is a highly scalable, distributed file system, meant to store large amount of data

• based on GoogleFS.

• Appears as a single disk: abstract physicalarchitecture , we can manipulate files as if wewere on a single disk.

• HDFS ?

– HADOOP DISTRIBUTED FILESYSTEM

HDFS Daemons Overview

• 2 types of processes: – Namenode (must always be running):

• Stores the metadata about files and blocks on the filesystem, manage namespaces– Maps a file name to a set of blocks– Maps a block to a set of Datanodes

• Redirect client for read/writes to appropriate datanode

– Datanodes:• Stores the data in local filesystem (ext4 in Kelkoo) • periodically reports to Namenode the list of blocks they host and send heartbeat to the

namenode• Serves data and meta-data to Clients• Runs on several machines

NameNodedc1-kdp-prod-hadoop-

Datanode 1ex: dc1-kdp-prod-

hadoop-06 Datanode 2 Datanode 3 … Datanode n

StanbyNameNode

dc1-kdp-prod-hadoop-01

HDFS files and blocks example• « toto.txt » file is managed by Namenode,

stored by Datanodes

– File split into blocks: Block #1 + Block # 2

– when a file is read, Datanode ask the namenodeon which blocks data is located

– Blocks are replicated (default is 3) : ensuresrobustness and availability

Namenodedc1-kdp-prod-

hadoop-00

Datanode 1 Datanode 2 Datanode 3…

Datanode n

SAME BLOCK on multiple

machines

Shared edits

HDFS High availability with Hadoop 2+Zookeeper service3 Zookeeper instances

Active NameNodedc1-kdp-prod-hadoop-00

Stanby NameNodedc1-kdp-prod-hadoop-01

• Namenodes: one active and one standby namenode, standby takes over if the active namenode goes down, (avoid SPOF ).

• Zookeeper : High availibility of process• Zookeeper server: keeps a copy of the

state of the entire system and persists this information in local log files.

• ZooKeeper Failover Controller ZKFC : monitors NameNode and failover when the Active NameNode is unavailable.

• Quorum Journal Manager & JournalNodes: High availability of data

• Instead of storing HDFS edit logs in a single location (nfs), store them in several remote locations => the JournalNodes

• Active Namenode : writes edits to journalNodes

• QJM (feature of the Namenode) ensuresthat we « reach the quorum » ie ensurethe journal log is written to the majorityof the JournalNodes

• Stanby Namenode : read edits

• On the server: conf written in • /etc/hadoop/hdfs_site.xml:

JournalNode JournalNode JournalNode

Monitor and maintain active lock

ZKFCZKFC

Monitor and try to take active

writes

HDFS User Interface• Interacting with HDFS using Filesystem shell commands (as kookel)

– All commands are on hadoop doc– hdfs dfs -<command> <options>– hdfs dfs -du -chs /user/kookel/logs/flume/

• Command for HDFS administration– hdfs dfsadmin -report -live | grep --color dc1-kdp-prod-hadoop-

10.prod.dc1.kelkoo.net -A1Name: 10.76.99.60:50010 (dc1-kdp-prod-hadoop-10.prod.dc1.kelkoo.net)Hostname: dc1-kdp-prod-hadoop-10.prod.dc1.kelkoo.netDecommission Status : Normal

– hdfs dfsadmin -getDatanodeInfo dc1-kdp-prod-hadoop-06.prod.dc1.kelkoo.net:50020

• Command for HighAvailabilty admin– hdfs haadmin -failover nn1 nn2

• Web interface :– http://dc1-kdp-prod-hadoop-00.prod.dc1.kelkoo.net:50070/dfsclusterhealth.html– http://dc1-kdp-prod-hadoop-00.prod.dc1.kelkoo.net:50070/dfschealth.html

3/ Spark on Yarn• HDFS = distributed storage

• Spark = distributed processing

• Apache Spark is an open-source data analytics cluster computing framework.[1] Spark fits into the Hadoop open-source community, building on top of the HadoopDistributed File System

aggregation

Spark on YarnDistributedprocessing

Hive / SparkSQL

Hadoop Yarn Cluster :: overview

• YARN ? (Yet Another Resource Negotiator)• 2 types of processes:

– ResourceManager• Arbitrates the available cluster resources

– helps manage the distributed applications running on the YARN system– orchestrates the division of resources (compute, memory, bandwidth, etc.) to

underlying NodeManagers

– NodeManagers:• Takes instruction from the ResourceManager• Monitor containers resource usage (cpu, memory, disk) • Reporting resources status to ResourceManager/Scheduler

YarnResource Managerdc1-kdp-prod-

hadoop-02

NodeManager 1ex: dc1-kdp-prod-

hadoop-06 NodeManager 2 NodeManager 3 … NodeManager n

on Yarn :: spark application lifecycleCLIEN

YARN ContainerSpark

Application

MasterYARN Containe

YARN Containe

r SparkExecut

SparkExecut

Yarn Resource Manager

dc1-kdp-prod-hadoop-02

YARN Containe

SparkTask

SparkExecut

orSparkTask

Starts AM

on Yarn ::: spark application lifecycle

• Key concepts:– Application: maybe a single job, sequence of jobs, KDP Spark

applications are mainly launched via Azkaban• sparkAppRunner is a component that allow to run a sparkApp on Yarn

in Kelkoo

– Application Master: • one per application, negotiate resource with YARN • Runs inside a container• Requests more hosts/containers to run the Spark application tasks

– Container @kelkoo => /d0/yarn/local/nm-local-dir/usercache/kookel/appcache/application_1441098196522_0213/container_1441098196522_0213_02_000006

– Spark Executor: A single JVM instance on a node that serves a single Spark application.

– Spark Task: a unit of work on a partition of a distributed dataset.

Yarn Cluster focus• Yarn Resource manager manages applications

• Yarn commands (installed on all servers running in the Yarn cluster)– Can be useful to monitor applications from the Resource manager, commands are invoked by the bin/yarn

script• yarn application -status application_1428487296152_99148• yarn kill application_1428487296152_99148

• Yarn Rest API (more or less like yarn script but more complete)– Xml output : curl --compressed -H "Accept: application/xml" -X GET http://hadoop-

server:8088/ws/v1/cluster/apps/application_1428487296152_99610– Json output: curl --compressed -H "Accept: application/json" -X GET http://hadoop-

server:8088/ws/v1/cluster/apps/application_1428487296152_99610– Cluster metrics : http://dc1-kdp-prod-hadoop-02.prod.dc1.kelkoo.net:8088/ws/v1/cluster/metrics

HDFS & YARN :: don’t mix things upYARN HDFS

Resource Managerresources

allocation for applications in

containers

NameNodehandles

metadata, mapfiles with

blocks,blockswith datanodes

NodeManagersmanage

containers

Datanodes

Containerapplication

data/d0/yarn/local/d1/yarn/local

Data directory/d0/d1

Kelkoo DataPlatform:: Accessing data • Hive: data warehouse infrastructure

– Kelkoo => hiveMetastoreSchema

– System for managing and querying structureddata, built on top of Hadoop

– Provides a simple query language called Hive QL, which is based on SQL

– Hive holds persistent data

HiveMetastore

Hdfs file: consistency_key_metrics

Hive service

Monitoring

• checking DataPlatform input and output– INPUT : missing logs– OUTPUT : missing or failed reports

• Monitoring hdfs:– Check all datanodes are live (HDFS) and HA is running fine (Zookeeper etc..)– Monitor capacity

• Flume monitoring – Check flume is up – Monitor flume Channel on Grafana

• Yarn– Monitor failed spark applications – Check all nodemanagers are live (YARN)– Monitor allocated ressources in containers

• Azkaban– Monitor failed processes

• Monitoring tools– Nagios: – Grafana:

Wrapp up : what you must remember

• Why Big data: to analyse large amounts of data• FLUME:

– Aggregate and stream data into HDFS– Transactionnal mode: client -> agent (source, channel, sink) -> HDFS Storage– Recoverability / reliable and scalable

• HDFS cluster, high performance distributed filesystem– NameNode (master), Datanodes (data)– HDFS High Availability with Zookeeper and Journal nodes– HDFS files and blocks, blocks are replicated (3 by default)

• Spark on Yarn– Yarn ResoureceManager (master), NodeManager(data)– Yarn is used to run Spark applications in distributed mode

• Hive mestatore– Turn hdfs files into structured data

setting up a big data platform at kelkoo

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