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No question: Moodle | cap theorem & kvs martijn de vos week 10 - cs - 460 cs - 460 1 modern web workloads • web - based applications cause spikes • data : large and unstructured • random reads and writes ; sometimes write - heavy ( e. g., finance apps ) • joins infrequent challenges with rdbms • not designed for distributed environments • s... | EPFL CS 460 Moodle |
No question: Moodle | consistency matter? consistency : every read receives the most recent write or an error cs - 460 7 use case what you expect ( consistency ) what could go wrong ( inconsistency ) app transfer €500 via your phone, it instantly shows up on your desktop app too. your balance looks updated on your phone but not on your desk... | EPFL CS 460 Moodle |
No question: Moodle | ##sea cable cut sea - me - we 5 cable incident ( 2024 ) connectivity loss between regions dns outage dyn ddos attack ( 2016 ) users can ’ t resolve hostnames bgp configuration error facebook outage ( 2021 ) outage of facebook and subsidiaries take - away : parnnons actually happen in real - world seongs cap combination... | EPFL CS 460 Moodle |
No question: Moodle | eventually consistent • copies becomes consistent at some later time if there are no more updates to that data item cs - 460 13 [ https : / / www. guru99. com / sql - vs - nosql. html ] key takeways 1. choose the right guarantee for the right task ( cp vs. ap ) 2. partition tolerance is non - negotiable in the cap theo... | EPFL CS 460 Moodle |
No question: Moodle | value post _ id ( x. com, facebook. com ) post content, author, timestamp item _ id ( amazon. com ) name, price, stock info flight _ no ( expedia. com ) route, availability, price account _ no ( bank. com ) balance, transactions, owner key - value is a powerful abstraction powering the modern web the key - value abstra... | EPFL CS 460 Moodle |
No question: Moodle | , a ` er dynamo ( 2007 ) and bigtable ( 2006 ) cs - 460 21 cassandra cs - 449 22 • a distributed key - value store • many companies use cassandra in their production clusters • ibm, adobe, hp, ebay, ericsson, symantec, twitter, spotify, netflix • scalable data model : data split across nodes • cap : availability and pa... | EPFL CS 460 Moodle |
No question: Moodle | - 460 25 data model ( 2 / 4 ) cs - 460 26 settings settings name value timestamp column column family keyspace type data model ( 3 / 4 ) cs - 460 27 feature rdbms cassandra organization database → table → row keyspace → column family → column row structure fixed schema dynamic columns column data name, type, value name... | EPFL CS 460 Moodle |
No question: Moodle | … super column k : termk row key < user id > super column 1 super column k column family ( user 1 ) super column 1 super column k column family ( user 2 ) facebook inbox search • primary key : userid • recipients id ’ s : super columns • columns within the super columns : messageid cs - 460 31 m msgidi m msgidj m msgid... | EPFL CS 460 Moodle |
No question: Moodle | inter. alice sends “ hello ” to bob ( msgid : ab ) cassandra architecture • decentralized, peer - to - peer architecture • easy to scale : add / remove nodes • read / write requests can go to any replica node • reads and write have a configurable consistency level cs - 460 34 cassandra architecture 1. partitioning 2. l... | EPFL CS 460 Moodle |
No question: Moodle | n : determines how many copies of the data exist • each data item is replicated at n nodes • various replication strategies • example with n = 2 ( right ) cs - 460 38 38 1 2 3 4 0 - 32 32 - 64 96 - 128 64 - 96 write “ user123 ” 1 2 h ( “ user123 ” ) = 35 3 route write to responsible nodes cassandra : replication strate... | EPFL CS 460 Moodle |
No question: Moodle | set cassandra : writes ( 2 / 2 ) • always writable : hinted mechanism • if any replica is down, the coordinator writes to all other replicas, and keeps the write locally unnl the down replica comes back up. • when all replicas are down, the coordinator ( front end ) writes ( for up to a few hours ). cs - 460 41 - world... | EPFL CS 460 Moodle |
No question: Moodle | commit log is full • another thread flushes all the marked memtables • commit log segments of the flushed memtable are marked for recycling • a bloom filter and index are created cs - 460 44 bloom filters • compact way of representing a set of items • checking for existence ( membership ) in set is cheap • probability of ... | EPFL CS 460 Moodle |
No question: Moodle | to be updated automatically as servers join, leave, and fail • membership protocol • efficient anti - entropy gossip - based protocol • p2p protocol to discover and share location and state information about other nodes in a cassandra cluster cs - 460 47 cassandra : membership management ( 2 / 2 ) 1 1 10120 66 2 10103 ... | EPFL CS 460 Moodle |
No question: Moodle | r = # of nodes in read quorum w = # of nodes in write quorum constraints ( for strong consistency ) : r + w > n most recent write is always read quorum = getting agreement from a committee – you don ’ t need everyone, just a majority quorums : example 51 let : n = 5 r = 3 w = 3 consistency cs - 460 1 2 3 5 4 read quoru... | EPFL CS 460 Moodle |
No question: Moodle | multiple data center support • nosql appropriate datastructures for many big data applications • distributed key - value stored widely used in production • uses many algorithms from p2p systems and distributed computing key takeaways 1. designing distributed systems is all about trade - offs 2. designing for scale requ... | EPFL CS 460 Moodle |
No question: Moodle | big table cassandra week 9 distributed messaging systems kafka – week 11 structured data spark sql graph data pregel, graphlab, x - streem, chaos machine learning week 12 batch data map reduce, dryad, spark streaming data storm, naiad, flink, spark streaming google data flow scheduling ( mesos, yarn ) - week 10 query o... | EPFL CS 460 Moodle |
No question: Moodle | 460 13 characteristics • aggregate resources • scalability • speed • reliability • at the price of • complexity • cost of maintenance cs 460 14 why are they more complex? • no global clock ; no single global notion of the correct time ( asynchrony ) • unpredictable failures of components : lack of response may be due t... | EPFL CS 460 Moodle |
No question: Moodle | - nodes are promoted to active components • nodes participate, interact, contribute to the services they use. • harness huge pools of resources accumulated in millions of end - nodes. • avoid a central / master entity • irregularities and dynamicity are treated as the norm cs 460 the internet : a decentralized system c... | EPFL CS 460 Moodle |
No question: Moodle | , v3 nodes operations : send ( ) p2p overlay network p2p infrastructure ensures mapping between keys and physical nodes 25 distributed hash table k6, v6 k1, v1 k5, v5 k2, v2 k4, v4 k3, v3 nodes operations : send ( m, k ) p2p overlay network • message sent to keys : implementation of a dht • p2p infrastructure ensures m... | EPFL CS 460 Moodle |
No question: Moodle | ##ds ( uniform random ) invariant : node with numerically closest nodeid maintains object. 29 pastry • naming space : • ring of 128 bit integers • nodeids chosen at random • key / node mapping • key associated to the node with the numerically closest node id • routing table • leaf set • 8 or 16 closest numerical neighb... | EPFL CS 460 Moodle |
No question: Moodle | table ( # 65a1fcx ) 0 x 1 x 2 x 3 x 4 x 5 x 7 x 8 x 9 x a x b x c x d x e x f x 6 0 x 6 1 x 6 2 x 6 3 x 6 4 x 6 6 x 6 7 x 6 8 x 6 9 x 6 a x 6 b x 6 c x 6 d x 6 e x 6 f x 6 5 0 x 6 5 1 x 6 5 2 x 6 5 3 x 6 5 4 x 6 5 5 x 6 5 6 x 6 5 7 x 6 5 8 x 6 5 9 x 6 5 b x 6 5 c x 6 5 d x 6 5 e x 6 5 f x 6 5 a 0 x 6 5 a 2 x 6 5 a 3 x ... | EPFL CS 460 Moodle |
No question: Moodle | of entry : cs 460 routing algorithm ( on node a ) b a shl d l d l / b l l,, i r, r l i b i l b and a between prefix shared the of length : ), ( key of digits the of value : leafset in the nodeid closest ith : 128 0 line 2 0 table routing the of entry : leaf set cs 460 36 node departure • explicit departure or failure •... | EPFL CS 460 Moodle |
No question: Moodle | network proximity metric : • closest topological node • satisfying the constraints of the routing table routetable ( i, j ) : • nodeid corresponding to the current nodeid courant up to level i • next digit = j • nodes are close at the top level of the routing table • random nodes at the bottom levels of the routing tab... | EPFL CS 460 Moodle |
No question: Moodle | per - hop distance normal routing tables perfect routing tables no locality 1. 59 slower than ip on average cs 460 references • antony i. t. rowstron, peter druschel : pastry : scalable, decentralized object location, and routing for large - scale peer - to - peer systems. middleware 2001 : 329 - 350 • ion stoica, robe... | EPFL CS 460 Moodle |
No question: Moodle | ) nosql db dynamo big table cassandra week 9 distributed messaging systems kafka – week 11 structured data spark sql graph data pregel, graphlab, x - streem, chaos machine learning week 12 batch data map reduce, dryad, spark streaming data storm, naiad, flink, spark streaming google data flow scheduling ( mesos, yarn )... | EPFL CS 460 Moodle |
No question: Moodle | my writes see all writes performed by reader bounded staleness see all “ old ” writes consistency requirements in a volley - ball game • the first team to reach 25 points and by at least two points wins a set ( for the first 4 sets ) • the first team to reach 15 points and by at least two points wins the 5th set • the ... | EPFL CS 460 Moodle |
No question: Moodle | 2 : 1 21 - 23 2 : 1 22 - 23 2 : 1 23 - 23 2 : 1 24 - 23 2 : 1 25 - 23 3 : 1 reader # 1 3 : 1 2 : 1 20 : 23 home - visitors reader # 2 3 : 1 eventual consistency eventually, in the absence of operations, replicas will be consistent guarantee : see some previous writes. eventually ( in the absence of new writes ), all th... | EPFL CS 460 Moodle |
No question: Moodle | value of a data item x, any successive operation on x by that process will always return the same or a more recent value guarantee : see increasing subset of previous writes ( local guarantee from a given reader ) reader # 1 at time t2 cs 460 58 2 : 1 20 - 22 2 : 1 20 - 23 2 : 1 21 - 23 2 : 1 22 - 23 2 : 1 23 - 23 2 : ... | EPFL CS 460 Moodle |
No question: Moodle | can be eventually - consistent. reader # 2 official score keeper cs 460 60 suppose visitor score read ( visitor _ score ) write ( visitor _ score. update ) read my writes ( single score keeper ) strong consistency otherwise referee cs 460 61 4th set, home scores @ 24 vs = read ( visitor _ score ) ; hs = read ( home _ s... | EPFL CS 460 Moodle |
No question: Moodle | writes strong eventual conclusions • different clients want different guarantees • one client might want different guarantees for different reads • several models can be applied • strong consistency would do but is prohibitive performance wise • use the lowest consistency ( to the left ) consistency model that is “ cor... | EPFL CS 460 Moodle |
No question: Moodle | ##sos, yarn ) - week 10 query optimization storage hierarchies & layouts transaction management query execution dissemination - multicast • key feature in distributed computing cs - 460 5 consistency protocols event dissemination fault - tolerant dissemination cs - 460 6 atomicity : 100 % nodes receive the message trad... | EPFL CS 460 Moodle |
No question: Moodle | - 460 15 r z n z y r r r round prior to processes infected of number the is / = ) ( n z p r = infect forever model n. t. j. bailey, the mathematical theory of infectious diseases and its applications, 2nd ed., hafner press, 1975. probability of “ atomic ” infection is connected is graph random a ity that probabibil the... | EPFL CS 460 Moodle |
No question: Moodle | ) ~ 10 log ( 1m ) ~ 20 log ( 1b ) ~ 30 performance ( 100, 000 peers ) 0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 f proportion of connected peers in non “ atomic ” broadcast proportion of “ atomic ” broadcast cs - 460 18 proportion of nodes who received the message in non ... | EPFL CS 460 Moodle |
No question: Moodle | we do this in a decentralized way? cs - 460 22 achieving random topologies cs - 460 23 the peer sampling service • how to create a graph upon which applying gossip - based dissemination?... by gossiping • goal : • create an overlay network • provide each peer with a random sample of the network in a decentralized way •... | EPFL CS 460 Moodle |
No question: Moodle | information periodically to update their own membership information • reflect the dynamics of the system • ensures connectivity • each peer maintains a local view ( membership table ) of c entries • network @ ( ip @ ) • age ( freshness of the descriptor ) • each entry is unique • ordered list • active and passive threa... | EPFL CS 460 Moodle |
No question: Moodle | buffer to p else send { } to p / / triggers response if pull then receive buffer from p view. selectview ( c, h, s, buffer ) view. increaseage ( viewp ) cs - 460 31 passive thread do forever receive bufferp from p if pull then mydescriptor < - ( my @, 0 ) buffer < - merge ( view, { mydescriptor } ) view. permute ( ) mo... | EPFL CS 460 Moodle |
No question: Moodle | pulling alone is pretty bad : a node has no opportunity to insert information on itself. potential loss of all incoming connections. cs - 460 35 design space : data exchange • buffer ( h ) • initialized with the descriptor of the gossiper • contains c / 2 elements • ignores h “ oldest ” • communication model • push : b... | EPFL CS 460 Moodle |
No question: Moodle | 2008 40 example b x d l i j a v x g 1. buffer appended to view 2. keep the freshest entry for each node b d l i j a v x g nov. 2008 41 example b d l i j a v x g 1. buffer appended to view 2. keep the freshest entry for each node 3. h ( = 0 ) oldest items removed b d l i j a v x g nov. 2008 42 example b d l i j a v g 1.... | EPFL CS 460 Moodle |
No question: Moodle | & al jnsm 2005 ] • node selection : random • data exchange : pushpull • data processing : shuffle cs - 460 degree distribution f = 30 in a 10. 000 node system metrics • degree distribution • average path length • clustering coefficient a generic gossip - based substrate cs - 460 45 gossip - based generic substrate • ea... | EPFL CS 460 Moodle |
No question: Moodle | ##tokeep : add received entries to local view cs - 460 47 gossip - based dissemination data exchanged data processing peer selection message k random how can we achieve random sampling? dissemination data = msg to broadcast each process gossips one message once cs - 460 48 topology maintenance data exchange membership ... | EPFL CS 460 Moodle |
No question: Moodle | value 0 but the initiator • global average = 1 / n • size of the network can be easily deduced • robust implementation • multiple nodes start with their identifier • each concurrent instance led by a node • message and data of an instance tagged with a unique id cs - 460 53 ordered slicing • create and maintain a parti... | EPFL CS 460 Moodle |
No question: Moodle | evaluation of unstructured gossip - based implementation » m. jelasity, r. guerraoui, a. - m. kermarrec and m. van steen, middleware 2004 – acm tocs 2007 • « newscast computing » m. jelasity, w. kowalczyk, m. van steen. internal report ir - cs - 006, vrije universiteit, department of computer science, november 2003 • «... | EPFL CS 460 Moodle |
No question: Moodle | ##rmarrec cs - 460 1 where are we? cs - 460 2 consistency protocols cap theorem week 9 gossip protocols week 7 distributed / decentralized systems week 8 - 12 data science software stack data processing ressource management & optimization data storage distributed file systems ( gfs ) nosql db dynamo big table cassandra... | EPFL CS 460 Moodle |
No question: Moodle | 5 6 3 3 8 • maintain tasks in a queue in order of arrival • when processor free, dequeue head and schedule it fifo scheduling ( first in first out ) fifo / fcfs performance • average completion time may be high • for our example on previous slides, • average completion time of fifo / fcfs = ( task 1 + task 2 + task 3 )... | EPFL CS 460 Moodle |
No question: Moodle | task at queue head • pre - empts processes by saving their state, and resuming later • after pre - empting, add to end of queue task 1 15 ( task 3 done ) … cs - 460 9 round - robin vs. stf / fifo • round - robin preferable for • interactive applications • user needs quick responses from system • fifo / stf preferable f... | EPFL CS 460 Moodle |
No question: Moodle | mesos “ a platform for fine - ‐ grained resource sharing in the data center “ benjamin hindman, andy konwinski, matei zaharia, ali ghodsi, anthony joseph, randy katz, scott shenker, ion stoica university of california, berkeley usenix 2011 cs - 460 15 mesos cs - 460 16 coexistence of multiple applications • ex : fb - >... | EPFL CS 460 Moodle |
No question: Moodle | , anthony joseph, randy katz, scott shenker, ion stoica. usenix 2011 ressource offers • delegates control over scheduling to the frameworks • offer available resources to frameworks, let them pick which resources to use and which tasks to launch • keeps mesos simple, lets it support future frameworks • high utilization... | EPFL CS 460 Moodle |
No question: Moodle | compared performance with statically partitioned cluster where each framework gets 25 % of nodes framework speedup on mesos facebook hadoop mix 1. 14× large hadoop mix 2. 10× spark 1. 26× torque / mpi 0. 96× from arka bhattacharya cs - 460 25 • ran 16 instances of hadoop on a shared hdfs cluster • used delay scheduling... | EPFL CS 460 Moodle |
No question: Moodle | / n of the shared resource. generalized by max - min fairness. • handles if a user wants less than its fair share. • e. g., user a wants no more than 20 %. generalized by weighted max - min fairness • give weights to users according to importance. • e. g., user a gets weight 1, user b weight 2. cs - 460 30 max - min fa... | EPFL CS 460 Moodle |
No question: Moodle | • user a wants 1cpu per task • user b wants 2cpu per task • multi - resource example • 2 resources : cpus and mem • user a wants 1cpu ; 2gb per task • user b wants 2cpu ; 4gb per task cs - 460 34 a natural policy ( 1 / 2 ) fairness : give weights to resources ( e. g., 1 cpu = 1 gb ) and equalize total value given to ea... | EPFL CS 460 Moodle |
No question: Moodle | 85 % ) ) • user a gets less than 50 % of both cpu and ram. • better off in a separate cluster with half the resources cs - 460 36 challenge : can we find a fair sharing policy that provides share guarantee & strategy - proofness can we generalize max - min fairness to multiple resources? dominant - resource fair schedu... | EPFL CS 460 Moodle |
No question: Moodle | dominant share is 25 %. cs - 460 40 dominant resource fairness ( drf ) ( 2 / 2 ) • apply max - min fairness to dominant shares : give every user an equal share of her dominant resource. • equalize the dominant share of the users. • total resources : ( 9cpu ; 18gb ) • user a wants ( 1cpu ; 4gb ) for each task ; dominant... | EPFL CS 460 Moodle |
No question: Moodle | • next : assign to user b step 2 : assign 1 task to user b • a : 1 cpu, 4 gb → 22. 2 % • b : 3 cpu, 1 gb → 3 / 9 = 33. 3 % • next : a ( smaller dominant share ) step 3 : a gets 2nd task • a : 2 cpu, 8 gb → 8 / 18 = 44. 4 % • b : 3 cpu, 1 gb → 33. 3 % • next : b user a wants ( 1cpu ; 4gb ) user b wants ( 3cpu ; 1gb ) to... | EPFL CS 460 Moodle |
No question: Moodle | , the % of its dominant resource type that it gets cluster - wide, is the same for all jobs • job 1 ’ s % of ram = job 2 ’ s % of cpu • can be written as linear equations, and solved drf fairness cs - 460 45 • drf generalizes to multiple jobs • drf also generalizes to more than 2 resource types • cpu, ram, network, dis... | EPFL CS 460 Moodle |
No question: Moodle | ##oop yarn : yet another resource negotiator ", acm cloud computing 2013 • p delgado, f dinu, am kermarrec, w zwaenepoel, “ hawk : hybrid datacenter scheduling ”, usenix atc, 2015 cs - 460 48 hawk : hybrid datacenter scheduling usenix, atc 2015 cs - 460 49 centralized schedulers cs - 460 50 centralized schedulers cs - ... | EPFL CS 460 Moodle |
No question: Moodle | work stealing cs - 460 67 under high load - > high probablity of contacting high - loaded nodes steal from them hawk : cluster partitioning cs - 460 68 hawk : cluster partitioning cs - 460 69 references • b. hindman et al., “ mesos : a platform for fine - grained resource sharing in the data center ", usenix 2011 • a. ... | EPFL CS 460 Moodle |
No question: Moodle | data spark sql graph data pregel, graphlab, x - streem, chaos machine learning week 12 batch data map reduce, dryad, spark streaming data storm, naiad, flink, spark streaming google data flow scheduling ( mesos ) - week 10 query optimization storage hierarchies & layouts transaction management query execution stream pr... | EPFL CS 460 Moodle |
No question: Moodle | results • do not need to accumulate data for processing • streaming operators typically require less memory • disadvantages of stream processing • some operators are harder to implement with streaming • stream algorithms are often approximations cs - 460 6 example • recommender system • every time someone loads a page ... | EPFL CS 460 Moodle |
No question: Moodle | ( subscriptions ) • a set of publishers / producers issue some events ( events ) • publish - subscribe system 1. manages users subscriptions 2. matches published events against subscriptions 3. disseminate events to matching subscribers ( and no others ) • flexible and seamless messaging substrate for applications subs... | EPFL CS 460 Moodle |
No question: Moodle | subscriptions ( subscribe / uns ubscribe ) event dissemination subscriber publish ( ) notify ( ) synchronization decoupling : producers are not blocked upon publication, subscribers are asynchronously notified pub - sub systems : expressiveness • differences in subscription expressiveness • topic - based ~ application ... | EPFL CS 460 Moodle |
No question: Moodle | overlay network used for group naming and group localization • flooding - based multicast [ can multicast ] : • creation of a specific network for each group • message flooded along the overlay links • tree - based multicast [ bayeux, scribe ] • creation of a tree per group • flooding along the tree branches cs - 460 2... | EPFL CS 460 Moodle |
No question: Moodle | packets are replicated low in the tree cs - 460 24 scribe : join ( group ) 1100 1101 1001 0100 0111 1011 1111 1100 0111 0100 1000 1111 1000 1101 1001 1011 cs - 460 25 scribe : message dissemination multicast ( group, m ) • routing through pastry to the root key = groupeid • flooding along the tree branches from the roo... | EPFL CS 460 Moodle |
No question: Moodle | ##ip - mcast • stress on each network link • load on each node • number of entries in the routing table • number of entries in the forwarding tables • experimental set - up • georgia tech transit - stub model ( 5050 core routers ) • 100 000 nodes chosen at random among 500 000 • zipf distribution for 1500 groups • band... | EPFL CS 460 Moodle |
No question: Moodle | • good support for large - scale distributed applications • alm infrastructure • scribe exhibits good performances / ip multicast • large size groups • large number of groups • good load - balancing properties cs - 460 kafka kafka : a distributed messaging system for log processing developed by linkedin, now apache, wr... | EPFL CS 460 Moodle |
No question: Moodle | 460 39 kafka in a nutshell • producers write data to brokers. • consumers read data from brokers ( pull model ) • distributed, run in a cluster • the data • data is stored in topics. • topics are split into partitions, which are replicated. 40 producer consumer producer broker broker broker broker consumer zk cs - 460 ... | EPFL CS 460 Moodle |
No question: Moodle | ##able cs - 460 43 kafka producer cs - 460 44 producer kafka broker consumer alice users topic publish “ alice ” to users topic topic, position append only ana jeanne consume “ alice ” to users topic kafka partitions cs - 460 45 producer kafka broker consumer alice users topic ana jeanne malo medhi partition 1 ( a - k ... | EPFL CS 460 Moodle |
No question: Moodle | re - consume data. • delivery guarantees • kafka guarantees that messages from a single partition are delivered to a consumer in order. • there is no guarantee on the ordering of messages coming from different partitions. • kafka only guarantees at - least - once delivery ( the client needs to check for duplicate ) • k... | EPFL CS 460 Moodle |
No question: Moodle | . zaharia et al., o'reilly media, 2018 - chapter 20 fundamentals of stream processing : application design, systems and analytics. h. andrade et al., cambridge university press, 2014 - chapter 1 - 5, 7, 9 high - availability algorithms for distributed stream processing. j. hwang et al., icde 2005 cs - 460 53 references... | EPFL CS 460 Moodle |
No question: Moodle | content - addressable networks » ( 2001 ) lecture notes in computer science, ngc 2001 london. • d. kostic, a. rodriguez, j. albrecht, and a. vahdat. « bullet : high bandwidth data dissemination using an overlay mesh ». in 19th acm symposium on operating systems principles, october 2003. cs - 460 54 cs460 systems for da... | EPFL CS 460 Moodle |
No question: Moodle | , convenient, and safe multi - user storage of and access to massive amounts of persistent data. reliable convenient safe multi - user massive persistent efficient extremely large ( often exabytes every day ) data outlives the programs that operate on it thousands of queries / updates per second 24x7 availability 6 wha... | EPFL CS 460 Moodle |
No question: Moodle | / privacy data integrity differential privacy cryptography data ethics biases ( data and algorithms ) impact on society regulations data engineering big data management data preparation large - scale deployment cs460 cs460 landscape 12 consistency protocols cap theorem gossip protocols distributed / decentralized syste... | EPFL CS 460 Moodle |
No question: Moodle | science breadth coverage exercises put the course in practice programming skills exam preparation background for the project project acquaintance with a real platform going in depth intended as a practical work may not be related to every part of the course cs460 ta / ae team 15 hamish ( ta ) martijn ( head ta ) yi ( t... | EPFL CS 460 Moodle |
No question: Moodle | ##ggregated storage ) have been growing at a historic rate over the past 10 years 0 20 40 60 80 100 120 140 160 180 200 data size ( zb ) explore data efficiently data age 2025, data from idc global datasphere, nov 2018 33 % of data is inaccurate in some way processing technology grows much slower than data how much dat... | EPFL CS 460 Moodle |
No question: Moodle | & data layout transaction management query execution 22 today ’ s topic ( simplified ) dbms architecture 23 recovery manager transaction manager files and access methods buffer management parser + optimizer + plan execution web forms application front ends sql interface sql commands storage management data today ’ s to... | EPFL CS 460 Moodle |
No question: Moodle | like dram, low - latency loads and stores • like ssd, persistent writes and high density • byte - addressable dram nvm ssd • ssd technology uses non - volatile flash chips package multiple flash chips into a single closure • ssd controller embedded processor that executes firmware - level software bridges flash chips t... | EPFL CS 460 Moodle |
No question: Moodle | heap files : best when typical access is a full file scan • sorted files : best for retrieval in an order, or for retrieving a ‘ range ’ • log - structured files : best for very fast insertions / deletions / updates 33 heap ( unordered ) files • simplest file structure – contains records in no particular order – need t... | EPFL CS 460 Moodle |
No question: Moodle | sequential transfers. 37 writing to log - structured files – inserts : store the entire tuple – deletes : mark tuple as deleted – updates : store delta of just the attributes that were modified 38 log file insert id = 1, val = a insert id = 2, val = b delete id = 4 insert id = 3, val = c update val = x ( id = 3 ) … rea... | EPFL CS 460 Moodle |
No question: Moodle | compaction ) – data can be lost if written but not checkpointed • dbms needs to address two issues – how to reconstruct tuples from logs efficiently – how to manage disk space with ever - growing logs 41 storage management : outline – storage technologies – file storage – buffer management ( refresher ) – page layout •... | EPFL CS 460 Moodle |
No question: Moodle | is not in pool : – choose a frame for replacement ( only un - pinned pages are candidates ) – if frame is “ dirty ”, write it to disk – read requested page into chosen frame • pin the page and return its address. 45 * if requests can be predicted ( e. g., sequential scans ) pages can be pre - fetched several pages at a... | EPFL CS 460 Moodle |
No question: Moodle | better in this situation ( but not in all situations, of course ). 48 sequential flooding – illustration 49 1 2 3 4 5 6 1 2 buffer pool lru : mru : repeated scan of file … buffer pool 1 2 4 3 5 2 4 3 5 6 4 3 5 6 4 1 5 6 2 1 1 2 4 3 1 2 5 3 1 2 6 3 1 2 6 3 1 2 6 3 3 4 5 6 5 1 2 3 4 6 1 2 “ clock ” replacement policy • a... | EPFL CS 460 Moodle |
No question: Moodle | uniq > - > < page _ id, slot _ number > • page format should support – fast searching, inserting, deleting • page format depends on record format – fixed - length – variable - length 52 record formats : fixed - length • schema is stored in system catalog – number of fields is fixed for all records of a table – domain i... | EPFL CS 460 Moodle |
No question: Moodle | and no longer fits? – shift all subsequent fields • if record no longer fits in page? – move a record to another page after modification • what if record size > page size? – limit allowed record size 57 storage management : outline – storage technologies – file storage – buffer management ( refresher ) – page layout • ... | EPFL CS 460 Moodle |
No question: Moodle | compression algorithm – sequences of redundant data are stored as a single data value compression 63 dept hr hr sales it it cdept ( 2 x hr ) ( 1 x sales ) ( 2 x it ) compression ( 2 ) • bit - vector encoding : compact and constant - time test – useful when we have categorical data & useful when a few distinct values – ... | EPFL CS 460 Moodle |
No question: Moodle | name from tbl where cdept = 1 – per - page dictionaries? • bit - vector encoding = > find the 1 ’ s directly from the bit vectors select count ( * ) from tbl where cdept = “ hr ” • run - length encoding = > batch processing ( aggregation ) dsm : writes • row insertions / deletions – affects all columns – multiple i / o... | EPFL CS 460 Moodle |
No question: Moodle | friendly with slotted - pages nsm i / o pattern column “ stitching ” delay per - column tuple ids only relevant attributes to cache pax americana • dsm most suitable for analytical queries, but required major rewrites of existing dbms, and penalized transactions a lot. • pax replaces nsm in - place – monetdb / x100 ( v... | EPFL CS 460 Moodle |
No question: Moodle | column store database systems. foundations and trends in databases, vol. 5, no. 3, pp. 227 - 263 only, 2013. available online at : stratos. seas. harvard. edu / files / stratos / files / columnstoresfntdbs. pdf • a. ailamaki et al. : weaving relations for cache performance. vldb 2001 • https : / / blog. twitter. com / ... | EPFL CS 460 Moodle |
No question: Moodle | systems data science software stack data processing ressource management & optimization data storage distribute d file systems ( gfs ) nosql db dynamo big table cassandra distributed messging systems kafka structured data spark sql graph data pregel, graphlab, x - streem, chaos machine learning batch data map reduce, d... | EPFL CS 460 Moodle |
No question: Moodle | at - a - time ) 6 iterator model ( volcano model ) each query operator implements a next function. • on each invocation, the operator returns either a single tuple or a marker that there are no more tuples • next calls next on the operator ’ s children to retrieve and process their tuples 7 common operator interface = ... | EPFL CS 460 Moodle |
No question: Moodle | expression pred generator < tuple > next ( ) : for t in input. next ( ) : if pred ( t ) emit t class operator : generator < tuple > next ( ) example : iterator model 9 example : iterator model ( cont ) 10 1 2 3 4 5 ( interpreted ) expression evaluation nodes in the tree represent different expression types : • comparis... | EPFL CS 460 Moodle |
No question: Moodle | to cover every table, datatype, query processing model the processing model of a dbms defines how the system executes a query plan. – different trade - offs for different workloads • extreme i : tuple - at - a - time via the iterator model • extreme ii : block - oriented model ( typically column - at - a - time ) 13 bl... | EPFL CS 460 Moodle |
No question: Moodle | 2 3 the ( output ) materialization problem – version 2 select name from tbl where age > 20 and dept = “ hr ” 17 name john jack jane age 22 19 37 dept hr hr it tbl tid 1 2 3 tbl > 20 = name john tid 1 3 tid 1 tid 1 tid as extra filter to reduce output! can we reduce it further? the ( output ) materialization problem – s... | EPFL CS 460 Moodle |
No question: Moodle | calls - > no per - tuple overhead combined with columnar storage ▪cache - friendly ▪simd - friendly ▪ “ run same operation over consecutive data ” interpretation when evaluating expressions ( in most cases ) – typically use macros to produce 1000s of micro - operators (!!! ) • selection _ gt _ int32 ( int * in, int pre... | EPFL CS 460 Moodle |
No question: Moodle | implements a next function • each operator emit a vector of tuples instead of a single tuple – vector - at - a - time, aka “ carry a crate of beers at a time ”! – the operator ’ s internal loop processes multiple tuples at a time. – vector size varies based on hardware or query properties • general idea : vector must f... | EPFL CS 460 Moodle |
No question: Moodle | by 25 processing model the processing model of a dbms defines how the system executes a query plan. – different trade - offs for different workloads • extreme i : tuple - at - a - time via the iterator model • query compilation • vectorization model • extreme ii : block - oriented model ( typically column - at - a - ti... | EPFL CS 460 Moodle |
No question: Moodle | for code generation transpilation • dbms converts a query plan into imperative source code • compile the produced code to generate native code with a conventional compiler jit compilation • generate an intermediate representation ( ir ) of the query that can be quickly compiled into native code. 30 transpilation use ca... | EPFL CS 460 Moodle |
No question: Moodle | # predicate _ offset = # # # parameter _ value = # # # for t in range ( table. num _ tuples ) : tuple = table. data + t ∗ tuple _ size val = ( tuple + predicate _ offset ) if ( val = = parameter _ value + 1 ) : emit ( tuple ) known at query compile time integrating with the rest of the dbms • the generated query code c... | EPFL CS 460 Moodle |
No question: Moodle | system • hique : holistic integrated query engine • for a given query plan, create a c program that implements that query ’ s execution plan. → bake in all the predicates and type conversions. • advantages : – fewer function calls during query evaluation – generated code uses cache - resident data more efficiently – co... | EPFL CS 460 Moodle |
No question: Moodle | ) : emit ( tuple ) known at query compile time integrating with the rest of the dbms • the generated query code can invoke any other function in the dbms →no need to generate code for the whole db! • re - use the same components as interpreted queries. – concurrency control – logging and checkpoints – indexes 39 indica... | EPFL CS 460 Moodle |
No question: Moodle | + 1 templated plan tuple _ size = # # # predicate _ offset = # # # parameter _ value = # # # for t in range ( table. num _ tuples ) : tuple = table. data + t ∗ tuple _ size val = * ( tuple + predicate _ offset ) if ( val = = parameter _ value + 1 ) : emit ( tuple ) interpreted plan for t in range ( table. num _ tuples ... | EPFL CS 460 Moodle |
No question: Moodle | ) … execute ( op - n ) select a. a + b. b from a, b where a. val =? + 1 and b. c = a. d σ generated code : more specialization 46 tuple _ size = # # # predicate _ offset = # # # ( val _ offset ) parameter _ value = # # # for t in range ( table. num _ tuples ) : tuple = table. data + t ∗ tuple _ size val = * ( tuple + p... | EPFL CS 460 Moodle |
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