Introduction to cloud computing Jiaheng Lu Department of Computer Science Renmin University of China www.jiahenglu.net

Cloud computing

Google Cloud computing techniques

The Google File System

The Google File System (GFS) A scalable distributed file system for large distributed data intensive applications Multiple GFS clusters are currently deployed. The largest ones have: 1000+ storage nodes 300+ TeraBytes of disk storage heavily accessed by hundreds of clients on distinct machines

Introduction Shares many same goals as previous distributed file systems performance, scalability, reliability, etc GFS design has been driven by four key observation of Google application workloads and technological environment

Intro: Observations 1 1. Component failures are the norm constant monitoring, error detection, fault tolerance and automatic recovery are integral to the system 2. Huge files (by traditional standards) Multi GB files are common I/O operations and blocks sizes must be revisited 1. GFS built from hundreds/thousands of machines built from inexpensive commodity parts and accessed by a comparable # of client machines

Intro: Observations 2 3. Most files are mutated by appending new data This is the focus of performance optimization and atomicity guarantees 4. Co-designing the applications and APIs benefits overall system by increasing flexibility 3. Rather than overwriting old data, Random writes virtual non existent, Reads are almost all sequential

Files are broken into chunks. The Design Cluster consists of a single master and multiple chunkservers and is accessed by multiple clients Files are broken into chunks.

The Master Maintains all file system metadata. names space, access control info, file to chunk mappings, chunk (including replicas) location, etc. Periodically communicates with chunkservers in HeartBeat messages to give instructions and check state Controls system wide activities (more later)

The Master Helps make sophisticated chunk placement and replication decision, using global knowledge For reading and writing, client contacts Master to get chunk locations, then deals directly with chunkservers Master is not a bottleneck for reads/writes Usually asks for locations of more than one chunk in one request

Chunkservers Files are broken into chunks. Each chunk has a immutable globally unique 64-bit chunk-handle. handle is assigned by the master at chunk creation Chunk size is 64 MB Each chunk is replicated on 3 (default) servers

Clients Linked to apps using the file system API. Communicates with master and chunkservers for reading and writing Master interactions only for metadata Chunkserver interactions for data Only caches metadata information Data is too large to cache.

Chunk Locations Master does not keep a persistent record of locations of chunks and replicas. Polls chunkservers at startup, and when new chunkservers join/leave for this. Stays up to date by controlling placement of new chunks and through HeartBeat messages (when monitoring chunkservers)

Operation Log Record of all critical metadata changes Stored on Master and replicated on other machines Defines order of concurrent operations Changes not visible to clients until they propigate to all chunk replicas Also used to recover the file system state Central to GFS Recovering FS state: checkpoints to state when log grows to some size. Loads from last checkpoint and replays records after that. Master starts new log file and creates the checkpoint in a separate thread. When checkpoint is created (a few minutes), it is stored locally and remotely. Can delete older checkpoints and log files

System Interactions: Leases and Mutation Order Leases maintain a mutation order across all chunk replicas Master grants a lease to a replica, called the primary The primary choses the serial mutation order, and all replicas follow this order Minimizes management overhead for the Master Mutation is an Op to change the contents/metadata of a chunk. Performed on all replicas FOR CONCURRENT WRITES: writes may be interleaved with and overwritten by concurrent OPS from other clients. The shared region may end up containing fragments from diff clients, HOWEVER replicas will be identical because the indiv ops are done in the same order.

Leases and Mutation Order System Interactions: Leases and Mutation Order 1&2. client gets chunk location. 3. Client pushes data to replicas. (replicas forward data once they receive over TCP it like a pipeline to achieve max BW. push to next closest replica) 4. client sends write req. to primary. saying write to this offset 5. primary forwards request to replicas. 6. replicas reply to primary when done. 7. primary replies to client, gives errors if there were any

Atomic Record Append Client specifies the data to write; GFS chooses and returns the offset it writes to and appends the data to each replica at least once Heavily used by Google’s Distributed applications. No need for a distributed lock manager GFS choses the offset, not the client Traditionally, the client specifies the offset to write to. Concurrent writes to the same region are not serializable: the section may contain data from different writers.

Atomic Record Append: How? Follows similar control flow as mutations Primary tells secondary replicas to append at the same offset as the primary If a replica append fails at any replica, it is retried by the client. So replicas of the same chunk may contain different data, including duplicates, whole or in part, of the same record

Atomic Record Append: How? GFS does not guarantee that all replicas are bitwise identical. Only guarantees that data is written at least once in an atomic unit. Data must be written at the same offset for all chunk replicas for success to be reported.

Replica Placement Placement policy maximizes data reliability and network bandwidth Spread replicas not only across machines, but also across racks Guards against machine failures, and racks getting damaged or going offline Reads for a chunk exploit aggregate bandwidth of multiple racks Writes have to flow through multiple racks tradeoff made willingly NOTE: there are hundreds of chunkservers spread across many machine racks

Chunk creation created and placed by master. placed on chunkservers with below average disk utilization limit number of recent “creations” on a chunkserver with creations comes lots of writes

Detecting Stale Replicas Master has a chunk version number to distinguish up to date and stale replicas Increase version when granting a lease If a replica is not available, its version is not increased master detects stale replicas when a chunkservers report chunks and versions Remove stale replicas during garbage collection Client is given the version number when requesting a chunks location so it can verify it is using the most up to date replica

Garbage collection When a client deletes a file, master logs it like other changes and changes filename to a hidden file. Master removes files hidden for longer than 3 days when scanning file system name space metadata is also erased During HeartBeat messages, the chunkservers send the master a subset of its chunks, and the master tells it which files have no metadata. Chunkserver removes these files on its own

Fault Tolerance: High Availability Fast recovery Master and chunkservers can restart in seconds Chunk Replication Master Replication “shadow” masters provide read-only access when primary master is down mutations not done until recorded on all master replicas

Fault Tolerance: Data Integrity Chunkservers use checksums to detect corrupt data Since replicas are not bitwise identical, chunkservers maintain their own checksums For reads, chunkserver verifies checksum before sending chunk Update checksums during writes FOR READ, returns an error if checksum doesn’t match updating CS for WRITES... OPTIMIZED for append writes, since that’s what’s dominant

Introduction to MapReduce 28

MapReduce: Insight ”Consider the problem of counting the number of occurrences of each word in a large collection of documents” How would you do it in parallel ? 29

MapReduce Programming Model Inspired from map and reduce operations commonly used in functional programming languages like Lisp. Users implement interface of two primary methods: 1. Map: (key1, val1) → (key2, val2) 2. Reduce: (key2, [val2]) → [val3] 30

Map operation Map, a pure function, written by the user, takes an input key/value pair and produces a set of intermediate key/value pairs. e.g. (doc—id, doc-content) Draw an analogy to SQL, map can be visualized as group-by clause of an aggregate query. 31

Reduce operation On completion of map phase, all the intermediate values for a given output key are combined together into a list and given to a reducer. Can be visualized as aggregate function (e.g., average) that is computed over all the rows with the same group-by attribute. 32

Pseudo-code for each word w in input_value: EmitIntermediate(w, "1"); map(String input_key, String input_value): // input_key: document name // input_value: document contents for each word w in input_value: EmitIntermediate(w, "1"); reduce(String output_key, Iterator intermediate_values): // output_key: a word // output_values: a list of counts int result = 0; for each v in intermediate_values: result += ParseInt(v); Emit(AsString(result)); 33

MapReduce: Execution overview 34

MapReduce: Example 35

MapReduce in Parallel: Example 36

MapReduce: Fault Tolerance Handled via re-execution of tasks. Task completion committed through master What happens if Mapper fails ? Re-execute completed + in-progress map tasks What happens if Reducer fails ? Re-execute in progress reduce tasks What happens if Master fails ? Potential trouble !! 37

Walk through of One more Application MapReduce: Walk through of One more Application 38

39

MapReduce : PageRank PageRank models the behavior of a “random surfer”. C(t) is the out-degree of t, and (1-d) is a damping factor (random jump) The “random surfer” keeps clicking on successive links at random not taking content into consideration. Distributes its pages rank equally among all pages it links to. The dampening factor takes the surfer “getting bored” and typing arbitrary URL. 40

PageRank : Key Insights Effects at each iteration is local. i+1th iteration depends only on ith iteration At iteration i, PageRank for individual nodes can be computed independently 41

PageRank using MapReduce Use Sparse matrix representation (M) Map each row of M to a list of PageRank “credit” to assign to out link neighbours. These prestige scores are reduced to a single PageRank value for a page by aggregating over them. 42

PageRank using MapReduce Map: distribute PageRank “credit” to link targets Reduce: gather up PageRank “credit” from multiple sources to compute new PageRank value Iterate until convergence Source of Image: Lin 2008 43

Phase 1: Process HTML Map task takes (URL, page-content) pairs and maps them to (URL, (PRinit, list-of-urls)) PRinit is the “seed” PageRank for URL list-of-urls contains all pages pointed to by URL Reduce task is just the identity function 44

Phase 2: PageRank Distribution Reduce task gets (URL, url_list) and many (URL, val) values Sum vals and fix up with d to get new PR Emit (URL, (new_rank, url_list)) Check for convergence using non parallel component 45

MapReduce: Some More Apps Distributed Grep. Count of URL Access Frequency. Clustering (K-means) Graph Algorithms. Indexing Systems MapReduce Programs In Google Source Tree 46

MapReduce: Extensions and similar apps PIG (Yahoo) Hadoop (Apache) DryadLinq (Microsoft) 47

Large Scale Systems Architecture using MapReduce User App MapReduce Distributed File Systems (GFS) 48

BigTable: A Distributed Storage System for Structured Data 49

Introduction BigTable is a distributed storage system for managing structured data. Designed to scale to a very large size Petabytes of data across thousands of servers Used for many Google projects Web indexing, Personalized Search, Google Earth, Google Analytics, Google Finance, … Flexible, high-performance solution for all of Google’s products 50

Motivation Lots of (semi-)structured data at Google Scale is large URLs: Contents, crawl metadata, links, anchors, pagerank, … Per-user data: User preference settings, recent queries/search results, … Geographic locations: Physical entities (shops, restaurants, etc.), roads, satellite image data, user annotations, … Scale is large Billions of URLs, many versions/page (~20K/version) Hundreds of millions of users, thousands or q/sec 100TB+ of satellite image data 51

Why not just use commercial DB? Scale is too large for most commercial databases Even if it weren’t, cost would be very high Building internally means system can be applied across many projects for low incremental cost Low-level storage optimizations help performance significantly Much harder to do when running on top of a database layer 52

Goals Want asynchronous processes to be continuously updating different pieces of data Want access to most current data at any time Need to support: Very high read/write rates (millions of ops per second) Efficient scans over all or interesting subsets of data Efficient joins of large one-to-one and one-to-many datasets Often want to examine data changes over time E.g. Contents of a web page over multiple crawls 53

BigTable Distributed multi-level map Fault-tolerant, persistent Scalable Thousands of servers Terabytes of in-memory data Petabyte of disk-based data Millions of reads/writes per second, efficient scans Self-managing Servers can be added/removed dynamically Servers adjust to load imbalance 54

Building Blocks Building blocks: BigTable uses of building blocks: Google File System (GFS): Raw storage Scheduler: schedules jobs onto machines Lock service: distributed lock manager MapReduce: simplified large-scale data processing BigTable uses of building blocks: GFS: stores persistent data (SSTable file format for storage of data) Scheduler: schedules jobs involved in BigTable serving Lock service: master election, location bootstrapping Map Reduce: often used to read/write BigTable data 55

(row, column, timestamp) -> cell contents Basic Data Model A BigTable is a sparse, distributed persistent multi-dimensional sorted map (row, column, timestamp) -> cell contents Good match for most Google applications 56

WebTable Example Want to keep copy of a large collection of web pages and related information Use URLs as row keys Various aspects of web page as column names Store contents of web pages in the contents: column under the timestamps when they were fetched. 57

Rows Name is an arbitrary string Rows ordered lexicographically Access to data in a row is atomic Row creation is implicit upon storing data Rows ordered lexicographically Rows close together lexicographically usually on one or a small number of machines 58

Rows (cont.) Reads of short row ranges are efficient and typically require communication with a small number of machines. Can exploit this property by selecting row keys so they get good locality for data access. Example: math.gatech.edu, math.uga.edu, phys.gatech.edu, phys.uga.edu VS edu.gatech.math, edu.gatech.phys, edu.uga.math, edu.uga.phys 59

Columns Columns have two-level name structure: Column family family:optional_qualifier Column family Unit of access control Has associated type information Qualifier gives unbounded columns Additional levels of indexing, if desired 60

Timestamps Used to store different versions of data in a cell New writes default to current time, but timestamps for writes can also be set explicitly by clients Lookup options: “Return most recent K values” “Return all values in timestamp range (or all values)” Column families can be marked w/ attributes: “Only retain most recent K values in a cell” “Keep values until they are older than K seconds” 61

Implementation – Three Major Components Library linked into every client One master server Responsible for: Assigning tablets to tablet servers Detecting addition and expiration of tablet servers Balancing tablet-server load Garbage collection Many tablet servers Tablet servers handle read and write requests to its table Splits tablets that have grown too large 62

Implementation (cont.) Client data doesn’t move through master server. Clients communicate directly with tablet servers for reads and writes. Most clients never communicate with the master server, leaving it lightly loaded in practice. 63

Tablets Large tables broken into tablets at row boundaries Tablet holds contiguous range of rows Clients can often choose row keys to achieve locality Aim for ~100MB to 200MB of data per tablet Serving machine responsible for ~100 tablets Fast recovery: 100 machines each pick up 1 tablet for failed machine Fine-grained load balancing: Migrate tablets away from overloaded machine Master makes load-balancing decisions 64

Tablet Location Since tablets move around from server to server, given a row, how do clients find the right machine? Need to find tablet whose row range covers the target row 65

Tablet Assignment Each tablet is assigned to one tablet server at a time. Master server keeps track of the set of live tablet servers and current assignments of tablets to servers. Also keeps track of unassigned tablets. When a tablet is unassigned, master assigns the tablet to an tablet server with sufficient room. 66

API Metadata operations Writes (atomic) Reads Create/delete tables, column families, change metadata Writes (atomic) Set(): write cells in a row DeleteCells(): delete cells in a row DeleteRow(): delete all cells in a row Reads Scanner: read arbitrary cells in a bigtable Each row read is atomic Can restrict returned rows to a particular range Can ask for just data from 1 row, all rows, etc. Can ask for all columns, just certain column families, or specific columns 67

Refinements: Locality Groups Can group multiple column families into a locality group Separate SSTable is created for each locality group in each tablet. Segregating columns families that are not typically accessed together enables more efficient reads. In WebTable, page metadata can be in one group and contents of the page in another group. 68

Refinements: Compression Many opportunities for compression Similar values in the same row/column at different timestamps Similar values in different columns Similar values across adjacent rows Two-pass custom compressions scheme First pass: compress long common strings across a large window Second pass: look for repetitions in small window Speed emphasized, but good space reduction (10-to-1) 69

Refinements: Bloom Filters Read operation has to read from disk when desired SSTable isn’t in memory Reduce number of accesses by specifying a Bloom filter. Allows us ask if an SSTable might contain data for a specified row/column pair. Small amount of memory for Bloom filters drastically reduces the number of disk seeks for read operations Use implies that most lookups for non-existent rows or columns do not need to touch disk 70

BigTable: A Distributed Storage System for Structured Data 71

Introduction BigTable is a distributed storage system for managing structured data. Designed to scale to a very large size Petabytes of data across thousands of servers Used for many Google projects Web indexing, Personalized Search, Google Earth, Google Analytics, Google Finance, … Flexible, high-performance solution for all of Google’s products 72

Motivation Lots of (semi-)structured data at Google Scale is large URLs: Contents, crawl metadata, links, anchors, pagerank, … Per-user data: User preference settings, recent queries/search results, … Geographic locations: Physical entities (shops, restaurants, etc.), roads, satellite image data, user annotations, … Scale is large Billions of URLs, many versions/page (~20K/version) Hundreds of millions of users, thousands or q/sec 100TB+ of satellite image data 73

Why not just use commercial DB? Scale is too large for most commercial databases Even if it weren’t, cost would be very high Building internally means system can be applied across many projects for low incremental cost Low-level storage optimizations help performance significantly Much harder to do when running on top of a database layer 74

Goals Want asynchronous processes to be continuously updating different pieces of data Want access to most current data at any time Need to support: Very high read/write rates (millions of ops per second) Efficient scans over all or interesting subsets of data Efficient joins of large one-to-one and one-to-many datasets Often want to examine data changes over time E.g. Contents of a web page over multiple crawls 75

BigTable Distributed multi-level map Fault-tolerant, persistent Scalable Thousands of servers Terabytes of in-memory data Petabyte of disk-based data Millions of reads/writes per second, efficient scans Self-managing Servers can be added/removed dynamically Servers adjust to load imbalance 76

Building Blocks Building blocks: BigTable uses of building blocks: Google File System (GFS): Raw storage Scheduler: schedules jobs onto machines Lock service: distributed lock manager MapReduce: simplified large-scale data processing BigTable uses of building blocks: GFS: stores persistent data (SSTable file format for storage of data) Scheduler: schedules jobs involved in BigTable serving Lock service: master election, location bootstrapping Map Reduce: often used to read/write BigTable data 77

(row, column, timestamp) -> cell contents Basic Data Model A BigTable is a sparse, distributed persistent multi-dimensional sorted map (row, column, timestamp) -> cell contents Good match for most Google applications 78

WebTable Example Want to keep copy of a large collection of web pages and related information Use URLs as row keys Various aspects of web page as column names Store contents of web pages in the contents: column under the timestamps when they were fetched. 79

Rows Name is an arbitrary string Rows ordered lexicographically Access to data in a row is atomic Row creation is implicit upon storing data Rows ordered lexicographically Rows close together lexicographically usually on one or a small number of machines 80

Rows (cont.) Reads of short row ranges are efficient and typically require communication with a small number of machines. Can exploit this property by selecting row keys so they get good locality for data access. Example: math.gatech.edu, math.uga.edu, phys.gatech.edu, phys.uga.edu VS edu.gatech.math, edu.gatech.phys, edu.uga.math, edu.uga.phys 81

Columns Columns have two-level name structure: Column family family:optional_qualifier Column family Unit of access control Has associated type information Qualifier gives unbounded columns Additional levels of indexing, if desired 82

Timestamps Used to store different versions of data in a cell New writes default to current time, but timestamps for writes can also be set explicitly by clients Lookup options: “Return most recent K values” “Return all values in timestamp range (or all values)” Column families can be marked w/ attributes: “Only retain most recent K values in a cell” “Keep values until they are older than K seconds” 83

Implementation – Three Major Components Library linked into every client One master server Responsible for: Assigning tablets to tablet servers Detecting addition and expiration of tablet servers Balancing tablet-server load Garbage collection Many tablet servers Tablet servers handle read and write requests to its table Splits tablets that have grown too large 84

Implementation (cont.) Client data doesn’t move through master server. Clients communicate directly with tablet servers for reads and writes. Most clients never communicate with the master server, leaving it lightly loaded in practice. 85

Tablets Large tables broken into tablets at row boundaries Tablet holds contiguous range of rows Clients can often choose row keys to achieve locality Aim for ~100MB to 200MB of data per tablet Serving machine responsible for ~100 tablets Fast recovery: 100 machines each pick up 1 tablet for failed machine Fine-grained load balancing: Migrate tablets away from overloaded machine Master makes load-balancing decisions 86

Tablet Location Since tablets move around from server to server, given a row, how do clients find the right machine? Need to find tablet whose row range covers the target row 87

Tablet Assignment Each tablet is assigned to one tablet server at a time. Master server keeps track of the set of live tablet servers and current assignments of tablets to servers. Also keeps track of unassigned tablets. When a tablet is unassigned, master assigns the tablet to an tablet server with sufficient room. 88

API Metadata operations Writes (atomic) Reads Create/delete tables, column families, change metadata Writes (atomic) Set(): write cells in a row DeleteCells(): delete cells in a row DeleteRow(): delete all cells in a row Reads Scanner: read arbitrary cells in a bigtable Each row read is atomic Can restrict returned rows to a particular range Can ask for just data from 1 row, all rows, etc. Can ask for all columns, just certain column families, or specific columns 89

Refinements: Locality Groups Can group multiple column families into a locality group Separate SSTable is created for each locality group in each tablet. Segregating columns families that are not typically accessed together enables more efficient reads. In WebTable, page metadata can be in one group and contents of the page in another group. 90

Refinements: Compression Many opportunities for compression Similar values in the same row/column at different timestamps Similar values in different columns Similar values across adjacent rows Two-pass custom compressions scheme First pass: compress long common strings across a large window Second pass: look for repetitions in small window Speed emphasized, but good space reduction (10-to-1) 91

Refinements: Bloom Filters Read operation has to read from disk when desired SSTable isn’t in memory Reduce number of accesses by specifying a Bloom filter. Allows us ask if an SSTable might contain data for a specified row/column pair. Small amount of memory for Bloom filters drastically reduces the number of disk seeks for read operations Use implies that most lookups for non-existent rows or columns do not need to touch disk 92