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1 Fattane Zarrinkalam کارگاه ساالنه آزمایشگاه فناوری وب 1391 زمستان

2 Outlines Introduction DataModel Architecture HBase vs. RDBMS HBase users 2

3 Why Hadoop? Datasets are growing to Petabytes Traditional datasets are expensive to scale and inherently difficult to distribute Need batch processing 3

4 Hadoop Hadoop Distributed Filesystem Scalable distributed file system which uses a cluster of commodity hardware to store huge amounts of data in terms of very large files Built-in support for data replication between nodes Optimized for streaming reads so that data could be read for processing later on

5 Hadoop Hadoop MapReduce HDFS plus MapReduce forms the backbone for processing massive amounts of data For instance the entire search index of Google 5

6 Why Hbase? Problems with Hadoop Good with a few very, very large files, but not as good with millions of tiny files Not intended for real time querying Does not support random access It is not a general purpose file system does not provide fast individual record lookups in files HBase has evolved to address these challenges 6

7 History of Hbase HBase is an open source implementation of Google s BigTable BigTable: A Distributed Storage System for Structured Data published November 2006 A solution that could drive interactive applications Uses the same infrastructure and relying on GFS for replication and data availability Data stored should be composed of much smaller entities, and the system would store transparently, take care of aggregating the small records into very large files, and offer some sort of indexing that allows the user to retrieve data with a minimal number of disk seeks 7

8 What is Hbase? Distributed, Column-Oriented, Multi-Dimensional, High-Availability, High-Performance Storage System Project Goals Billion of Rows * Million of Columns * thousand of Versions Petabyte across thousands of commodity servers 8

9 Hbase is not A SQL Database No join No query engine No types No SQL A drop- in replacement for your RDBMS 9

10 Data Model Applications store data into labeled tables. Table consists of Rows, each which has a row key This can be thought of as a primary index on the row key! Row keys are always unique 10

11 Data Model Table rows are sorted lexicographically by their row key hbase(main):001:0> scan 'table1' ROW COLUMN+CELL row-1 column=cf1:, timestamp= row-10 column=cf1:, timestamp= row-11 column=cf1:, timestamp= row-2 column=cf1:, timestamp= row-22 column=cf1:, timestamp= row-3 column=cf1:, timestamp= row-abc column=cf1:, timestamp= row(s) in seconds 11

12 Data Model Each Row may have any number of columns Columns are grouped into column families. All column family members have a common prefix for example, the columns temperature:air and temperature: dew_point are both members of the temperature column family, whereas station:identifier belongs to the station family. the colon character (:) delimits the column family from the column family qualifier. 12

13 Data Model A table s column families must be specified up front as part of the table schema definition New column family members can be added on demand. For example, a new column station:address can be offered by a client as part of an update, and its value persisted, as long as the column family station is already in existence on the targeted table. 13

14 Data Model Physically, all column family members are stored together on the filesystem. So, though earlier we described HBase as a columnoriented store, it would be more accurate if it were described as a column-family-oriented store. A note on the NULL value In RDBMS NULL cells need to be set and occupy space In HBase, NULL cells or columns are simply not stored 14

15 Data Model 15

16 Data Model All table accesses are via the table row key. Table cells the intersection of row and column coordinates are versioned (implicitly or explicitly). By default, their version is a timestamp auto-assigned by HBase at the time of cell insertion. This can be used to save multiple versions of a value that changes over time Versions are stored in decreasing timestamp, most recent first Access to data (Table, RowKey, Family, Column, Timestamp) -> Value

17 Data Model

18 Data Model In synopsis, HBase tables are like those in an RDBMS, only cells are versioned, rows are sorted, and columns can be added on the fly by the client as long as the column family they belong to preexists. 18

19 Data Model Tables are automatically partitioned horizontally by HBase into regions. Regions are contiguous ranges of rows stored together Regions are dynamically split by the system when they become too large Regions can also be merged to reduce the number of storage files Each region is served by exactly one Region Server Region servers can serve multiple regions Fine-grained Load Balancing is also achieved using regions as they can be easily moved across servers 19

20 Data Model Regions in practice Initially, there is one region System monitors region size: if a threshold is attained, SPLIT Regions are split in two at the middle key This creates roughly two equivalent (in size) regions Region is the basic unit of scalability and load balancing

21 Data Model

22 A canonical use case of Hbase Webtable The web pages stored while crawling the Internet. The row key is the reversed URL of the page For example, org.hbase.www. There is a column family storing: The actual HTML code Anchor» It is used to store outgoing links Inbound links For metadata like language.

23 A canonical use case of Hbase Using multiple versions for the contents family allows you to store a few older copies of the HTML It is helpful when you want to analyze how often a page changes, for example. The timestamps used are the actual times when they were fetched from the crawled website. 23

24 A canonical use case of Hbase Column Family Row key TimeStamp value

25 Architecture: overview There are three major components: Master server Region server Client API Make use of existing system, like HDFS and ZooKeeper

26 Architecture: overview 1. Master server Assigning regions to region servers using Apache ZooKeeper handling load balancing of regions across region servers The master is not part of the actual data storage or retrieval path It negotiates load balancing and maintains the state of the cluster It takes care of schema changes

27 Architecture: overview 2. Region server Region servers are responsible for all read and write requests for all regions they serve Split regions that have exceeded the configured region size thresholds. 27

28 Architecture: overview 3. Client API Get Put Scan Returns attributes for a specified row. Either adds new rows to a table (if the key is new) or can update existing rows (if the key already exists). Allow iteration over multiple rows for specified attributes. Delete Removes a row from a table. 28

29 Architecture

30 Architecture Zookeeper Distributed, highly available coordination service Role of Zookeeper in Hbase Master uses Zookeeper to discover available servers at start and track server failures Zookeeper provide client with the server name hosts the ROOTregion. With this information it can query that region server to get the server name that hosts the.meta. table region containing the row key in question. Lastly it can query the reported.meta. Server and retrieve the server name that has the region containing the row key the client looking for.

31 Architecture HDFS A distributed file system that runs on large clusters of commodity machines Hbase using HDFS for its underlying storage The store files are typically saved in HDFS, which provides a scalable, persistent, replicated storage layer for HBase. 31

32 Architecture Store A Store hosts a MemStore and StoreFiles (HFiles). A Store corresponds to a column family for a table for a given region. MemStore Holds in-memory modifications to the Store until enough is collected then flush to disk, avoiding creation of too many small files. 32

33 Architecture StoreFile (HFile) Store files are divided up into smaller blocks when stored within the Hadoop Distributed Filesystem (HDFS). Write-Ahead Log (HLog) data resides in memory is volatile, meaning it could be lost if server looses power. Data is written to the WAL and then passed to MemStore. 33

34 Operation: write

35 Operation: Read Check for data in cache Note that data in MemStore is sorted by keys, matching what happens in the HFiles. Look for data in persisted data 35

36 Operation: Delete Since HFiles are immutable, how can we delete data? A delete marker is written to indicate that a given key is deleted During the read process, data marked as deleted is skipped 36

37 37

38 Comparison #1 System to store a shopping cart Customers, Products, Orders 38

39 Simple SQL Schema 39

40 Simple Hbase Schema 40

41 Efficient Queries with Both Get name, , orders for customer Get name, price for product Get customer, stamp, total for order Get list of products in order 41

42 Where SQL Makes Life Easy Joining In a single query, get all products in an order with their products information Secondary Indexing Get customerid by Referential Integrity Deleting an order would delete links out of orderproducts ID updates propagate Realtime Analysis GROUP BY and ORDER BY allow for simple statistical analysis 42

43 Where HBase Makes Life Easy Dataset Scale We have 1M customers and 100M products Product information includes large text datasheets or PDF files Want to track every time a customer looks at a product page Read/Write Scale Tables distributed across nodes means reads/writes are fully distributed Writers are extremely fast and require no index updates Replication Comes for free Batch Analysis Massive and convoluted SQL queries executed serially become efficient MapReduce jobs distributed and executed in parallel 43

44 Conclusion For small instances of simple/straightforward systems, relational databases offer a much more convenient way to model and access data Can outsource most work to transaction and query engine HBase will force you to pull complexity into Application layer Once you need to scale, the properties and flexibility of HBase can relieve you from the headaches associated with scaling an RDBMS 44

45 Comparison #2 Compare key factors Hardware Requirements Scalability Reliability Ease of Use Cost 45

46 Hardware Requirements RDBMS are IO-bound Typically require large arrays of fast and expensive disks Modest production environment might have a single node with k RPM drivers, 16 cores, and GB RAM Requires a backup server with similar specs $$$$$$ HBase is designed for commodity hardware Biggest factor for performance is number of nodes Modest production environment might have nodes each with 2 500GB 7.2k RPM drivers, 4 cores, and 4GB RAM Common to have one master node with RAID, dual PSU, etc as this is currently a SPOF 46

47 Scalability RDBMS scale achieved through Caching, e.g. through Mamcached Partitioning often left up to the application or external tools Replication can be built-in or an add-on with most popular RDBMS Regardless of scale mechanisms architecture does not allow efficient multi-master support HBase scales out of the box Random access often made faster with something similar to Mamcached (built-in with 0.20 release) Constant performance from low to high concurrency Writes are distributed and there are no indexed Scale by plugging in more RegionServers 47

48 Reliability RDBMS Slave replication Warm/Hot backups Single node failure is often catastrophic HBase Replication is built-in Backups are unnecessary but available 48

49 Ease of Use RDBMS Millions are trained in SQL and relational data model Normalized schemas are well understood and have predictable performance However schemas are often limiting, difficult to change, and scale poorly HBase and MapReduce Significant learning curve Both have excellent communities and increasing numbers of tools to help ease of the initial pain Schemas are loosely defined so data structure is easy to change and performance is constant 49

50 Other factors Operating System / Architecture RDBMS vary greatly on their target architecture HBase designed for Linux though also being run on Solaris and with some success on Windows Cost HBase is FOSS Plenty of mature FOSS RDBMS, but many used in enterprise are expensive Widespread use RDBMS are tried and true Hadoop and HBase are still in development and though production ready are not yet in wide use 50

51 Conclusion Similar to the first comparison RDBMS provide tremendous functionality out of the box but is extremely difficult and costly to scale HBase provides barebones functionality out of the box but scaling is built-in and inexpensive 51

52 Users 52

53 Users: Facebook-messaging system 53

54 Old version: Facebook- messaging system Chat messages were held in memory and stored only for a small number of days Non-chat messages were stored in MySQL 54

55 Facebook- messaging system New version: Started in Dec 2009 Brings together chat, and the earlier version of messages into one umbrella. Needs 8B+ messages/day A storage system that could efficiently support a 20x increase in the amount of writes, Providing cheap and elastic storage for datasets that we were expecting to grow at 150TB+/month. 55

56 Facebook- messaging system Solution : HBase Traffic to HBase 75+ Billion R+W ops/day At peak: 1.5M ops/sec ~ 55% Read vs. 45% Write ops Avg write op inserts ~16 records across multiple column families 56

57 Users: Mozilla-Socorro 57

58 Mozilla-Socorro The crash-reports can help the software developers to diagnose and fix the root cause of the crashes. The automatic collection of crash-reports in Mozilla Firefox improved the reliability of Mozilla Firefox by 40% from November 2009 to March

59 Mozilla-Socorro Challenge Organizations to manage large amount of collected crashreports effectively 2.5 million crash-reports every day Around 320Gb each day! Solution: Socorro is Mozilla s crash reporting system Data storage and analytics are built on HBase 59

60 Users: OpenTSDB Web-based products serving millions of users typically have hundreds or thousands of servers in their back-end infrastructure. serving traffic, capturing logs, storing data, processing data, and so on. To keep the products up and running, it s critical to monitor the health of the servers as well as the software running on these servers. 60

61 Users: OpenTSDB Challenge Monitoring the entire stack at scale requires systems that can collect and store metrics of all kinds from different sources. make metrics be available for access over a long period of time Solution: OpenTSDB An open source framework that allows the company to collect metrics of all kinds into a single system. This framework uses HBase at its core to store and access the collected metrics. 61

62 When to use HBase? Storing large amounts of data (100s of TBs) Seed to scale gracefully with data for structured and semi-structured data Need efficient random access (key lookups) within large data sets Don t need full RDMS capabilities (cross row/cross table transactions, joins, etc.) 62

63 63

64 Architecture LogSyncer Every time an edit is sent to the servers, a call to sync() is initiated, it is a call that forces the update to the log so that you have durability. LogRoller Make sure a log is persisted on a regular basis. Every 60 minutes, the log is closed and a new one starts. It checks what the highest sequence number written to a storage file, delete all logs with smallest sequence numbers and leaves all others as they are still needed, and this to optimize the sapce needed to save HLog files.

65 Architecture Region lookup 65

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