Non-Relational Databases - PowerPoint Presentation

Non-Relational Databases Jeff Allen

Overview Background Benefits PitfallsAvailable PlatformsSummary

Overview Background Benefits PitfallsAvailable PlatformsSummary “A database is a collection of data.” Ramakrishnan , Gehrke Database Management Systems p.4

Non-Relational Database Many names Key/Value Store (Amazon) Document-Oriented (CouchDB) Attribute-Oriented Distributed Hash Table Sharded Sorted ArraysDistributed Database (non-unique)Contains a collection of data which cannot (necessarily) be described using predetermined relations. No schema defined Scales to petabytes of data across thousands of servers

How It Works Relational Model Non-Relational Model Lookups, insertions, and updates all handled through key (Generally, key would be hashed, not the raw integer) CarID Make Model Interior Color Mileage 13 HondaAccordNULLGreenNULL18MitsubishiNULLGreyNULLNULL Key Value 13 Make=“Honda”, Model=“Accord”, Color=“Green” 18 Make=“Mitsubishi”, Interior=“Grey”

Indexing Storing the value in the hash table on the primary key Redundantly store the “entity model” with each rowMake=“Honda”… This hash table is the only “index” in the database

Overview Background Benefits PitfallsAvailable Platforms Summary “Even though RDBMS have provided database users with the best mix of simplicity, robustness, flexibility, performance, scalability, and compatibility, their performance in each of these areas is not necessarily better than that of an alternate solution pursuing one of these benefits in isolation.” Tony Bain ReadWrite Enterprise

Environments Non-Relational Data Attribute table Different data may be available about different objects Difficult to search or index CarID Make Model Color Year License 1 Honda AccordNULL2002NULL2MitsubishiNULLGreenNULLNULL3NULLEscapeBlack2003 NULL 4 NULL NULL Blue NULL YC3-XYZ 5 Ford Mustang Red 1998 NULL

Environments Non-Relational Data Attribute table Different data may be available about different objects Difficult to search or index CarID Make Model Color Year License Interior 1 HondaAccordNULL2002NULLNULL2MitsubishiNULLGreenNULLNULLNULL3 NULL Escape Black 2003 NULL NULL 4 NULL NULL Blue NULL YC3-XYZ NULL 5 Ford Mustang Red 1998 NULL Black

Distributed DBMS Divide up workload across multiple servers In the interest of time/computation Also for space – don’t want to redundantly store the entire DB Attainable with RDBMS Difficult to program and coordinate Makes a well-implemented distributed RDBMS expensive May have to interrogate multiple server to get an answer to a single query In a Non-Relational DBMS Easy to partition data Unlikely that a query could span >1 slave node Overlap partitions for redundancy Seamless failure recovery

Storage Can tailor data storage to its use May reduce the need to join multiple normalized tables Could make re-assembly/display of database data easierStoring model redundantly Beneficial to have short column names Performance Minimize time spend “plumbing” the data Much lighter overhead on distributed systemsFaster queries (10-20k operations/second) Due to limitations on what queries are allowed If data can be stored to work within these limitations, valid claim

Overview Background Benefits PitfallsAvailable Platforms Summary “The responsibility for ensuring data integrity falls entirely to the application. But application code often carries bugs. Bugs in a properly designed relational database usually don't lead to data integrity issues; bugs in a key/value database, however, quite easily lead to data integrity issues.” Tony Bain ReadWrite Enterprise

Pitfalls – Foreign Keys No integrity checks Relies on the Model View Control (MVC) principle Assumes the integrity will be handled elsewhere

Pitfalls – Proprietary RDMBS rely on SQL as a standard interface No such interface between Non-RDBMS Locked in to a single systemMany of whom have only had these systems deployed for a handful of years

Pitfalls – Proprietary Voldemort String bootstrapUrl = "tcp://localhost:6666"; StoreClientFactory factory = new SocketStoreClientFactory (new ClientConfig (). setBootstrapUrls ( bootstrapUrl));StoreClient client = factory.getStoreClient("my_store_name"); Versioned value = client.get("some_key"); value.setObject("some_value"); client.put("some_key", value);

Pitfalls – Proprietary CouchDB Session s = new Session("localhost",5984); Database db = s.getDatabase (" foodb "); Document doc = db.getDocument ("documentid1234"); doc.put (" foo","bar"); db.saveDocument(doc); Document newdoc = new Document(); newdoc.put("foo","baz"); newdoc.saveDocument(newdoc); ViewResults result = db.getAllDocuments(); for (Document d: result.getResults()) { System.out.println( d.getId ()); Document full = db.getDocument ( d.getId ()); } ViewResults resultAdHoc = db.adhoc ("function (doc) { if (doc.foo=='bar') { return doc; }}");

Pitfalls – Analytics If only able to query a few rows at a time, could be very difficult to extract large chunks of data Difficult to mine or analyze Difficult to export to another systemAmazon’s offering can’t run a query that takes longer than 5 seconds Google’s AppEngine Datastore can’t retrieve more than 1,000 items for a query

Pitfalls - Consistency Exists in distributed systems All data is versioned Each key has an implicit value field called “time”Can be used to detect collisions or out-of-date updates across redundant data Garbage collected automatically (implementation-dependent) Many will “eventually” get to a consistent state

Pitfalls – Column-Based Querying Difficult to index non-primary key columns Any “WHERE” clause on non-primary key columns would have to scan through the entire database Will be very expensive on large/distributed databases Forces one, exclusive view of the data (Could combine DBMSs to get the benefits of both) Can’t guarantee that all rows have any field

CouchDB’s Views Cache results to these very expensive queries Extract only those entries that have the value in question Apply filter as we progress through the database (height>18) Build a temporary table containing the results Will not, necessarily, be the result of a consistent state May be outdated //Get all rows from a particular user id map: function(doc) { if ( doc.user_id ) { emit( doc.user_id, null); }}

CouchDB’s Views SELECT min(field) FROM table; SELECT max(field) FROM table; Min/Max of a Column in SQL

CouchDB’s Views // Map function function(doc) { var risk_exponent = -3.194 + doc.CV_VOLOCC_1 *1.080 + doc.CV_VOLOCC_M *0.627 + doc.CV_VOLOCC_R *0.553 + doc.CORR_VOLOCC_1M *1.439 + doc.CORR_VOLOCC_MR *0.658 + doc.LAG1_OCC_M *0.412 + doc.LAG1_OCC_R *1.424 + doc.MU_VOL_1 *0.038 + doc.MU_VOL_M *0.100 + doc["CORR_OCC_1M X MU_VOL_M"] *-0.168 + doc["CORR_OCC_1M X SD_VOL_R" ] *0.479 + doc["CORR_OCC_1M X LAG1_OCC_R"] *-1.462 ; var risk = Math.exp(risk_exponent); // parse the date and "chunk" it up var pattern = new RegExp ("(.*)-0?(.*)-0?(.*)T0?(.*):0?(.*):0?(.*)(-0800)"); var result = pattern.exec ( doc.EstimateTime ); var day; if(result){ //new Date(year, month, day, hours, minutes, seconds, ms) // force rounding to 5 minutes, 0 seconds, for aggregation of 5 minute chunks var fivemin = 5 * Math.floor (result[5]/5) day = new Date(result[1],result[2]-1,result[3],result[4], fivemin , 0); } var weekdays = [" Sun","Mon","Tue","Wed","Thu","Fri","Sat "]; emit([weekdays[ day.getDay ()], day.toLocaleTimeString ( )],{' risk':risk }); }   // Reduce function function (keys, values, rereduce) {  // algorithm for on-line computation of moments from // // Tony F. Chan, Gene H. Golub, and Randall J. LeVeque: "Updating // Formulae and a Pairwise Algorithm for Computing Sample // Variances." Technical Report STAN-CS-79-773, Department of // Computer Science, Stanford University, November 1979. url: // ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf // so there is some weirdness in that the original was Fortran, index from 1, // and lots of arrays (no lists, no hash tables)   // also consulted http://people.xiph.org/~tterribe/notes/homs.html // and http://www.jstor.org/stable/2683386 // and (ick!) the wikipedia description of Knuth's algorithm // to clarify what was going on with http://www.slamb.org/svn/repos/trunk/projects/common/src/java/org/slamb/common/stats/Sample.java  /* combine the variance esitmates for two partitions, A and B. partitionA and partitionB both should contain { S : the current estimate of the second moment Sum : the sum of observed values M : the number of observations used in the partition to calculate S and Sum }  The output will be an identical object, containing the S, Sum and M for the combination of partitions A and B This routine is derived from original fortran code in Chan et al, (1979)  But it is easily derived by recognizing that all you're doing is multiplying each partition's S and Sum by its respective count M, and then dividing by the new count Ma + Mb. The arrangement of the diff etc is just rearranging terms to make it look nice.  And then summing up the sums, and summing up the counts  */ function combine_S(partitionA,partitionB){ var NewS=partitionA.S; var NewSum=partitionA.Sum; var min = partitionA.min; var max = partitionA.max; var M = partitionB.M; if(!M){M=0;} if(M){ var diff = ((partitionA.M * partitionB.Sum / partitionB.M) - partitionA.Sum ); NewS += partitionB.S + partitionB.M*diff*diff/(partitionA.M * (partitionA.M+partitionB.M) ); NewSum += partitionB.Sum ;  min = Math.min(partitionB.min, min); max = Math.max(partitionB.max, max); } return {'S':NewS,'Sum':NewSum, 'M': partitionA.M+M, 'min':min, 'max':max }; }   /*  This routine is derived from original fortran code in Chan et al, (1979), with the combination step split out above to allow that to be called independently in the rereduce step.  Arguments:   The first argument (values) is an array of objects. The assumption is that the key to the variable of interest is 'risk'. If this is not the case, the seventh argument should be the correct key to use. More complicated data structures are not supported.  The second, third, and fourth arguments are in case this is a running tally. You can pass in exiting values for M (the number of observations already processed), Sum (the running sum of those M observations) and S (the current estimate of variance for those M observations). Totally optional, defaulting to zero.   The fifth parameter is for the running min, and the sixth for the max.  Pass "null" for parameters 2 through 6 if you need to pass a key in the seventh slot.  Some notes on the algorithm. There is a precious bit of trickery with stack pointers, etc that make for a minimal amount of temporary storage. All this was included in the original algorithm. I can't see that it makes much sense to include all that effort given that I've got gobs of RAM and am instead most likely processor bound, but it reminded me of programming in assembly so I kept it in.   If you watch the progress of this algorithm in a debugger or firebug, you'll see that the size of the stack stays pretty small, with the bottom (0) entry staying at zero, then the [1] entry containing a power of two (2,4,8,16, etc), and the [2] entry containing the next power of two down from [1] and so on. As the slots of the stack get filled up, they get cascaded together by the inner loop.  You could skip all that, and just pairwise process repeatedly until the list of intermediate values is empty, but whatever. And there seems to be some super small gain in efficiency in using identical support for two groups being combined, in that you don't have to consider different Ma and Mb in the computation. One less divide I guess)  */ function pairwise_update (values, M, Sum, S, min, max, key){ if(!key){key='risk';} if(!Sum){Sum = 0; S = 0; M=0;} if(!S){Sum = 0; S = 0; M=0;} if(!M){Sum = 0; S = 0; M=0;} if(!min){ min = Infinity; } if(!max){ max = -Infinity; } var T; var stack_ptr=1; var N = values.length; var half = Math.floor(N/2); var NewSum; var NewS ; var SumA=[]; var SA=[]; var Terms=[]; Terms[0]=0; if(N == 1){ Nsum=values[0][key]; Ns=0; }else if(N > 1){ // loop over the data pairwise for(var i = 0; i < half; i++){ // check min max if(values[2*i+1][key] < values[2*i][key] ){ min = Math.min(values[2*i+1][key], min); max = Math.max(values[2*i][key], max); }else{ min = Math.min(values[2*i][key], min); max = Math.max(values[2*i+1][key], max); } SumA[stack_ptr]=values[2*i+1][key] + values[2*i][key]; var diff = values[2*i + 1][key] - values[2*i][key] ; SA[stack_ptr]=( diff * diff ) / 2; Terms[stack_ptr]=2; while( Terms[stack_ptr] == Terms[stack_ptr-1]){ // combine the top two elements in storage, as // they have equal numbers of support terms. this // should happen for powers of two (2, 4, 8, etc). // Everything else gets cleaned up below stack_ptr--; Terms[stack_ptr]*=2; // compare this diff with the below diff. Here // there is no multiplication and division of the // first sum (SumA[stack_ptr]) because it is the // same size as the other. var diff = SumA[stack_ptr] - SumA[stack_ptr+1]; SA[stack_ptr]= SA[stack_ptr] + SA[stack_ptr+1] + (diff * diff)/Terms[stack_ptr]; SumA[stack_ptr] += SumA[stack_ptr+1]; } // repeat as needed stack_ptr++; } stack_ptr--; // check if N is odd if(N % 2 != 0){ // handle that dangling entry stack_ptr++; Terms[stack_ptr]=1; SumA[stack_ptr]=values[N-1][key]; SA[stack_ptr]=0; // the variance of a single observation is zero! min = Math.min(values[N-1][key], min); max = Math.max(values[N-1][key], max); } T=Terms[stack_ptr]; NewSum=SumA[stack_ptr]; NewS= SA[stack_ptr]; if(stack_ptr > 1){ // values.length is not power of two, so not // everything has been scooped up in the inner loop // above. Here handle the remainders for(var i = stack_ptr-1; i>=1 ; i--){ // compare this diff with the above diff---one // more multiply and divide on the current sum, // because the size of the sets (SumA[i] and NewSum) // are different. var diff = Terms[i]*NewSum/T-SumA[i]; NewS = NewS + SA[i] + ( T * diff * diff )/ (Terms[i] * (Terms[i] + T)); NewSum += SumA[i]; T += Terms[i]; } } } // finally, combine NewS and NewSum with S and Sum return combine_S( {'S':NewS,'Sum':NewSum, 'M': T , 'min':min, 'max':max}, {'S':S,'Sum':Sum, 'M': M , 'min':min, 'max':max}); }   /*  This function is attributed to Knuth, the Art of Computer Programming. Donald Knuth is a math god, so I am sure that it is numerically stable, but I haven't read the source so who knows.  The first parameter is again values, a list of objects with the expectation that the variable of interest is contained under the key 'risk'. If this is not the case, pass the correct variable in the 7th field. Parameters 2 through 6 are all optional. Pass nulls if you need to pass a key in slot 7.  In order they are   mean: the current mean value estimate M2: the current estimate of the second moment (variance) n: the count of observations used in the current estimate min: the current min value observed max: the current max value observed  */ function KnuthianOnLineVariance(values, M2, n, mean, min, max, key){ if(!M2){ M2 = 0; } if(!n){ n = 0; } if(!mean){ mean = 0; } if(!min){ min = Infinity; } if(!max){ max = -Infinity; } if(!key){ key = 'risk'; }  // this algorithm is apparently a special case of the above // pairwise algorithm, in which you just apply one more value // to the running total. I don't know why bun Chan et al // (1979) and again in their later paper claim that using M // greater than 1 is always better than not.  // but this code is certainly cleaner! code based on Scott // Lamb's Java found at // http://www.slamb.org/svn/repos/trunk/projects/common/src/java/org/slamb/common/stats/Sample.java // but modified a bit  for(var i=0; i<values.length; i++ ){ var diff = (values[i][key] - mean); var newmean = mean + diff / (n+i+1); M2 += diff * (values[i][key] - newmean); mean = newmean; min = Math.min(values[i][key], min); max = Math.max(values[i][key], max); } return {'M2': M2, 'n': n + values.length, 'mean': mean, 'min':min, 'max':max }; }  function KnuthCombine(partitionA,partitionB){ if(partitionB.n){ var newn = partitionA.n + partitionB.n; var diff = partitionB.mean - partitionA.mean; var newmean = partitionA.mean + diff*(partitionB.n/newn) var M2 = partitionA.M2 + partitionB.M2 + (diff * diff * partitionA.n * partitionB.n / newn ); min = Math.min(partitionB.min, partitionA.min); max = Math.max(partitionB.max, partitionA.max); return {'M2': M2, 'n': newn, 'mean': newmean, 'min':min, 'max':max }; } else { return partitionA; } }  var output={}; var knuthOutput={};  // two cases in the application of reduce. In the first reduce // case the rereduce flag is false, and we have raw values. We // also have keys, but that isn't applicable here. // // In the rereduce case, rereduce is true, and we are being passed // output for identical keys that needs to be combined further.  if(!rereduce) { output = pairwise_update(values); output.variance_n=output.S/output.M; output.mean = output.Sum/output.M; knuthOutput = KnuthianOnLineVariance(values); knuthOutput.variance_n=knuthOutput.M2/knuthOutput.n; output.knuthOutput=knuthOutput;  } else { /* we have an existing pass, so should have multiple outputs to combine */ for(var v in values){ output = combine_S(values[v],output); knuthOutput = KnuthCombine(values[v].knuthOutput, knuthOutput); } output.variance_n=output.S/output.M; output.mean = output.Sum/output.M; knuthOutput.variance_n=knuthOutput.M2/knuthOutput.n; output.knuthOutput=knuthOutput; } // and done return output;}  Min/Max of a Column – 334 lines

Overview Background Benefits PitfallsAvailable PlatformsSummary

Applications Very popular in the Web 2.0 community Appeal to web startups concerned with enormous scalability issues (Traffic triples overnight)Need dynamic and easy scalability Not concerned with relationships between data Maybe no budget for Oracle or other systems

Server Solutions Apache’s CouchDB Simple API, ViewsProject Voldemort - http://project-voldemort.com/ Automatic replication and scaling across multiple servers Mongo - http://www.mongodb.org/ Supports indexing other columns Drizzle - https://launchpad.net/drizzle Hybrid Non-relational/relational Based on a MySQL core

“Cloud” Solutions Amazon’s SimpleDB http://aws.amazon.com/simpledb/Google’s AppEngine Datastore http://code.google.com/appengine/docs/python/datastore/

Overview Background Benefits PitfallsAvailable PlatformsSummary

Summary “Non-Relational Databases” Also called “Document-Oriented” or “Key-Value Databases” Useful when data is almost exclusively retrieved by its unique IDQueries could be expressed without needing complex joins Non-trivial amount of data and don’t have the capability to manage a distributed RDBMS

Bibliography Amazon. Amazon SimpleDB . Access October 15, 2009. http://aws.amazon.com/simpledb/ Apache. CouchDB : Technical Overview. Accessed October 15, 2009. http://couchdb.apache.org/docs/overview.html Bain, Tony. Is the Relational Database Doomed? ReadWrite Enterprise. February 12, 2009. http://www.readwriteweb.com/enterprise/2009/02/is-the-relational-database-doomed.php?p=3 CouchDB. View_Snippets. September 20, 2009. http://wiki.apache.org/couchdb/View_SnippetsCheng et. al, Bigtable: A Distributed Storage System for Structured Data, Google Inc., 2007DeCandia et. al., Dynamo: Amazon's Highly Available Key-value Store, Amazon.com, 2007Faler, Wille. Don't store non-relational data in a relational database. May 20, 2009. http://faler.wordpress.com/2009/05/20/dont-store-non-relational-data-in-a-relational-database/Jones, Richard. Andti-RDBMS: A list of distributed key-value stores. January 19, 2009. http://www.metabrew.com/article/anti-rdbms-a-list-of-distributed-key-value-stores/Kleppmann, Martin. Should you go Beyond Relational Databases? 24 June, 2009. http://carsonified.com/blog/dev/should-you-go-beyond-relational-databases/Project Voldemort. Accessed October 15, 2009. http://project-voldemort.com/quickstart.phpTaylor, Bret. How FriendFeed uses MySQL to store schema-less data. February 27, 2009. http://bret.appspot.com/entry/how-friendfeed-uses-mysql

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