Intermittent performance issues with Apache ignite 2.7.5 - ignite
We are facing intermittent performance issues with ignite where response times become very high and we see below error in our logs. We have 10 indexed columns and I don't see any issues with indexes as all the columns in the "where" clause are indexed. Joins are happening on the fields with affinity colocation which means that joins are happening only on the data in a particular node and not across nodes.
[21:48:30,765][WARNING][jvm-pause-detector-worker][IgniteKernal%PincodeGrid] Possible too long JVM pause: 4939 milliseconds.
[21:48:30,783][WARNING][query-#120%PincodeGrid%][IgniteH2Indexing] Query execution is too long [time=5052 ms, sql='SELECT
Please let me know if you can provide any help on this.
Apache Ignite version : 2.7.5
Ignite persistence is enabled (true)
2 node cluster in partitioned mode
RAM - 150 GB per node
JVM xms and xmx 20G
Number of records - 160 million
JVM options -
/usr/java/jdk1.8.0_144/bin/java -XX:+AggressiveOpts -server -Xms20g -Xmx20g -XX:+AlwaysPreTouch -XX:+UseG1GC -XX:+ScavengeBeforeFullGC -XX:+DisableExplicitGC -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/etappdata/ignite/logs/PROD/etail-prod-ignite76-163/logs -XX:+ExitOnOutOfMemoryError -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:+PrintGCDateStamps -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=10 -XX:GCLogFileSize=100M -Xloggc:/etappdata/ignite/logs/PROD/etail-prod-ignite76-163/gc.log -XX:+PrintAdaptiveSizePolicy -XX:+UseTLAB -verbose:gc -XX:+ParallelRefProcEnabled -XX:+UseLargePages -XX:+AggressiveOpts -Djava.net.preferIPv4Stack=true -Djava.net.preferIPv4Addresses=true -Djava.net.preferIPv6Stack=false -Djava.net.preferIPv6Addresses=false -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.port=8996 -Dcom.sun.management.jmxremote.rmi.port=8996 -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.local.only=false -Djava.rmi.server.hostname=etail-prod-ignite76-163 -XX:MaxDirectMemorySize=4g -javaagent:/tmp/apminsight-javaagent-prod/apminsight-javaagent.jar -Dfile.encoding=UTF-8 -XX:+UseG1GC -DIGNITE_QUIET=false -DIGNITE_SUCCESS_FILE=/ignite/apache-ignite-2.7.5-bin/work/ignite_success_7d9ec20d-9728-475a-aa80-4355eb8eaf02 -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.port=49112 -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.ssl=false -DIGNITE_HOME=/ignite/apache-ignite-2.7.5-bin -DIGNITE_PROG_NAME=./bin/ignite.sh -cp /ignite/apache-ignite-2.7.5-bin/libs/:/ignite/apache-ignite-2.7.5-bin/libs/ignite-indexing/:/ignite/apache-ignite-2.7.5-bin/libs/ignite-spring/:/ignite/apache-ignite-2.7.5-bin/libs/licenses/ org.apache.ignite.startup.cmdline.CommandLineStartup config/config-cache.xml
Added additional details
[04:03:48,251][INFO][main][IgniteKernal%PincodeGrid] IgniteConfiguration [igniteInstanceName=PincodeGrid, pubPoolSize=30, svcPoolSize=30, callbackPoolSize=30, stripedPoolSize=30, sysPoolSize=30, mgmtPoolSize=4, igfsPoolSize=30, dataStreamerPoolSize=30, utilityCachePoolSize=30, utilityCacheKeepAliveTime=60000, p2pPoolSize=2, qryPoolSize=30, igniteHome=/ignite/apache-ignite-2.7.5-bin, igniteWorkDir=/ignite/apache-ignite-2.7.5-bin/work, mbeanSrv=com.sun.jmx.mbeanserver.JmxMBeanServer#13221655, nodeId=6aee7bb4-2804-4396-a9ec-65abdc9483e3, marsh=BinaryMarshaller [], marshLocJobs=false, daemon=false, p2pEnabled=true, netTimeout=5000, sndRetryDelay=1000, sndRetryCnt=3, metricsHistSize=10000, metricsUpdateFreq=2000, metricsExpTime=9223372036854775807, discoSpi=TcpDiscoverySpi [addrRslvr=null, sockTimeout=0, ackTimeout=0, marsh=null, reconCnt=10, reconDelay=2000, maxAckTimeout=600000, forceSrvMode=false, clientReconnectDisabled=false, internalLsnr=null], segPlc=STOP, segResolveAttempts=2, waitForSegOnStart=true, allResolversPassReq=true, segChkFreq=10000, commSpi=TcpCommunicationSpi [connectGate=null, connPlc=org.apache.ignite.spi.communication.tcp.TcpCommunicationSpi$FirstConnectionPolicy#34123d65, enableForcibleNodeKill=false, enableTroubleshootingLog=false, locAddr=null, locHost=null, locPort=47100, locPortRange=100, shmemPort=-1, directBuf=true, directSndBuf=false, idleConnTimeout=600000, connTimeout=5000, maxConnTimeout=600000, reconCnt=10, sockSndBuf=32768, sockRcvBuf=65536, msgQueueLimit=2048, slowClientQueueLimit=0, nioSrvr=null, shmemSrv=null, usePairedConnections=true, connectionsPerNode=10, tcpNoDelay=true, filterReachableAddresses=false, ackSndThreshold=32, unackedMsgsBufSize=0, sockWriteTimeout=10000, boundTcpPort=-1, boundTcpShmemPort=-1, selectorsCnt=15, selectorSpins=0, addrRslvr=null, ctxInitLatch=java.util.concurrent.CountDownLatch#59474f18[Count = 1], stopping=false], evtSpi=org.apache.ignite.spi.eventstorage.NoopEventStorageSpi#65fb9ffc, colSpi=NoopCollisionSpi [], deploySpi=LocalDeploymentSpi [], indexingSpi=org.apache.ignite.spi.indexing.noop.NoopIndexingSpi#3590fc5b, addrRslvr=null, encryptionSpi=org.apache.ignite.spi.encryption.noop.NoopEncryptionSpi#397fbdb, clientMode=false, rebalanceThreadPoolSize=16, txCfg=TransactionConfiguration [txSerEnabled=false, dfltIsolation=REPEATABLE_READ, dfltConcurrency=PESSIMISTIC, dfltTxTimeout=0, txTimeoutOnPartitionMapExchange=0, pessimisticTxLogSize=0, pessimisticTxLogLinger=10000, tmLookupClsName=null, txManagerFactory=null, useJtaSync=false], cacheSanityCheckEnabled=true, discoStartupDelay=60000, deployMode=SHARED, p2pMissedCacheSize=100, locHost=null, timeSrvPortBase=31100, timeSrvPortRange=100, failureDetectionTimeout=80000, sysWorkerBlockedTimeout=30000, clientFailureDetectionTimeout=120000, metricsLogFreq=6000000, hadoopCfg=null, connectorCfg=ConnectorConfiguration [jettyPath=null, host=null, port=11211, noDelay=true, directBuf=false, sndBufSize=32768, rcvBufSize=32768, idleQryCurTimeout=600000, idleQryCurCheckFreq=60000, sndQueueLimit=0, selectorCnt=4, idleTimeout=7000, sslEnabled=false, sslClientAuth=false, sslCtxFactory=null, sslFactory=null, portRange=100, threadPoolSize=30, msgInterceptor=null], odbcCfg=null, warmupClos=null, atomicCfg=AtomicConfiguration [seqReserveSize=1000, cacheMode=PARTITIONED, backups=1, aff=null, grpName=null], classLdr=null, sslCtxFactory=null, platformCfg=null, binaryCfg=null, memCfg=null, pstCfg=null, dsCfg=DataStorageConfiguration [sysRegionInitSize=41943040, sysRegionMaxSize=104857600, pageSize=4096, concLvl=0, dfltDataRegConf=DataRegionConfiguration [name=Default_Region, maxSize=128849018880, initSize=112742891520, swapPath=null, pageEvictionMode=DISABLED, evictionThreshold=0.9, emptyPagesPoolSize=100, metricsEnabled=false, metricsSubIntervalCount=5, metricsRateTimeInterval=60000, persistenceEnabled=true, checkpointPageBufSize=4294967296], dataRegions=null, storagePath=/ignite/persistence, checkpointFreq=180000, lockWaitTime=10000, checkpointThreads=8, checkpointWriteOrder=SEQUENTIAL, walHistSize=20, maxWalArchiveSize=1073741824, walSegments=10, walSegmentSize=1073741824, walPath=/wal/pincode, walArchivePath=/wal/pincode/archive, metricsEnabled=false, walMode=BACKGROUND, walTlbSize=131072, walBuffSize=0, walFlushFreq=2000, walFsyncDelay=1000,
walRecordIterBuffSize=67108864, alwaysWriteFullPages=false, fileIOFactory=org.apache.ignite.internal.processors.cache.persistence.file.AsyncFileIOFactory#2d3379b4, metricsSubIntervalCnt=5, metricsRateTimeInterval=60000, walAutoArchiveAfterInactivity=-1, writeThrottlingEnabled=false, walCompactionEnabled=false, walCompactionLevel=1, checkpointReadLockTimeout=null], activeOnStart=true, autoActivation=true, longQryWarnTimeout=3000, sqlConnCfg=null, cliConnCfg=ClientConnectorConfiguration [host=null, port=10800, portRange=100, sockSndBufSize=0, sockRcvBufSize=0, tcpNoDelay=true, maxOpenCursorsPerConn=128, threadPoolSize=30, idleTimeout=0, jdbcEnabled=true, odbcEnabled=true, thinCliEnabled=true, sslEnabled=false, useIgniteSslCtxFactory=true, sslClientAuth=false, sslCtxFactory=null], mvccVacuumThreadCnt=2, mvccVacuumFreq=5000, authEnabled=false, failureHnd=NoOpFailureHandler [super=AbstractFailureHandler [ignoredFailureTypes=[SYSTEM_WORKER_BLOCKED, SYSTEM_CRITICAL_OPERATION_TIMEOUT]]], commFailureRslvr=null]
Edit -2 - GC logs
2020-12-01T22:49:31.729+0530: 15.630: [GC pause (Metadata GC Threshold) (young) (initial-mark) 15.630: [G1Ergonomics (CSet Construction) start choosing CSet, _pending_cards: 0, predicted base time: 38.43 ms, remaining time: 161.57 ms, target pause time: 200.00 ms]
15.630: [G1Ergonomics (CSet Construction) add young regions to CSet, eden: 24 regions, survivors: 2 regions, predicted young region time: 356.58 ms]
15.630: [G1Ergonomics (CSet Construction) finish choosing CSet, eden: 24 regions, survivors: 2 regions, old: 0 regions, predicted pause time: 395.01 ms, target pause time: 200.00 ms]
15.657: [G1Ergonomics (Mixed GCs) do not start mixed GCs, reason: concurrent cycle is about to start], 0.0274990 secs]
[Parallel Time: 15.8 ms, GC Workers: 21]
[GC Worker Start (ms): Min: 15630.2, Avg: 15630.5, Max: 15630.8, Diff: 0.7]
[Ext Root Scanning (ms): Min: 1.6, Avg: 3.4, Max: 11.4, Diff: 9.8, Sum: 71.8]
[Update RS (ms): Min: 0.0, Avg: 0.0, Max: 0.0, Diff: 0.0, Sum: 0.0]
[Processed Buffers: Min: 0, Avg: 0.0, Max: 0, Diff: 0, Sum: 0]
[Scan RS (ms): Min: 0.0, Avg: 0.0, Max: 0.0, Diff: 0.0, Sum: 0.1]
[Code Root Scanning (ms): Min: 0.0, Avg: 1.2, Max: 12.6, Diff: 12.6, Sum: 24.2]
[Object Copy (ms): Min: 0.0, Avg: 9.5, Max: 12.0, Diff: 11.9, Sum: 199.9]
[Termination (ms): Min: 0.0, Avg: 0.7, Max: 0.8, Diff: 0.7, Sum: 14.8]
[Termination Attempts: Min: 1, Avg: 2.2, Max: 4, Diff: 3, Sum: 47]
[GC Worker Other (ms): Min: 0.0, Avg: 0.0, Max: 0.2, Diff: 0.1, Sum: 1.0]
[GC Worker Total (ms): Min: 14.5, Avg: 14.8, Max: 15.2, Diff: 0.7, Sum: 311.8]
[GC Worker End (ms): Min: 15645.3, Avg: 15645.3, Max: 15645.4, Diff: 0.1]
[Code Root Fixup: 0.6 ms]
[Code Root Purge: 0.0 ms]
[Clear CT: 0.5 ms]
[Other: 10.5 ms]
[Choose CSet: 0.0 ms]
[Ref Proc: 8.3 ms]
[Ref Enq: 0.5 ms]
[Redirty Cards: 0.5 ms]
[Humongous Register: 0.0 ms]
[Humongous Reclaim: 0.0 ms]
[Free CSet: 0.2 ms]
[Eden: 192.0M(1008.0M)->0.0B(984.0M) Survivors: 16.0M->40.0M Heap: 198.0M(20.0G)->33.7M(20.0G)]
[Times: user=0.31 sys=0.00, real=0.03 secs]
2020-12-01T22:49:31.757+0530: 15.657: [GC concurrent-root-region-scan-start]
2020-12-01T22:49:31.764+0530: 15.664: [GC concurrent-root-region-scan-end, 0.0067826 secs]
2020-12-01T22:49:31.764+0530: 15.664: [GC concurrent-mark-start]
2020-12-01T22:49:31.765+0530: 15.666: [GC concurrent-mark-end, 0.0015043 secs]
2020-12-01T22:49:31.766+0530: 15.666: [GC remark 2020-12-01T22:49:31.766+0530: 15.666: [Finalize Marking, 0.0010641 secs] 2020-12-01T22:49:31.767+0530: 15.667: [GC ref-proc, 0.0100232 secs] 2020-12-01T22:49:31.777+0530: 15.677: [Unloading, 0.0072592 secs], 0.0191010 secs]
[Times: user=0.20 sys=0.00, real=0.02 secs]
2020-12-01T22:49:31.785+0530: 15.685: [GC cleanup 37M->37M(20G), 0.0085803 secs]
[Times: user=0.04 sys=0.00, real=0.01 secs]
2020-12-01T22:53:45.090+0530: 268.990: [GC pause (G1 Evacuation Pause) (young) 268.990: [G1Ergonomics (CSet Construction) start choosing CSet, _pending_cards: 0, predicted base time: 30.72 ms, remaining time: 169.28 ms, target pause time: 200.00 ms]
268.990: [G1Ergonomics (CSet Construction) add young regions to CSet, eden: 123 regions, survivors: 5 regions, predicted young region time: 1342.47 ms]
268.990: [G1Ergonomics (CSet Construction) finish choosing CSet, eden: 123 regions, survivors: 5 regions, old: 0 regions, predicted pause time: 1373.18 ms, target pause time: 200.00 ms]
269.040: [G1Ergonomics (Mixed GCs) do not start mixed GCs, reason: candidate old regions not available]
, 0.0494933 secs]
[Parallel Time: 31.8 ms, GC Workers: 21]
[GC Worker Start (ms): Min: 268991.8, Avg: 268992.1, Max: 268992.5, Diff: 0.7]
[Ext Root Scanning (ms): Min: 0.9, Avg: 1.9, Max: 5.7, Diff: 4.7, Sum: 39.6]
[Update RS (ms): Min: 0.0, Avg: 0.0, Max: 0.0, Diff: 0.0, Sum: 0.0]
[Processed Buffers: Min: 0, Avg: 0.0, Max: 0, Diff: 0, Sum: 0]
[Scan RS (ms): Min: 0.0, Avg: 0.0, Max: 0.0, Diff: 0.0, Sum: 0.5]
[Code Root Scanning (ms): Min: 0.0, Avg: 1.0, Max: 7.0, Diff: 7.0, Sum: 20.2]
[Object Copy (ms): Min: 21.7, Avg: 28.1, Max: 29.1, Diff: 7.4, Sum: 591.0]
[Termination (ms): Min: 0.0, Avg: 0.0, Max: 0.0, Diff: 0.0, Sum: 0.1]
[Termination Attempts: Min: 1, Avg: 1.0, Max: 1, Diff: 0, Sum: 21]
[GC Worker Other (ms): Min: 0.0, Avg: 0.1, Max: 0.2, Diff: 0.2, Sum: 1.8]
If you have 20G heap you may expect to have an eventual full GC taking 4 seconds. I can believe that. Why do you need so much heap with Apache Ignite? How much heap is used during normal course of operations? You may also take a heap dump and search for a memory leak.
Node that Apache Ignite does not store the data on heap by default so it can't be explained by amount of data alone.
I ran your GC log through gceasy.io and it have found several GCs spanning around 2 seconds. I'm not sure it explains your observed 4s pause but you may expect 2s pauses obviously, from GC, which is in the same ballpark.
So you need to figure out why your JVM becomes slow sometimes. Maybe it's IO, virtualization pauses, etc, etc. Also, if your heap is never larger than 2G, maybe you should run with something like -Xmx4G?
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How to create a pandas dataframe from csv where one column contains nested dictionary?
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Split column values to several in pandas dataframe
I am trying to do sentiment analysis on tweets using sentimentIntensityAnalyzer() from nltk.sentiment.vader sid = SentimentIntensityAnalyzer() listy = [] for index, row in data.iterrows(): ss = sid.polarity_scores(row["Tweets"]) listy.append(ss) se = pd.Series(listy) data['polarity'] = se.values display(data.head(100)) This is the resulting dataFramee : Tweets polarity 0 RT #spectatorindex: Facebook controls:\n\n- Wh... {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound... 1 RT #YAATeamWest: Today we're at #BradfordUniSU... {'neg': 0.0, 'neu': 0.902, 'pos': 0.098, 'comp... 2 #SachinTendulkar launches India’s first Multip... {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound... 3 How To Create a 360 Render (And How to Improv... {'neg': 0.0, 'neu': 0.722, 'pos': 0.278, 'comp... 4 The Most Disturbing Virtual Reality You Will E... {'neg': 0.174, 'neu': 0.826, 'pos': 0.0, 'comp... 5 VR Training for Troops 🎮\n\n... {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound... 6 RT #DefenceHQ: The #BritishArmy has awarded a ... {'neg': 0.0, 'neu': 0.847, 'pos': 0.153, 'comp... 7 RT #UofGHumanities: #UofGCSPE Humanities Lectu... {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound... 8 RT #OyezServices: Ever wanted a tour of Machu ... {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound... 9 RT #ProjectDastaan: We are an Oxford Universit... {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound... 10 RT #Paula_Piccard: Virtual reality will change... {'neg': 0.0, 'neu': 0.878, 'pos': 0.122, 'comp... In order to do statistical analysis on the 'neg','pos','neu' and 'compound' entities in the polarity column I wanted to split the data into four different columns. To achieve this I used : list_pos= [] list_neg = [] list_comp = [] list_neu = [] for index, row in data.iterrows(): list_pos.append(row['polarity']['pos']) list_neg.append(row['polarity']['neg']) list_comp.append(row['polarity']['compound']) list_neu.append(row['polarity']['neu']) se_pos = pd.Series(list_pos) se_neg = pd.Series(list_neg) se_comp = pd.Series(list_comp) se_neu = pd.Series(list_neu) data['positive'] = se_pos.values data['negative'] = se_neg.values data['compound'] = se_comp.values data['neutral'] = se_neu.values The resulting dataFrame: Tweets polarity positive negative compound neutral 0 RT #spectatorindex: Facebook controls:\n\n- Wh... {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound... 0.000 0.000 0.0000 1.000 1 RT #YAATeamWest: Today we're at #BradfordUniSU... {'neg': 0.0, 'neu': 0.902, 'pos': 0.098, 'comp... 0.098 0.000 0.3612 0.902 2 #SachinTendulkar launches India’s first Multip... {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound... 0.000 0.000 0.0000 1.000 Is there a more concise way of achieving a similar dataFrame? Using the lambda function perhaps? Thanks for the help!
Drop in Fps while Reading From Camera
I have Two cameras, one is microsoft and another one is logitech. For both cameras i have used the below pipeline. gst-launch-1.0 -v v4l2src device=/dev/video1 ! videoconvert ! video/x-raw,format=I420,width=640,height=480 ! fpsdisplaysink For Microsoft : /GstPipeline:pipeline0/GstFPSDisplaySink:fpsdisplaysink0: last-message = rendered: 678, dropped: 10, current: 30.10, average: 29.71 /GstPipeline:pipeline0/GstFPSDisplaySink:fpsdisplaysink0: last-message = rendered: 678, dropped: 10, current: 30.10, average: 29.71 /GstPipeline:pipeline0/GstFPSDisplaySink:fpsdisplaysink0: last-message = rendered: 678, dropped: 10, current: 30.10, average: 29.71 But, when i moved my hand very close to the camera, or i closed the camera with my hand then the results are, /GstPipeline:pipeline0/GstFPSDisplaySink:fpsdisplaysink0: last-message = rendered: 2554, dropped: 44, current: 7.52, average: 28.93 /GstPipeline:pipeline0/GstFPSDisplaySink:fpsdisplaysink0/GstTextOverlay:fps-display-text-overlay: text = rendered: 2558, dropped: 44, current: 7.51, average: 28.81 /GstPipeline:pipeline0/GstFPSDisplaySink:fpsdisplaysink0: last-message = rendered: 2558, dropped: 44, current: 7.51, average: 28.81 There is a Huge Drop in Frame Rate. What is the problem in this scenario and how to resolve it?? For Logitech: Same pipeline i had used, but the results are as follows, /GstPipeline:pipeline0/GstFPSDisplaySink:fpsdisplaysink0: last-message = rendered: 0, dropped: 79, fps: 0.00, drop rate: 24.07 /GstPipeline:pipeline0/GstFPSDisplaySink:fpsdisplaysink0: last-message = rendered: 0, dropped: 79, fps: 0.00, drop rate: 24.07 /GstPipeline:pipeline0/GstFPSDisplaySink:fpsdisplaysink0: last-message = rendered: 0, dropped: 79, fps: 0.00, drop rate: 24.07 I am totally confused, What is the problem with these two scenario's??
How to round times in Xcode
I am struggling for days trying to solve this puzzle. I have this code that calculates time IN & OUT as decimal hours: (6 min = 0.1 hr)~(60 min = 1.0 hr) NSUInteger unitFlag = NSCalendarUnitHour | NSCalendarUnitMinute; NSDateComponents *components = [calendar components:unitFlag fromDate:self.outT toDate:self.inT options:0]; NSInteger hours = [components hour]; NSInteger minutes = [components minute]; if (minutes <0) (minutes -= 60*-1) && (hours -=1); if (hours<0 && minutes<0)(hours +=24)&& (minutes -=60*-1); if(hours<0 && minutes>0)(hours +=24)&& (minutes = minutes); if(hours <0 && minutes == 00)(hours +=24)&&(minutes = minutes); if(minutes >0)(minutes = (minutes/6)); self.blockDecimalLabel.text = [NSString stringWithFormat:#"%d.%d", (int)hours, (int)minutes]; The green lines show what the code does, what I am looking for is to round the minutes like the blue lines, 1,2 minutes round down to the next decimal hr, 3,4,5 minutes round up to the next decimal hr What I am try to achieve is: If the result is 11 minutes the code return 0.1 then only after 12 minutes it will return 0.2. What I am trying to do is if the result is 8 the code returns 01, but if it is 9 will round to the next decimal that is 0.2 and so on.The objective is do not loose maximum of 5 minutes in each multiple of 6 in worst cases. Doing this the maximum lost will be 3 minutes in average Any input is more than welcome :) Cheers
Your goals seem incoherent to me. However, I tried this: let beh = NSDecimalNumberHandler( roundingMode: .RoundPlain, scale: 1, raiseOnExactness: false, raiseOnOverflow: false, raiseOnUnderflow: false, raiseOnDivideByZero: false ) for t in 0...60 { let div = Double(t)/60.0 let deci = NSDecimalNumber(double: div) let deci2 = deci.decimalNumberByRoundingAccordingToBehavior(beh) let result = deci2.doubleValue println("min: \(t) deci: \(result)") } The output seems pretty much what you are asking for: min: 0 deci: 0.0 min: 1 deci: 0.0 min: 2 deci: 0.0 min: 3 deci: 0.1 min: 4 deci: 0.1 min: 5 deci: 0.1 min: 6 deci: 0.1 min: 7 deci: 0.1 min: 8 deci: 0.1 min: 9 deci: 0.2 min: 10 deci: 0.2 min: 11 deci: 0.2 min: 12 deci: 0.2 min: 13 deci: 0.2 min: 14 deci: 0.2 min: 15 deci: 0.3 min: 16 deci: 0.3 min: 17 deci: 0.3 min: 18 deci: 0.3 min: 19 deci: 0.3 min: 20 deci: 0.3 min: 21 deci: 0.4 min: 22 deci: 0.4 min: 23 deci: 0.4 min: 24 deci: 0.4 min: 25 deci: 0.4 min: 26 deci: 0.4 min: 27 deci: 0.5 min: 28 deci: 0.5 min: 29 deci: 0.5 min: 30 deci: 0.5 min: 31 deci: 0.5 min: 32 deci: 0.5 min: 33 deci: 0.6 min: 34 deci: 0.6 min: 35 deci: 0.6 min: 36 deci: 0.6 min: 37 deci: 0.6 min: 38 deci: 0.6 min: 39 deci: 0.7 min: 40 deci: 0.7 min: 41 deci: 0.7 min: 42 deci: 0.7 min: 43 deci: 0.7 min: 44 deci: 0.7 min: 45 deci: 0.8 min: 46 deci: 0.8 min: 47 deci: 0.8 min: 48 deci: 0.8 min: 49 deci: 0.8 min: 50 deci: 0.8 min: 51 deci: 0.9 min: 52 deci: 0.9 min: 53 deci: 0.9 min: 54 deci: 0.9 min: 55 deci: 0.9 min: 56 deci: 0.9 min: 57 deci: 1.0 min: 58 deci: 1.0 min: 59 deci: 1.0 min: 60 deci: 1.0