session identification from web server log using java program - java

I have this kind of web log as a text file from that i want to find session from log by considering session time as 30 minutes.
1 in24.inetnebr.com 01/08/1995:00:00:01 GET /shuttle/missions/sts-68/news/sts-68-mcc-05.txt 200 1839
2 in24.inetnebr.com 01/08/1995:00:00:34 GET /shuttle/missions/sts-68/news/sts-68-mcc-06.txt 200 2303
3 in24.inetnebr.com 01/08/1995:00:05:01 GET /shuttle/missions/sts-68/news/sts-68-mcc-05.txt 200 1839
4 in24.inetnebr.com 01/08/1995:00:30:00 GET /shuttle/missions/sts-68/news/sts-68-mcc-05.txt 200 1839
5 in24.inetnebr.com 01/08/1995:00:30:55 GET /shuttle/missions/sts-68/news/sts-68-mcc-05.txt 200 1839
6 in24.inetnebr.com 01/08/1995:00:35:00 GET /shuttle/missions/sts-68/news/sts-68-mcc-05.txt 200 1839
7 slppp6.intermind.net 01/08/1995:00:00:10 GET
/history/skylab/skylab.html 200 1687
8 slppp6.intermind.net 01/08/1995:00:00:32 GET /history/skylab/skylab-1.html 200 1659
9 slppp6.intermind.net 01/08/1995:00:30:00 GET /history/skylab/skylab.html 200 1687
10 slppp6.intermind.net 01/08/1995:00:35:10 GET
/history/skylab/skylab.html 200 1687
11 133.43.96.45 01/08/1995:00:00:16 GET /shuttle/missions/sts-69/mission-sts-69.html 200 10566
12 133.43.96.45 01/08/1995:00:00:55 GET /shuttle/missions/sts-69/mission-sts-69.html 200 10566
13 133.43.96.45 01/08/1995:00:30:16 GET /shuttle/missions/sts-69/mission-sts-69.html 200 10566
14 133.43.96.45 01/08/1995:00:55:16 GET /shuttle/missions/sts-69/mission-sts-69.html 200 10566
Help me out to develop java program for finding session and what data structure i have to use.
package com.pradip.test;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileWriter;
import java.io.IOException;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Scanner;
public class SessionIdentification {
public static void main(String[] args) throws IOException, ParseException {
Scanner s=new Scanner(new File("E:\\me_3rd\\uniqueurl.txt"));
BufferedWriter writer=new BufferedWriter(new FileWriter(new File("E:\\me_3rd\\sessions.txt")));
SimpleDateFormat format=new SimpleDateFormat("dd/mm/yyyy:HH:mm:ss");
Date d1;
Date d2;
while (s.hasNextLine()) {
String line=s.nextLine();
Scanner s1=new Scanner(new File("E:\\me_3rd\\ordertoIP.txt"));
while(s1.hasNextLine()){
Scanner s2=new Scanner(new File("E:\\me_3rd\\ordertoIP.txt"));
String line1=s1.nextLine();
String sline1[]=line1.split("");
long id=Long.parseLong(sline1[0]);
String timestamp=sline1[2];
if(line.equals(sline1[1])){
while (s2.hasNextLine()) {
String line2=s2.nextLine();
String sline2[]=line2.split(" ");
long id1=Long.parseLong(sline2[0]);
String timestamp1=sline2[2];
d1=format.parse(timestamp);
d2=format.parse(timestamp1);
long diff=d2.getTime()-d1.getTime();
long diffSeconds = diff / 1000 % 60;
long diffMinutes = diff / (60 * 1000) % 60;
long diffHours = diff / (60 * 60 * 1000) % 24;
long diffDays = diff / (24 * 60 * 60 * 1000);
long seconds=(diffDays*24*60*60)+(diffHours*60*60)+(diffMinutes*60)+diffSeconds;
if(seconds<=1800){
continue ;
}
else{
String sessionboundry=id+" "+id1; //id1-1
break;
}
}
}
}
}
}
}
This is I was try but i dont know what to try next


I believe you are developing in JSF or JSP. So, You can retrieve session id from the FacesContext. you just need to append this sessionId in your log, than you can filter the result. As you required
FacesContext fCtx = FacesContext.getCurrentInstance();
HttpSession session = (HttpSession) fCtx.getExternalContext().getSession(false);
String sessionId = session.getId();
As you are interested in reading and unique logs only under 30 min session. you can read file line by line and populate your list of logs according.
A rough sketch
Format your log file properly. web / Timestamp / method / state / action / status / session id etc.
Read log file line by line in String.
Split and save logs into ArrayList if match criteria (under 30 min).

Related

How to use spring #Scheduled with cron for execute every X weeks? [duplicate]

I need to create Job that will :
starts one 12/20/2012
endDate = 12/31/2017
will occur every 2 weeks on Sunday and monday
fires at 5 pm.
is this cron expression valid?
Date start = 12/20/2012;
Date endDate = 12/31/2017;
SimpleTrigger trigger = newTrigger()
.withIdentity("trigger3", "group1")
.startAt(startDate)
.withSchedule(cronSchedule("* * 17 0 0/2 *,SUN,MON").build())
.endAt(endDate)
.build;
Please advise.

I suggest, that you make a unit test based on your cron expression. With kudos to Van de Voorde Toni, you can base it on this code, and use it to verify that the "nextValidTimeAfter" matches your expectation:
import java.text.ParseException;
import java.util.Date;
import org.quartz.CronExpression;
public class CronTester {
public static void main(String[] args) throws ParseException {
final String expression = "* * 17 0 0/2 *,SUN,MON";
final CronExpression cronExpression = new CronExpression(expression);
final Date nextValidDate1 = cronExpression.getNextValidTimeAfter(new Date());
final Date nextValidDate2 = cronExpression.getNextValidTimeAfter(nextValidDate1);
System.out.println(nextValidDate1);
System.out.println(nextValidDate2);
}
}

Delete all files created till yesterday in directory (but not directory) - one liner solution

How can I delete all older files created till yesterday in directory (but not directory)?
I tried with this but unable to get delete files exists till yesterday.
for(File file: new File(strFile).listFiles())
if (!file.isDirectory() && file.lastModified() < 1.0)
file.delete();

First lastModified returns:
A long value representing the time the file was last modified,
measured in milliseconds since the epoch (00:00:00 GMT, January 1,
1970), or 0L if the file does not exist or if an I/O error occurs
You need to get current time in milliseconds then subtract what last modified returns and the verify if it was modified before your target period. Or perform the time calculation in any type you desire.
Following your code:
long day = 1000 * 60 * 60 * 24;
long now = System.currentTimeMillis();
for(File file: new File(DIRECTORY).listFiles())
if (!file.isDirectory() && (now - file.lastModified() > day))
file.delete();
Ideally you would be running this as an scheduled task

here is an example which deletes all temp files more than a day old:
import org.apache.commons.lang3.Validate;
import java.io.File;
import java.util.Arrays;
import java.util.Calendar;
import java.util.Date;
public class StackOverflow44850006 {
/**
* deletes all the files in a directory modified before a given timestamp
*
* #param directoryPath path to delete from
* #param stamp everything with a mod date before this timestamp will be deleted
* #return the number of items deleted
*/
public static long deleteOld(final String directoryPath, final Date stamp) {
final File[] files = new File(directoryPath).listFiles();
Validate.notNull(files, "Unable to open dir. Does it exist?");
return Arrays.stream(files)
.filter(f -> !f.isDirectory())
.filter(f -> f.lastModified() < stamp.getTime())
.map(File::delete)
.filter(Boolean::booleanValue)
.count();
}
public static void main(String[] args) {
final String tempDir = System.getProperty("java.io.tmpdir");
final Calendar yesterday = Calendar.getInstance();
yesterday.add(Calendar.DATE, -1);
System.out.println(deleteOld(tempDir, yesterday.getTime()));
}
}

Unexpected output of InfluxDB batch write

I am using batch processing to write into InfluxDB and below is my code for doing that.
String dbName = "test";
influxDB.query(new Query("CREATE DATABASE " + dbName, dbName));
Stopwatch watch = Stopwatch.createStarted();
influxDB.enableBatch(2000, 100, TimeUnit.MILLISECONDS);
for (int j = 0; j < 100000; j++) {
Point point = Point.measurement("cpu")
.addField("idle", (double) j)
.addField("system", 3.0 * j).build();
influxDB.write(dbName, "autogen", point);
}
influxDB.disableBatch();
System.out.println("Write for " + 100000 + " Points took:" + watch);
}
Here i am writing 100000 points and which is taking very reasonable time to write, however only few records are written into DB instead of expected 100000 records.
select count(idle) from cpu gives me only "89" i am expecting it to be "100000"
While select * from cpu gives me following:
cpu
time idle system
2016-10-06T23:57:41.184Z 8 24
2016-10-06T23:57:41.185Z 196 588
2016-10-06T23:57:41.186Z 436 1308
2016-10-06T23:57:41.187Z 660 1980
2016-10-06T23:57:41.188Z 916 2748
2016-10-06T23:57:41.189Z 1278 3834
2016-10-06T23:57:41.19Z 1405 4215
2016-10-06T23:57:41.191Z 1409 4227
2016-10-06T23:57:41.192Z 1802 5406
2016-10-06T23:57:41.193Z 1999 5997
2016-10-06T23:57:41.456Z 3757 11271
2016-10-06T23:57:41.457Z 3999 11997
2016-10-06T23:57:41.858Z 4826 14478 and so on.....
Here my question is why the values of idle are missing, for example, after 8 it should 9, 10, 11, and so on but these values were not persisted and comes directly 196 and then missing in between and then 436. Any idea how to persist all value of loop variable "j" in this situation?

This line
influxDB.enableBatch(2000, 100, TimeUnit.MILLISECONDS);
says that it will flush input data if there are more than 2000 samples per 100 ms period. Since you are trying to write 100k samples then logically most of them get flushed.
Instead, write less samples in a single batch. My recommendation would be to write 5000 samples in a single batch, and make multiple batches until all your data is in the db.
// Batch 1
influxDB.enableBatch(5000, 100, TimeUnit.MILLISECONDS);
for (int j = 0; j < 5000; j++) {
Point point = Point.measurement("cpu")
.addField("idle", (double) j)
.addField("system", 3.0 * j).build();
influxDB.write(dbName, "autogen", point);
}
influxDB.disableBatch();
// Batch 2
// ...

MongoDB ACKNOWLEDGED write concern faster than UNACKNOWLEDGED?

I've got a very simple test program that performs faster with ACKNOWLEDGED bulk inserts than with UNACKNOWLEDGED. And it's not just a little faster - I'm seeing a factor of nearly 100!
My understanding of the difference between these two write concerns is solely that with ACKNOWLEDGED the client waits for confirmation from the server that the operation has been executed (but not necessarily made durable), while with UNACKNOWLEDGED the client only knows that the request made it out onto the wire. So it would seem preposterous that the former could actually perform at a higher speed, yet that's what I'm seeing.
I'm using the Java driver (v2.12.0) with Oracle's Java JDK v1.7.0_71, and mongo version 3.0.0 on 64-bit Windows 7. I'm running mongod, completely out-of-the-box (fresh install), no sharding or anything. And before each test I ensure that the collection is empty and has no non-default indexes.
I would appreciate any insight into why I'm consistently seeing the opposite of what I'd expect.
Thanks.
Here's my code:
package test;
import com.mongodb.BasicDBObject;
import com.mongodb.BulkWriteOperation;
import com.mongodb.BulkWriteResult;
import com.mongodb.DBCollection;
import com.mongodb.DBObject;
import com.mongodb.MongoClient;
import com.mongodb.ServerAddress;
import com.mongodb.WriteConcern;
import java.util.Arrays;
public class Test {
private static final int BATCHES = 100;
private static final int BATCH_SIZE = 1000;
private static final int COUNT = BATCHES * BATCH_SIZE;
public static void main(String[] argv) throws Exception {
DBCollection coll = new MongoClient(new ServerAddress()).getDB("test").getCollection("test");
for (String wcName : Arrays.asList("UNACKNOWLEDGED", "ACKNOWLEDGED")) {
WriteConcern wc = (WriteConcern) WriteConcern.class.getField(wcName).get(null);
coll.dropIndexes();
coll.remove(new BasicDBObject());
long start = System.currentTimeMillis();
BulkWriteOperation bulkOp = coll.initializeUnorderedBulkOperation();
for (int i = 1; i < COUNT; i++) {
DBObject doc = new BasicDBObject().append("int", i).append("string", Integer.toString(i));
bulkOp.insert(doc);
if (i % BATCH_SIZE == 0) {
BulkWriteResult results = bulkOp.execute(wc);
if (wc == WriteConcern.ACKNOWLEDGED && results.getInsertedCount() != 1000) {
throw new RuntimeException("Bogus insert count: " + results.getInsertedCount());
}
bulkOp = coll.initializeUnorderedBulkOperation();
}
}
long time = System.currentTimeMillis() - start;
double rate = COUNT / (time / 1000.0);
System.out.printf("%s[w=%s,j=%s]: Inserted %d documents in %s # %f/sec\n",
wcName, wc.getW(), wc.getJ(), COUNT, duration(time), rate);
}
}
private static String duration(long msec) {
return String.format("%d:%02d:%02d.%03d",
msec / (60 * 60 * 1000),
(msec % (60 * 60 * 1000)) / (60 * 1000),
(msec % (60 * 1000)) / 1000,
msec % 1000);
}
}
And here's typical output:
UNACKNOWLEDGED[w=0,j=false]: Inserted 100000 documents in 0:01:27.025 # 1149.095088/sec
ACKNOWLEDGED[w=1,j=false]: Inserted 100000 documents in 0:00:00.927 # 107874.865156/sec
EDIT
Ran more extensive tests, per request from Markus W. Mahlberg. For these tests, I ran the code with four write concerns: UNACKNOWLEDGED, ACKNOWLEDGED, JOURNALED, and FSYNCED. (I would expect this order to show decreasing speed.) I ran 112 repetitions, each of which performed 100 batches of 1000 inserts under each of the four write concerns, each time into an empty collection with no indexes. Code was identical to original post but with two additional write concerns, and with output to CSV format for easy analysis.
Results summary:
UNACKNOWLEDGED: 1147.105004 docs/sec avg, std dev 27.88577035
ACKNOWLEDGED: 77539.27653 docs/sec avg, std dev 1567.520303
JOURNALED: 29574.45243 docs/sec avg, std dev 123.9927554
FSYNCED: 29567.02467 docs/sec avg, std dev 147.6150994
The huge inverted performance difference between UNACKNOWLEDGED and ACKNOWLEDGED is what's got me baffled.
Here's the raw data if anyone cares for it ("time" is elapsed msec for 100*1000 insertions; "rate" is docs/second):
"UNACK time","UNACK rate","ACK time","ACK rate","JRNL time","JRNL rate","FSYNC time","FSYNC rate"
92815,1077.4120562409094,1348,74183.9762611276,3380,29585.798816568047,3378,29603.31557134399
90209,1108.5368422219512,1303,76745.97083653108,3377,29612.081729345577,3375,29629.62962962963
91089,1097.8273995762386,1319,75815.01137225171,3382,29568.30277942046,3413,29299.73630237328
90159,1109.1516099335618,1320,75757.57575757576,3375,29629.62962962963,3377,29612.081729345577
89922,1112.0749093658949,1315,76045.62737642587,3380,29585.798816568047,3376,29620.853080568722
89997,1111.1481493827573,1306,76569.67840735069,3381,29577.048210588586,3379,29594.55460195324
90141,1109.373093264996,1319,75815.01137225171,3386,29533.372711163614,3378,29603.31557134399
89771,1113.9454835080371,1325,75471.69811320755,3387,29524.65308532625,3521,28401.022436807725
89716,1114.6283828971423,1325,75471.69811320755,3379,29594.55460195324,3379,29594.55460195324
90205,1108.5859985588381,1323,75585.78987150417,3377,29612.081729345577,3376,29620.853080568722
90092,1109.976468498868,1328,75301.2048192771,3382,29568.30277942046,3379,29594.55460195324
89822,1113.3129968159249,1322,75642.965204236,3385,29542.097488921714,3383,29559.562518474726
89821,1113.3253916122064,1310,76335.87786259541,3380,29585.798816568047,3383,29559.562518474726
89945,1111.7905386625162,1318,75872.53414264036,3379,29594.55460195324,3379,29594.55460195324
89917,1112.1367483345753,1352,73964.49704142011,3381,29577.048210588586,3377,29612.081729345577
90358,1106.7088691648773,1303,76745.97083653108,3377,29612.081729345577,3380,29585.798816568047
90187,1108.8072560346836,1348,74183.9762611276,3387,29524.65308532625,3395,29455.081001472754
90634,1103.3387029150208,1322,75642.965204236,3384,29550.827423167848,3381,29577.048210588586
90148,1109.2869503483162,1331,75131.48009015778,3389,29507.22927117144,3381,29577.048210588586
89767,1113.9951207013714,1321,75700.22710068131,3380,29585.798816568047,3382,29568.30277942046
89910,1112.2233344455567,1321,75700.22710068131,3381,29577.048210588586,3385,29542.097488921714
89852,1112.9412812180028,1316,75987.84194528875,3381,29577.048210588586,3401,29403.116730373422
89537,1116.8567184515898,1319,75815.01137225171,3380,29585.798816568047,3380,29585.798816568047
89763,1114.0447623185498,1331,75131.48009015778,3380,29585.798816568047,3382,29568.30277942046
90070,1110.2475852115022,1325,75471.69811320755,3383,29559.562518474726,3378,29603.31557134399
89771,1113.9454835080371,1302,76804.91551459293,3389,29507.22927117144,3378,29603.31557134399
90518,1104.7526458825869,1325,75471.69811320755,3383,29559.562518474726,3380,29585.798816568047
90314,1107.2480457071995,1322,75642.965204236,3380,29585.798816568047,3384,29550.827423167848
89874,1112.6688474976079,1329,75244.54477050414,3386,29533.372711163614,3379,29594.55460195324
89954,1111.6793027547415,1318,75872.53414264036,3381,29577.048210588586,3381,29577.048210588586
89903,1112.3099340400208,1325,75471.69811320755,3379,29594.55460195324,3388,29515.9386068477
89842,1113.0651588343983,1314,76103.500761035,3382,29568.30277942046,3377,29612.081729345577
89746,1114.2557885588217,1325,75471.69811320755,3378,29603.31557134399,3385,29542.097488921714
93249,1072.3975592231552,1327,75357.95026375283,3381,29577.048210588586,3377,29612.081729345577
93638,1067.9425019756936,1331,75131.48009015778,3377,29612.081729345577,3392,29481.132075471698
87775,1139.2765593847905,1340,74626.86567164179,3379,29594.55460195324,3378,29603.31557134399
86495,1156.136192843517,1271,78678.20613690009,3375,29629.62962962963,3376,29620.853080568722
85584,1168.442699570013,1276,78369.90595611285,3432,29137.529137529138,3376,29620.853080568722
86648,1154.094728095282,1278,78247.2613458529,3382,29568.30277942046,3411,29316.91586045148
85745,1166.2487608606916,1274,78492.93563579278,3380,29585.798816568047,3363,29735.355337496283
85813,1165.3246011676551,1279,78186.08287724786,3375,29629.62962962963,3376,29620.853080568722
85831,1165.0802157728558,1288,77639.75155279503,3376,29620.853080568722,3377,29612.081729345577
85807,1165.4060857505797,1259,79428.11755361399,3466,28851.702250432772,3375,29629.62962962963
85964,1163.2776511097668,1258,79491.2559618442,3378,29603.31557134399,3378,29603.31557134399
85854,1164.7680946723508,1257,79554.49482895785,3382,29568.30277942046,3375,29629.62962962963
85787,1165.6777833471272,1257,79554.49482895785,3377,29612.081729345577,3377,29612.081729345577
85537,1169.084723569917,1272,78616.35220125786,3377,29612.081729345577,3377,29612.081729345577
85408,1170.8505058074186,1271,78678.20613690009,3375,29629.62962962963,3425,29197.080291970804
85577,1168.5382754712132,1261,79302.14115781126,3378,29603.31557134399,3375,29629.62962962963
85663,1167.365140142185,1261,79302.14115781126,3377,29612.081729345577,3378,29603.31557134399
85812,1165.3381811401669,1273,78554.59544383347,3377,29612.081729345577,3378,29603.31557134399
85783,1165.7321380693145,1273,78554.59544383347,3377,29612.081729345577,3376,29620.853080568722
85682,1167.106276697556,1280,78125.0,3381,29577.048210588586,3376,29620.853080568722
85753,1166.1399601180133,1260,79365.07936507936,3379,29594.55460195324,3377,29612.081729345577
85573,1168.5928972923703,1332,75075.07507507507,3377,29612.081729345577,3377,29612.081729345577
86206,1160.0120641254668,1263,79176.56373713381,3376,29620.853080568722,3383,29559.562518474726
85593,1168.31983923919,1264,79113.92405063291,3380,29585.798816568047,3378,29603.31557134399
85903,1164.1036983574495,1261,79302.14115781126,3378,29603.31557134399,3377,29612.081729345577
85516,1169.3718134618082,1277,78308.53563038372,3375,29629.62962962963,3376,29620.853080568722
85553,1168.8660830128692,1291,77459.3338497289,3490,28653.295128939826,3377,29612.081729345577
85550,1168.907071887785,1293,77339.52049497294,3379,29594.55460195324,3379,29594.55460195324
85610,1168.0878402055835,1298,77041.60246533128,3384,29550.827423167848,3378,29603.31557134399
85522,1169.2897733916418,1267,78926.59826361484,3379,29594.55460195324,3379,29594.55460195324
85595,1168.2925404521293,1276,78369.90595611285,3379,29594.55460195324,3376,29620.853080568722
85451,1170.2613193526115,1286,77760.49766718507,3376,29620.853080568722,3391,29489.82601002654
85792,1165.609847071988,1252,79872.20447284346,3382,29568.30277942046,3376,29620.853080568722
86501,1156.0559993526085,1255,79681.2749003984,3379,29594.55460195324,3379,29594.55460195324
85718,1166.616113301757,1269,78802.20646178094,3382,29568.30277942046,3376,29620.853080568722
85605,1168.156065650371,1265,79051.38339920949,3378,29603.31557134399,3380,29585.798816568047
85398,1170.9876109510762,1274,78492.93563579278,3377,29612.081729345577,3395,29455.081001472754
86370,1157.809424568716,1273,78554.59544383347,3376,29620.853080568722,3376,29620.853080568722
85905,1164.0765962400326,1280,78125.0,3379,29594.55460195324,3379,29594.55460195324
86020,1162.5203441060219,1285,77821.01167315176,3375,29629.62962962963,3376,29620.853080568722
85726,1166.5072440099852,1272,78616.35220125786,3380,29585.798816568047,3380,29585.798816568047
85628,1167.8422945765403,1270,78740.15748031496,3379,29594.55460195324,3376,29620.853080568722
85989,1162.93944574306,1258,79491.2559618442,3376,29620.853080568722,3378,29603.31557134399
85981,1163.047650062223,1276,78369.90595611285,3376,29620.853080568722,3376,29620.853080568722
86558,1155.2947156819703,1269,78802.20646178094,3385,29542.097488921714,3378,29603.31557134399
85745,1166.2487608606916,1293,77339.52049497294,3378,29603.31557134399,3375,29629.62962962963
85544,1168.9890582624148,1266,78988.94154818325,3376,29620.853080568722,3377,29612.081729345577
85536,1169.0983913206135,1268,78864.35331230283,3380,29585.798816568047,3380,29585.798816568047
85477,1169.9053546568082,1278,78247.2613458529,3388,29515.9386068477,3377,29612.081729345577
85434,1170.4941826439124,1253,79808.45969672786,3378,29603.31557134399,3375,29629.62962962963
85609,1168.1014846569872,1276,78369.90595611285,3364,29726.516052318668,3376,29620.853080568722
85740,1166.316771635176,1258,79491.2559618442,3377,29612.081729345577,3377,29612.081729345577
85640,1167.6786548341897,1266,78988.94154818325,3378,29603.31557134399,3377,29612.081729345577
85648,1167.569587147394,1281,78064.012490242,3378,29603.31557134399,3376,29620.853080568722
85697,1166.9019919017,1287,77700.0777000777,3377,29612.081729345577,3378,29603.31557134399
85696,1166.9156086631815,1256,79617.83439490446,3379,29594.55460195324,3376,29620.853080568722
85782,1165.7457275419085,1258,79491.2559618442,3379,29594.55460195324,3379,29594.55460195324
85837,1164.9987767512844,1264,79113.92405063291,3379,29594.55460195324,3376,29620.853080568722
85632,1167.7877428998504,1278,78247.2613458529,3380,29585.798816568047,3459,28910.089621277824
85517,1169.3581393173288,1256,79617.83439490446,3379,29594.55460195324,3380,29585.798816568047
85990,1162.925921618793,1302,76804.91551459293,3380,29585.798816568047,3377,29612.081729345577
86690,1153.535586572846,1281,78064.012490242,3375,29629.62962962963,3381,29577.048210588586
86045,1162.1825788831425,1274,78492.93563579278,3380,29585.798816568047,3383,29559.562518474726
86146,1160.820003250296,1274,78492.93563579278,3382,29568.30277942046,3418,29256.87536571094
86027,1162.4257500552153,1280,78125.0,3382,29568.30277942046,3381,29577.048210588586
85992,1162.8988743138896,1281,78064.012490242,3376,29620.853080568722,3380,29585.798816568047
85857,1164.727395553071,1288,77639.75155279503,3382,29568.30277942046,3376,29620.853080568722
85853,1164.7816616775185,1284,77881.6199376947,3375,29629.62962962963,3374,29638.41138114997
86069,1161.8585088707896,1295,77220.07722007722,3378,29603.31557134399,3378,29603.31557134399
85842,1164.930919596468,1296,77160.49382716049,3378,29603.31557134399,3376,29620.853080568722
86195,1160.160102094089,1301,76863.95080707148,3376,29620.853080568722,3379,29594.55460195324
85523,1169.2761011657683,1305,76628.35249042146,3376,29620.853080568722,3378,29603.31557134399
85752,1166.1535591006625,1275,78431.37254901961,3374,29638.41138114997,3377,29612.081729345577
85441,1170.3982865369085,1286,77760.49766718507,3377,29612.081729345577,3380,29585.798816568047
85566,1168.6884977678048,1265,79051.38339920949,3377,29612.081729345577,3380,29585.798816568047
85523,1169.2761011657683,1267,78926.59826361484,3377,29612.081729345577,3376,29620.853080568722
86152,1160.7391586962578,1285,77821.01167315176,3374,29638.41138114997,3378,29603.31557134399
85684,1167.0790345922226,1272,78616.35220125786,3378,29603.31557134399,3384,29550.827423167848
86252,1159.3934053703103,1271,78678.20613690009,3376,29620.853080568722,3377,29612.081729345577

Frame to Timecode Conversion HTML5

I'm looking to convert a frame number (for a given sample rate) into time in HTML5.
eg:
sample_rate = 24fps
frame 226 => ? time
Please advise.

24 fps = 1 second (your sample rate = 1 second)
226 frames / 24 fps = 9,41 seconds
total / frame_rate = time in seconds
Example:
video.currentTime = 9.41;

Categories