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Consulting Engineer, MongoDB
Bryan Reinero
#ConferenceHashTag
Time Series Data- Part 2
Aggregations in Action
Real Time Traffic Data Project
Our network of 16,000 speed sensors report
data every minute.
What we want from our data
Charting and Trending
What we want from our data
Historical & Predictive Analysis
What we want from our data
Real Time Traffic Dashboard
Document Structure
{ _id: ObjectId("5382ccdd58db8b81730344e2"),
linkId: 900006,
date: ISODate("2014-03-12T17:00:00Z"),
data: [
{ speed: NaN, time: NaN },
{ speed: NaN, time: NaN },
{ speed: NaN, time: NaN },
...
],
conditions: {
status: "Snow / Ice Conditions",
pavement: "Icy Spots",
weather: "Light Snow"
}
}
Sample Document Structure
Compound, unique
Index identifies the
Individual document
{ _id: ObjectId("5382ccdd58db8b81730344e2"),
linkId: 900006,
date: ISODate("2014-03-12T17:00:00Z"),
data: [
{ speed: NaN, time: NaN },
{ speed: NaN, time: NaN },
{ speed: NaN, time: NaN },
...
],
conditions: {
status: "Snow / Ice Conditions",
pavement: "Icy Spots",
weather: "Light Snow"
}
}
Sample Document Structure
Saves an extra index
{ _id: “900006:14031217”,
data: [
{ speed: NaN, time: NaN },
{ speed: NaN, time: NaN },
{ speed: NaN, time: NaN },
...
],
conditions: {
status: "Snow / Ice Conditions",
pavement: "Icy Spots",
weather: "Light Snow"
}
}
{ _id: “900006:14031217”,
data: [
{ speed: NaN, time: NaN },
{ speed: NaN, time: NaN },
{ speed: NaN, time: NaN },
...
],
conditions: {
status: "Snow / Ice Conditions",
pavement: "Icy Spots",
weather: "Light Snow"
}
}
Sample Document Structure
Range queries:
/^900006:1403/
Regex must be
left-anchored &
case-sensitive
{ _id: “900006:140312”,
data: [
{ speed: NaN, time: NaN },
{ speed: NaN, time: NaN },
{ speed: NaN, time: NaN },
...
],
conditions: {
status: "Snow / Ice Conditions",
pavement: "Icy Spots",
weather: "Light Snow"
}
}
Sample Document Structure
Pre-allocated,
60 element array of
per-minute data
Charts
0
10
20
30
40
50
60
70
MonMar10201404:57:00…
MonMar10201405:31:00…
MonMar10201406:05:00…
MonMar10201406:39:00…
MonMar10201407:13:00…
MonMar10201407:47:00…
MonMar10201408:21:00…
MonMar10201408:55:00…
MonMar10201409:29:00…
MonMar10201410:04:00…
MonMar10201410:38:00…
MonMar10201411:55:00…
TueMar11201402:41:00…
TueMar11201403:15:00…
TueMar11201403:49:00…
TueMar11201404:39:00…
TueMar11201405:13:00…
TueMar11201405:47:00…
TueMar11201406:21:00…
TueMar11201406:55:00…
TueMar11201407:29:00…
TueMar11201408:03:00…
TueMar11201408:37:00…
TueMar11201409:18:00…
TueMar11201410:44:00…
TueMar11201411:18:00…
TueMar11201411:53:00…
TueMar11201412:27:00…
TueMar11201413:04:00…
TueMar11201413:38:00…
TueMar11201414:15:00…
TueMar11201416:56:00…
WedMar12201401:45:00…
WedMar12201402:19:00…
WedMar12201402:53:00…
WedMar12201403:27:00…
WedMar12201406:46:00…
WedMar12201408:26:00…
WedMar12201409:00:00…
WedMar12201410:12:00…
WedMar12201410:46:00…
db.linkData.find( { _id : /^20484097:2014031/ } )
Rollups
{ _id: "20484097:20140204",
hours: [
{ speed: { sum: 1889, count: 60 }
time: { sum: 20562, count: 60 },
conditions: {
status: "Snow / Ice Conditions",
pavement: "Icy Spots",
weather: "Light Snow"
}
},
{ speed: {m: 1892, count: 60 },
time: {sum: 20442, count: 60 },
conditions: {
status: "Snow / Ice Conditions",
pavement: "Slush",
weather: "Light Snow"
}
}
]}
Document retention
Doc per hour
Doc per day
2 days
2 months
1year
Doc per Month
Analysis with The Aggregation
Framework
Pipelining operations
grep | sort | uniq
Piping command line operations
Pipelining operations
$match $group | $sort|
Piping aggregation operations
Stream of documents Result documents
What is the average speed for a
given road segment?
> db.linkData.aggregate(
{ $match: { ”_id" : /^20484097:/ } },
{ $project: { "data.speed": 1 } } ,
{ $unwind: "$data"},
{ $group: { _id: “”, ave: { $avg: "$data.speed"} } }
);
{ "_id" : 20484097, "ave" : 47.067650676506766 }
What is the average speed for a
given road segment?
Select documents on the target segment
> db.linkData.aggregate(
{ $match: { ”_id" : /^20484097:/ } },
{ $project: { "data.speed": 1, linkId: 1 } } ,
{ $unwind: "$data"},
{ $group: { _id: "$linkId", ave: { $avg: "$data.speed"} } }
);
{ "_id" : 20484097, "ave" : 47.067650676506766 }
What is the average speed for a
given road segment?
Keep only the fields we really need
> db.linkData.aggregate(
{ $match: { ”_id" : /^20484097:/ } },
{ $project: { "data.speed": 1, linkId: 1 } } ,
{ $unwind: "$data"},
{ $group: { _id: "$linkId", ave: { $avg: "$data.speed"} } }
);
{ "_id" : 20484097, "ave" : 47.067650676506766 }
What is the average speed for a
given road segment?
Loop over the array of data points
> db.linkData.aggregate(
{ $match: { ”_id" : /^20484097:/ } },
{ $project: { "data.speed": 1, linkId: 1 } } ,
{ $unwind: "$data"},
{ $group: { _id: "$linkId", ave: { $avg: "$data.speed"} } }
);
{ "_id" : 20484097, "ave" : 47.067650676506766 }
What is the average speed for a
given road segment?
Use the handy $avg operator
> db.linkData.aggregate(
{ $match: { ”_id" : /^20484097:/ } },
{ $project: { "data.speed": 1, linkId: 1 } } ,
{ $unwind: "$data"},
{ $group: { _id: "$linkId", ave: { $avg: "$data.speed"} } }
);
{ "_id" : 20484097, "ave" : 47.067650676506766 }
More Sophisticated Pipelines:
average speed with variance
{ "$project" : {
mean: "$meanSpd",
spdDiffSqrd : {
"$map" : {
"input": {
"$map" : {
"input" : "$speeds",
"as" : "samp",
"in" : { "$subtract" : [ "$$samp", "$meanSpd" ] }
}
},
as: "df", in: { $multiply: [ "$$df", "$$df" ] }
} } } },
{ $unwind: "$spdDiffSqrd" },
{ $group: { _id: mean: "$mean", variance: { $avg: "$spdDiffSqrd" } } }
Historic Analysis
How does weather and road conditions affect
traffic?
The Ask: what are the average speeds per
weather, status and pavement
MapReduce
function map() {
for( var i = 0; i < this.data.length; i++ ) {
emit (
this.conditions.weather,
{ speed : this.data[i].speed }
);
emit (
this.conditions.status,
{ speed : this.data[i].speed }
);
emit (
this.conditions.pavement,
{ speed : this.data[i].speed }
);
} }
MapReduce
function map() {
for( var i = 0; i < this.data.length; i++ ) {
emit (
this.conditions.weather,
{ speed : this.data[i].speed }
);
emit (
this.conditions.status,
{ speed : this.data[i].speed }
);
emit (
this.conditions.pavement,
{ speed : this.data[i].speed }
);
} }
“Snow”,
34
MapReduce
function map() {
for( var i = 0; i < this.data.length; i++ ) {
emit (
this.conditions.weather,
{ speed : this.data[i].speed }
);
emit (
this.conditions.status,
{ speed : this.data[i].speed }
);
emit (
this.conditions.pavement,
{ speed : this.data[i].speed }
);
} }
“Icy spots”, 34
MapReduce
function map() {
for( var i = 0; i < this.data.length; i++ ) {
emit (
this.conditions.weather,
{ speed : this.data[i].speed }
);
emit (
this.conditions.status,
{ speed : this.data[i].speed }
);
emit (
this.conditions.pavement,
{ speed : this.data[i].speed }
);
} }
“Delays”, 34
MapReduce
MapReduce
Weather: “Rain”, speed: 44
MapReduce
Weather: “Rain”, speed: 39
MapReduce
Weather: “Rain”, speed: 46
MapReduce
function reduce ( key, values ) {
var result = { count : 1, speedSum : 0 };
values.forEach( function( v ){
result.speedSum += v.speed;
result.count++;
});
return result;
}
MapReduce
function reduce ( key, values ) {
var result = { count : 1, speedSum : 0 };
values.forEach( function( v ){
result.speedSum += v.speed;
result.count++;
});
return result;
}
Results
results: [
{
"_id" : "Generally Clear and Dry Conditions",
"value" : {
"count" : 902,
"speedSum" : 45100
}
},
{
"_id" : "Icy Spots",
"value" : {
"count" : 242,
"speedSum" : 9438
}
},
{
"_id" : "Light Snow",
"value" : {
"count" : 122,
"speedSum" : 7686
}
},
{
"_id" : "No Report",
"value" : {
"count" : 782,
"speedSum" : NaN
}
}
Processing Large Data Sets
• Need to break data into smaller pieces
• Process data across multiple nodes
Hadoop Hadoop Hadoop Hadoop
Hadoop Hadoop Hadoop HadoopHadoop
Hadoop
Benefits of the Hadoop Connector
• Increased parallelism
• Access to analytics libraries
• Separation of concerns
• Integrates with existing tool chains
• Drivers will be accessing the data via web, mobile
devices, and navigation systems
• We need to provide current average speed, travel time
and weather per road segment
Real-time Dashboard
Current Real-Time Conditions
Last ten minutes of speeds and
times
{ _id : “I-87:10656”,
description : "NYS Thruway Harriman Section Exits 14A - 16",
update : ISODate(“2013-10-10T23:06:37.000Z”),
speeds : [ 52, 49, 45, 51, ... ],
times : [ 237, 224, 246, 233,... ],
pavement: "Wet Spots",
status: "Wet Conditions",
weather: "Light Rain”,
averageSpeed: 50.23,
averageTime: 234,
maxSafeSpeed: 53.1,
location" : {
"type" : "LineString",
"coordinates" : [
[ -74.056, 41.098 ],
[ -74.077, 41.104 ] }
}
{ _id : “I-87:10656”,
description : "NYS Thruway Harriman Section Exits 14A - 16",
update : ISODate(“2013-10-10T23:06:37.000Z”),
speeds : [ 52, 49, 45, 51, ... ],
times : [ 237, 224, 246, 233,... ],
pavement: "Wet Spots",
status: "Wet Conditions",
weather: "Light Rain”,
averageSpeed: 50.23,
averageTime: 234,
maxSafeSpeed: 53.1,
location" : {
"type" : "LineString",
"coordinates" : [
[ -74.056, 41.098 ],
[ -74.077, 41.104 ] }
}
Current Real-Time Conditions
Pre-aggregated
metrics
{ _id : “I-87:10656”,
description : "NYS Thruway Harriman Section Exits 14A - 16",
update : ISODate(“2013-10-10T23:06:37.000Z”),
speeds : [ 52, 49, 45, 51, ... ],
times : [ 237, 224, 246, 233,... ],
pavement: "Wet Spots",
status: "Wet Conditions",
weather: "Light Rain”,
averageSpeed: 50.23,
averageTime: 234,
maxSafeSpeed: 53.1,
location" : {
"type" : "LineString",
"coordinates" : [
[ -74.056, 41.098 ],
[ -74.077, 41.104 ] }
}
Current Real-Time Conditions
Geo-spatially indexed
road segment
db.linksAvg.update(
{"_id" : linkId},
{ "$set" : {"update " : date},
"$push" : {
"times" : { "$each" : [ time ], "$slice" : -10 },
"speeds" : {"$each" : [ speed ], "$slice" : -10}
}
})
Maintaining the current conditions
Each update pops the last element off the
array and pushes the new value
Putting it all together
Patterns common to time series
data:
• You need to store and manage an incoming
stream of data samples
• You need to compute derivative data sets based
on these samples
• You need low latency access to up-to-date data
Patterns common to time series
data:
• You need to store and manage an incoming
stream of data samples
• You need to compute derivative data sets based
on these samples
• You need low latency access to up-to-date data
Introducing The High Volume Data
Feed
HVDF: Reference Implementation
Screech -- High Volume Data Feed engine
REST
Service API
Processor
Plugins
Inline
Batch
Stream
Channel Data Storage
Raw
Channel
Data
Aggregated
Rollup T1
Aggregated
Rollup T2
Query Processor Streaming spout
Custom Stream
Processing Logic
Incoming Sample Stream
POST /feed/channel/data
GET
/feed/channeldata?time=XX
X&range=YYY
Real-time Queries
HVDF:
https://github.com/10gen-labs/hvdf
Hadoop Connector:
https://github.com/mongodb/mongo-hadoop
Consulting Engineer, MongoDB Inc.
Bryan Reinero
#MongoDBWorld
Thank You

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MongoDB for Time Series Data Part 2: Analyzing Time Series Data Using the Aggregation Framework and Hadoop

Hinweis der Redaktion

  1. Reports (group, summing, averaging) Analytics(incremental reporting, rollups) Analysis (trends, segmentation, anomalies) Analytics (regression, forecasting, filtering) Warehousing (long term storage and simplified querying)
  2. Compound unique index on linkId & Interval update field used to identify new documents for aggregation
  3. Compound unique index on linkId & Interval update field used to identify new documents for aggregation
  4. Compound unique index on linkId & Interval update field used to identify new documents for aggregation
  5. Compound unique index on linkId & Interval update field used to identify new documents for aggregation
  6. Compound unique index on linkId & Interval update field used to identify new documents for aggregation
  7. Priority Floating point number between 0..1000 Highest member that is up to date wins Up to date == within 10 seconds of primary If a higher priority member catches up, it will force election and win Slave Delay Lags behind master by configurable time delay Automatically hidden from clients Protects against operator errors Fat fingering Application corrupts data
  8. Compound unique index on linkId & Interval update field used to identify new documents for aggregation
  9. Priority Floating point number between 0..1000 Highest member that is up to date wins Up to date == within 10 seconds of primary If a higher priority member catches up, it will force election and win Slave Delay Lags behind master by configurable time delay Automatically hidden from clients Protects against operator errors Fat fingering Application corrupts data
  10. Priority Floating point number between 0..1000 Highest member that is up to date wins Up to date == within 10 seconds of primary If a higher priority member catches up, it will force election and win Slave Delay Lags behind master by configurable time delay Automatically hidden from clients Protects against operator errors Fat fingering Application corrupts data
  11. Priority Floating point number between 0..1000 Highest member that is up to date wins Up to date == within 10 seconds of primary If a higher priority member catches up, it will force election and win Slave Delay Lags behind master by configurable time delay Automatically hidden from clients Protects against operator errors Fat fingering Application corrupts data
  12. Priority Floating point number between 0..1000 Highest member that is up to date wins Up to date == within 10 seconds of primary If a higher priority member catches up, it will force election and win Slave Delay Lags behind master by configurable time delay Automatically hidden from clients Protects against operator errors Fat fingering Application corrupts data
  13. Priority Floating point number between 0..1000 Highest member that is up to date wins Up to date == within 10 seconds of primary If a higher priority member catches up, it will force election and win Slave Delay Lags behind master by configurable time delay Automatically hidden from clients Protects against operator errors Fat fingering Application corrupts data
  14. Priority Floating point number between 0..1000 Highest member that is up to date wins Up to date == within 10 seconds of primary If a higher priority member catches up, it will force election and win Slave Delay Lags behind master by configurable time delay Automatically hidden from clients Protects against operator errors Fat fingering Application corrupts data
  15. Priority Floating point number between 0..1000 Highest member that is up to date wins Up to date == within 10 seconds of primary If a higher priority member catches up, it will force election and win Slave Delay Lags behind master by configurable time delay Automatically hidden from clients Protects against operator errors Fat fingering Application corrupts data
  16. Priority Floating point number between 0..1000 Highest member that is up to date wins Up to date == within 10 seconds of primary If a higher priority member catches up, it will force election and win Slave Delay Lags behind master by configurable time delay Automatically hidden from clients Protects against operator errors Fat fingering Application corrupts data
  17. Priority Floating point number between 0..1000 Highest member that is up to date wins Up to date == within 10 seconds of primary If a higher priority member catches up, it will force election and win Slave Delay Lags behind master by configurable time delay Automatically hidden from clients Protects against operator errors Fat fingering Application corrupts data
  18. Priority Floating point number between 0..1000 Highest member that is up to date wins Up to date == within 10 seconds of primary If a higher priority member catches up, it will force election and win Slave Delay Lags behind master by configurable time delay Automatically hidden from clients Protects against operator errors Fat fingering Application corrupts data
  19. Reports (group, summing, averaging) Analytics(incremental reporting, rollups) Analysis (trends, segmentation, anomalies) Analytics (regression, forecasting, filtering) Warehousing (long term storage and simplified querying)
  20. Reports (group, summing, averaging) Analytics(incremental reporting, rollups) Analysis (trends, segmentation, anomalies) Analytics (regression, forecasting, filtering) Warehousing (long term storage and simplified querying)
  21. Reports (group, summing, averaging) Analytics(incremental reporting, rollups) Analysis (trends, segmentation, anomalies) Analytics (regression, forecasting, filtering) Warehousing (long term storage and simplified querying)