MongoDB for Time Series Data: Analyzing Time Series Data Using the Aggregation Framework and Hadoop
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This document discusses using time series data from traffic sensors to monitor road conditions and support navigation systems. It reviews using MongoDB to store sensor data and perform aggregations to calculate metrics like average speed. MapReduce and the aggregation framework are demonstrated for queries like calculating average speeds by weather, road status, or pavement conditions. Hadoop and the MongoDB connector for Hadoop are mentioned for processing large datasets in parallel across nodes.
![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: ”Ice Spots",
weather: ”Light Snow"
}
}](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-12-320.jpg)
![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"
}
}](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-13-320.jpg)
![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"
}
}](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-14-320.jpg)
![{ _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](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-15-320.jpg)
![{ _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
Pre-allocated,
60 element array of
per-minute data](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-16-320.jpg)
![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 ] }
}](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-21-320.jpg)
![{ _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](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-22-320.jpg)
![{ _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](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-23-320.jpg)
![db.linksAvg.update(
{"_id" : linkId},
{ "$set" : {"lUpdate" : date},
"$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](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-24-320.jpg)
![Rollup – 1 day
// daily document
// retained for 2 months
{
_id: "link:date",
// 24 element array
hourly: [
{ speed: { sum: , count: },
time: { sum: , count: }
},
{ speed: { sum: , count: },
time: { sum: , count: }
}
]
}](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-27-320.jpg)
![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" } } }](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-36-320.jpg)
![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 }
);
} }](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-39-320.jpg)
![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](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-40-320.jpg)
![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](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-41-320.jpg)
![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](https://image.slidesharecdn.com/timeseriesaggregationv10edited-140709151941-phpapp02/85/MongoDB-for-Time-Series-Data-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-42-320.jpg)
MongoDB for Time Series Data: Analyzing Time Series Data Using the Aggregation Framework and Hadoop
- 1. Solution Architect Jay Runkel @jayrunkel Time Series Data: Aggregations in Action
- 2. Agenda • Review Traffic Use Case • Review Schema Design • Document Retention Model • Aggregation Queries • Map Reduce • Hadoop
- 4. We need to prepare for this
- 5. Develop Nationwide traffic monitoring system
- 7. Traffic sensors to monitor interstate conditions • 16,000 sensors • Measure at one minute intervals • Speed • Travel time • Weather, pavement, and traffic conditions • Support desktop, mobile, and car navigation systems
- 8. What we want from our data Charting and Trending
- 9. What we want from our data Historical & Predictive Analysis
- 10. What we want from our data Real Time Traffic Dashboard
- 12. 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: ”Ice Spots", weather: ”Light Snow" } }
- 13. 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" } }
- 14. 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" } }
- 15. { _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
- 16. { _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 Pre-allocated, 60 element array of per-minute data
- 17. Advantages 1. In place updates efficient 2. Dashboards simple queries
- 19. Supporting Queries From Navigation Systems
- 20. Navigation System Queries What is the average speed for the last 10 minutes on 50 upcoming road segments?
- 21. 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 ] } }
- 22. { _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
- 23. { _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
- 24. db.linksAvg.update( {"_id" : linkId}, { "$set" : {"lUpdate" : 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
- 26. Document retention Doc per hour Doc per day 2 weeks 2 months 1year Doc per Month
- 27. Rollup – 1 day // daily document // retained for 2 months { _id: "link:date", // 24 element array hourly: [ { speed: { sum: , count: }, time: { sum: , count: } }, { speed: { sum: , count: }, time: { sum: , count: } } ] }
- 28. Analysis With The Aggregation Framework
- 29. Pipelining operations grep | sort | uniq Piping command line operations
- 30. Pipelining operations $match $group | $sort| Piping aggregation operations Stream of documents Result document
- 31. What is the average speed for a given road 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 }
- 32. 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 }
- 33. 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, _id: 1 } } , { $unwind: "$data"}, { $group: { _id: "$_id", ave: { $avg: "$data.speed"} } } ); { "_id" : 20484097, "ave" : 47.067650676506766 }
- 34. 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, _id: 1 } } , { $unwind: "$data"}, { $group: { _id: "$_id", ave: { $avg: "$data.speed"} } } ); { "_id" : 20484097, "ave" : 47.067650676506766 }
- 35. 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, “_id”: 1 } } , { $unwind: "$data"}, { $group: { _id: "$_id", ave: { $avg: "$data.speed"} } } ); { "_id" : 20484097, "ave" : 47.067650676506766 }
- 36. 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" } } }
- 38. Historic Analysis How does weather and road conditions affect traffic? The Ask: what are the average speeds per weather, status and pavement
- 39. 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 } ); } }
- 40. 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
- 41. 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
- 42. 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
- 43. MapReduce
- 47. MapReduce function reduce ( key, values ) { var result = { count : 1, speedSum : 0 }; values.forEach( function( v ){ result.speedSum += v.speed; result.count++; }); return result; }
- 48. MapReduce function reduce ( key, values ) { var result = { count : 1, speedSum : 0 }; values.forEach( function( v ){ result.speedSum += v.speed; result.count++; }); return result; }
- 49. 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 } }
- 50. Analysis With Hadoop (using the MongoDB Connector)
- 51. 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
- 52. Benefits of the Hadoop Connector • Increased parallelism • Access to analytics libraries • Separation of concerns • Integrates with existing tool chains
- 53. MongoDB Hadoop Connector • Multi-source analytics • Interactive & Batch • Data lake • Online, Real-time • High concurrency & HA • Live analytics Operational Post Processingand MongoDB Connector for Hadoop
- 54. Questions? @jayrunkel jay.runkel@mongodb.com Part 3 - July 16th, 2:00 PM EST
- 55. Sign up for our “Path to Proof” Program and get expert advice on implementation, architecture, and configuration. www.mongodb.com/lp/contact/path-proof-program
- 58. Consulting Engineer, MongoDB Inc. Bryan Reinero #ConferenceHashtag Thank You
Editor's Notes
- #13: Compound unique index on linkId & Interval update field used to identify new documents for aggregation
- #14: Compound unique index on linkId & Interval update field used to identify new documents for aggregation
- #15: Compound unique index on linkId & Interval update field used to identify new documents for aggregation
- #16: Compound unique index on linkId & Interval update field used to identify new documents for aggregation
- #17: Compound unique index on linkId & Interval update field used to identify new documents for aggregation
- #19: 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
- #22: 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
- #23: 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
- #24: 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
- #25: 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
- #28: Compound unique index on linkId & Interval update field used to identify new documents for aggregation
- #32: 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
- #33: 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
- #34: 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
- #35: 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
- #36: 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
- #37: 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
- #54: Makes MongoDB a Hadoop-enabled file system Read and write to live data, in-place Copy data between Hadoop and MongoDB Uses MongoDB indexes to filter data Full support for data processing Hive MapReduce Pig Streaming