MongoDB for Time Series Data Part 2: Analyzing Time Series Data Using the Aggregation Framework and Hadoop
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The document discusses the implementation of a time series data aggregator using MongoDB for real-time traffic data analysis, focusing on creating a traffic dashboard supported by various data structures, including sample documents and aggregation operations. It covers topics such as document retention, historical analysis, and the use of MapReduce functions to analyze traffic impact based on weather conditions. Additionally, it highlights the benefits of a Hadoop connector for processing large datasets and improving analytic capabilities.
![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"
}
}](https://image.slidesharecdn.com/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-6-320.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/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-7-320.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/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-8-320.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/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-9-320.jpg)
![{ _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](https://image.slidesharecdn.com/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-10-320.jpg)
![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"
}
}
]}](https://image.slidesharecdn.com/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-12-320.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/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-22-320.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/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-24-320.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/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-25-320.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/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-26-320.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/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-27-320.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/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-38-320.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/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-39-320.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/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-40-320.jpg)
![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](https://image.slidesharecdn.com/timeseriesaggregations-140805154418-phpapp02/85/MongoDB-for-Time-Series-Data-Part-2-Analyzing-Time-Series-Data-Using-the-Aggregation-Framework-and-Hadoop-41-320.jpg)
MongoDB for Time Series Data Part 2: Analyzing Time Series Data Using the Aggregation Framework and Hadoop
- 1. Consulting Engineer, MongoDB Bryan Reinero #ConferenceHashTag Time Series Data- Part 2 Aggregations in Action
- 2. Real Time Traffic Data Project Our network of 16,000 speed sensors report data every minute.
- 3. What we want from our data Charting and Trending
- 4. What we want from our data Historical & Predictive Analysis
- 5. What we want from our data Real Time Traffic Dashboard
- 6. 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" } }
- 7. 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" } }
- 8. 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" } }
- 9. { _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
- 10. { _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
- 12. 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" } } ]}
- 13. Document retention Doc per hour Doc per day 2 days 2 months 1year Doc per Month
- 14. Analysis with The Aggregation Framework
- 15. Pipelining operations grep | sort | uniq Piping command line operations
- 16. Pipelining operations $match $group | $sort| Piping aggregation operations Stream of documents Result documents
- 17. 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 }
- 18. 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 }
- 19. 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 }
- 20. 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 }
- 21. 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 }
- 22. 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" } } }
- 23. Historic Analysis How does weather and road conditions affect traffic? The Ask: what are the average speeds per weather, status and pavement
- 24. 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 } ); } }
- 25. 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
- 26. 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
- 27. 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
- 28. MapReduce
- 32. MapReduce function reduce ( key, values ) { var result = { count : 1, speedSum : 0 }; values.forEach( function( v ){ result.speedSum += v.speed; result.count++; }); return result; }
- 33. MapReduce function reduce ( key, values ) { var result = { count : 1, speedSum : 0 }; values.forEach( function( v ){ result.speedSum += v.speed; result.count++; }); return result; }
- 34. 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 } }
- 35. 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
- 36. Benefits of the Hadoop Connector • Increased parallelism • Access to analytics libraries • Separation of concerns • Integrates with existing tool chains
- 37. • 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
- 38. 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 ] } }
- 39. { _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
- 40. { _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
- 41. 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
- 42. Putting it all together
- 43. 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
- 44. 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
- 45. 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
- 47. Consulting Engineer, MongoDB Inc. Bryan Reinero #MongoDBWorld Thank You
Editor's Notes
- #3: Reports (group, summing, averaging) Analytics(incremental reporting, rollups) Analysis (trends, segmentation, anomalies) Analytics (regression, forecasting, filtering) Warehousing (long term storage and simplified querying)
- #7: Compound unique index on linkId & Interval update field used to identify new documents for aggregation
- #8: Compound unique index on linkId & Interval update field used to identify new documents for aggregation
- #9: Compound unique index on linkId & Interval update field used to identify new documents for aggregation
- #10: Compound unique index on linkId & Interval update field used to identify new documents for aggregation
- #11: Compound unique index on linkId & Interval update field used to identify new documents for aggregation
- #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: Compound unique index on linkId & Interval update field used to identify new documents for aggregation
- #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: 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
- #20: 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
- #21: 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
- #39: 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
- #40: 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
- #41: 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
- #42: 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
- #44: Reports (group, summing, averaging) Analytics(incremental reporting, rollups) Analysis (trends, segmentation, anomalies) Analytics (regression, forecasting, filtering) Warehousing (long term storage and simplified querying)
- #45: Reports (group, summing, averaging) Analytics(incremental reporting, rollups) Analysis (trends, segmentation, anomalies) Analytics (regression, forecasting, filtering) Warehousing (long term storage and simplified querying)
- #46: Reports (group, summing, averaging) Analytics(incremental reporting, rollups) Analysis (trends, segmentation, anomalies) Analytics (regression, forecasting, filtering) Warehousing (long term storage and simplified querying)