SlideShare ist ein Scribd-Unternehmen logo
1 von 134
Downloaden Sie, um offline zu lesen
@
#MDBlocal
How MongoDB 4.2 Pipeline
Powers Queries, Updates and Views
Asya Kamsky, Principal MongoDB Knowitall
asya999
AGGREGATION POWER++
SAN FRANCISCO
PREVIOUSLY ...
... 2017
#MDBW17
Analytics with MongoDB Aggregation Framework
@asya999 by Asya Kamsky,
Lead MongoDB Maven
PIPELINE POWER
STORE
RETRIEVE
#MDBLocal
ps ax | grep mongod | head 1
*nix command line pipe
PIPELINE
#MDBLocal
$match $group | $sort|
Input stream {} {} {} {} Result {} {} ...
PIPELINE
MongoDB document pipeline
DATA PIPELINE
STAGES
Stage 1 Stage 2 Stage 3 Stage 4
{} {} {} {}
{} {} {} {}
DATA PIPELINE
{} {} {} {}
{"$stage":{ ... }}
START
Collection
View
Special stage
STAGES
{title: "The Great Gatsby",
language: "English",
subjects: "Long Island"}
{title: "The Great Gatsby",
language: "English",
subjects: "New York"}
{title: "The Great Gatsby",
language: "English",
subjects: "1920s"}
{title: "The Great Gatsby",
language: "English",
subjects: [
"Long Island",
"New York",
"1920s"] },
{"$match":{"language":"English"}}
$match
{ _id:"Long Island",
count: 1 },
$group
{ _id: "New York",
count: 2 },
$unwind
{ _id: "1920s",
count: 1 },
$sort $skip$limit $project
{"$unwind":"$subjects"}
{"$group":{"_id":"$subjects", "count":{"$sum:1}}
{ _id: "Harlem",
count: 1 },
{ _id: "Long Island",
count: 1 },
{ _id: "New York",
count: 2 },
{ _id: "1920s",
count: 1 },
{title: "Open City",
language: "English",
subjects: [
"New York"
"Harlem" ] }
{ title: "The Great Gatsby",
language: "English",
subjects: [
"Long Island",
"New York",
"1920s"] },
{ title: "War and Peace",
language: "Russian",
subjects: [
"Russia",
"War of 1812",
"Napoleon"] },
{ title: "Open City",
language: "English",
subjects: [
"New York",
"Harlem" ] },
{title: "Open City",
language: "English",
subjects: "New York"}
{title: "Open City",
language: "English",
subjects: "Harlem"}
{ _id: "Harlem",
count: 1 },
{"$sort:{"count":-1} {"$limit":3}
{"$project":...}
$cursor
{title: "The Great Gatsby",
language: "English".
subjects: "Long Island"}
{title: "The Great Gatsby",
language: "English",
subjects: "New York"}
{title: "The Great Gatsby",
language: "English",
subjects: "1920s"}
{title: "The Great Gatsby",
language: "English",
subjects: [
"Long Island",
"New York",
"1920s"] },
{"$match":{"language":"English"}}
$match
{ _id:"Long Island",
count: 1 },
$group
{ _id: "New York",
count: 2 },
$unwind
{ _id: "1920s",
count: 1 },
$sort $skip$limit $project
{"$unwind":"$subjects"}
{"$group":{"_id":"$subjects", "count":{"$sum:1}}
{ _id: "Harlem",
count: 1 },
{ _id: "Long Island",
count: 1 },
{ _id: "New York",
count: 2 },
{ _id: "1920s",
count: 1 },
{title: "Open City",
language: "English",
subjects: [
"New York"
"Harlem" ] }
{ title: "The Great Gatsby",
language: "English",
subjects: [
"Long Island",
"New York",
"1920s"] },
{ title: "War and Peace",
language: "Russian",
subjects: [
"Russia",
"War of 1812",
"Napoleon"] },
{ title: "Open City",
language: "English",
subjects: [
"New York",
"Harlem" ] },
{title: "Open City",
language: "English",
subjects: "New York"}
{title: "Open City",
language: "English",
subjects: "Harlem"}
{ _id: "Harlem",
count: 1 },
{"$sort:{"count":-1} {"$limit":3}
{"$project":...}
$group $sort
1
#MDBLocal
INPUT STAGE RESULTSSTAGE
Each document is streamed through in RAM
STREAMING RESOURCE USE
#MDBLocal
INPUT STAGE RESULTSSTAGE
BLOCKING RESOURCE USE
Everything has to be kept in RAM (or spill)
$sort$match $group
start=ISODate("...")
end=ISODate("...")
{
user: "303900",
ipaddr: "71.56.112.56",
ts:ISODate("2017-05-08T...")
}
{$match:{ts:{$gte:start,$lt:end}}},
{$sort:{ts:1}},
{$group:{_id:"$user",ips:{$push:{ip:"$ipaddr", ts:"$ts"}},
diffIps:{$addToSet:"$ipaddr"}}},
{$match:{"diffIps.1":{$exists:true}}},
{$addFields:{diffs: {$filter:{
input:{$map:{
input: {$range:[0,{$subtract:[{$size:"$ips"},1]}]}, as:"i",
in:{$let:{vars:{ip1:{$arrayElemAt:["$ips","$$i"]},
ip2:{$arrayElemAt:["$ips",{$add:["$$i",1]}]}},
in:{
diff:{$cond:{
if:{$ne:["$$ip1.ip","$$ip2.ip"]},
then:{$divide:[{$subtract:["$$ip2.ts","$$ip1.ts"]},60000]},
else: 9999 }},
ip1:"$$ip1.ip", t1:"$$ip1.ts",
ip2:"$$ip2.ip", t2:"$$ip2.ts"
}}}}},
cond:{$lt:["$$this.diff",10]}
}}}},
{$match:{"diffs":{$ne:[]}}},
{$project:{_id:0, user:"$_id", suspectLogins:"$diffs"}}
{ "user" : "35237073",
"suspectLogins" : [
{"diff": 4.8333333333,
"ip1": "106.220.151.16",
"t1":"2017-05-08T06:58",
"ip2": "223.182.113.15"
"t2":"2017-05-08T07:03"
},
{"diff": 8.3,
"ip1": "223.182.113.15",
"t1":"2017-05-08T07:03",
"ip2": "49.206.217.26",
"t2":"2017-05-08T07:11"
}
]
} $match $addFields $match $project
https://github.com/asya999/mdbw17
5 minute review
https://github.com/asya999/mdbw17
#MDBW17
Analytics with MongoDB Aggregation Framework
@asya999 by Asya Kamsky,
Lead MongoDB Maven
PIPELINE POWER
https://github.com/asya999/mdbw17
PREVIOUSLY ...
... 2017
PREVIOUSLY ...
... 2017 ...
PREVIOUSLY ...
... 2017 ... 2018
#MDBLocal
THE FUTURE OF AGGREGATION
Better performance & optimizations
More stages & expressions
More options for output
Compass helper for aggregate
Unify different languages
#MDBLocal
THE FUTURE OF AGGREGATION
Better performance & optimizations
More stages & expressions
More options for output
Compass helper for aggregate
Unify different languages
#MDBLocal
THE FUTURE OF AGGREGATION
Better performance & optimizations
More stages & expressions
More options for output
Compass helper for aggregate
Unify different languages
#MDBLocal
THE FUTURE OF AGGREGATION
More options for output
Unify different languages
#MDBLocal
THE PRESENT OF AGGREGATION
More options for output
Unify different languages
#MDBLocal
Unify Different Languages
#MDBLocal
Unify Different Languages
{children: [
{name:"Max", dob:"1994-12-01", dep:true},
{name:"Sam", dob:"1997-09-28", dep:true},
{name:"Kim", dob:"2000-02-29", dep:true}
]}
AGGREGATION
#MDBLocal
Unify Different Languages
{children: [
{name:"Max", dob:"1994-12-01", dep:true},
{name:"Sam", dob:"1997-09-28", dep:true},
{name:"Kim", dob:"2000-02-29", dep:true}
]}
AGGREGATION
db.c.aggregate([
{$addFields:{
numChildren:{$size:"$children"},
numDependents:{$size:{
$filter:{
input:"$children.dep",
cond: "$$this"
}
}}
}},
...
])
#MDBLocal
Unify Different Languages
{children: [
{name:"Max", dob:"1994-12-01", dep:true},
{name:"Sam", dob:"1997-09-28", dep:true},
{name:"Kim", dob:"2000-02-29", dep:true}
]}
AGGREGATION
FIND
db.c.aggregate([
{$addFields:{
numChildren:{$size:"$children"},
numDependents:{$size:{
$filter:{
input:"$children.dep",
cond: "$$this"
}
}}
}},
...
])
#MDBLocal
Unify Different Languages
{children: [
{name:"Max", dob:"1994-12-01", dep:true},
{name:"Sam", dob:"1997-09-28", dep:true},
{name:"Kim", dob:"2000-02-29", dep:true}
]}
AGGREGATION
FIND
db.c.find (
{$expr:{
$lt:[
{$size:{$filter:{
input: "$children.dep",
cond: "$$this"
}}},
2
]
}}
)
#MDBLocal
Unify Different Languages
{children: [
{name:"Max", dob:"1994-12-01", dep:true},
{name:"Sam", dob:"1997-09-28", dep:true},
{name:"Kim", dob:"2000-02-29", dep:true}
]}
AGGREGATION
FIND
UPDATE
db.c.find (
{$expr:{
$lt:[
{$size:{$filter:{
input: "$children.dep",
cond: "$$this"
}}},
2
]
}}
)
#MDBLocal
Unify Different Languages
{children: [
{name:"Max", dob:"1994-12-01", dep:true},
{name:"Sam", dob:"1997-09-28", dep:true},
{name:"Kim", dob:"2000-02-29", dep:true}
]}
AGGREGATION
FIND
UPDATE
db.c.update(
{$expr:{
$anyElementTrue:{$map:{
input:"$children",
in: {$and:[
{$lt:["$$this.dob","1997-01-22"]},
"$$this.dep"
]}
}}
}},
{$set:{ audit:true }}
)
#MDBLocal
Update
db.coll.update(
<query>,
<update>,
<options>
)
#MDBLocal
Update
db.coll.update(
<query>,
<update>,
<options>
)
#MDBLocal
Update
db.coll.update(
<query>,
<update>,
<options>
)
<update>
#MDBLocal
Update
{
f1: <value>,
f2: <value>,
...
}
{
$set: { },
$inc: { },
$...
}
<update>
#MDBLocal
Update in 4.2
{ } OR [ ]
<update>
#MDBLocal
Update in 4.2
{ <same> } [ ]
<update>
#MDBLocal
Update in 4.2
{ <same> } [ <aggregation-pipeline> ]
<update>
Updates Using Aggregation
Pipeline
#MDBLocal
{ $addFields: { } }
{ $project: { } }
{ $replaceRoot: { } }
{ $set: { } }
{ $unset: [ ] }
{ $replaceWith: { } }
#MDBLocal
db.coll.update({_id:1},
{$inc:{a:1}},
{upsert:true})
{ _id: 1 }
{ _id: 1, a: 10 }
{ _id: 1, a: 100 }
---
{ _id: 1, a: "10" }
{ _id: 1, a: 1 }
{ _id: 1, a: 11 }
{ _id: 1, a: 101 }
{ _id: 1, a: 1 }
"errmsg" : "Cannot apply to a value of
non-numeric type."
#MDBLocal
{ _id: 1 }
{ _id: 1, a: 10 }
{ _id: 1, a: 100 }
---
{ _id: 1, a: "10" }
{ _id: 1, a: 1 }
{ _id: 1, a: 11 }
{ _id: 1, a: 101 }
{ _id: 1, a: 1 }
{ _id: 1, a: 1 }
db.coll.update({_id:1},
[ {$set:{a:{$sum:["$a",1]}}} ],
{upsert:true})
#MDBLocal
{ _id: 1 }
{ _id: 1, a: 10 }
{ _id: 1, a: 100 }
---
{ _id: 1, a: "10" }
{ _id: 1, a: 1 }
{ _id: 1, a: 11 }
{ _id: 1, a: 101 }
{ _id: 1, a: 1 }
"errmsg" : "$add only supports
numeric or date types, not string"
db.coll.update({_id:1},
[ {$set:{a:{$add:["$a",1]}}} ],
{upsert:true})
#MDBLocal
{ _id: 1 }
{ _id: 1, a: 10 }
{ _id: 1, a: 100 }
---
{ _id: 1, a: "10" }
{ _id:1, a: 21 }
{ _id: 1, a: 11 }
{ _id: 1, a: 101 }
{ _id:1, a: 21 }
db.coll.update({_id:1},
[ {$set:{a:{$ }} ],
{upsert:true})
#MDBLocal
{ _id: 1 }
{ _id: 1, a: 10 }
{ _id: 1, a: 100 }
---
{ _id: 1, a: "10" }
{ _id:1, a: 21 }
{ _id: 1, a: 11 }
{ _id: 1, a: 101 }
{ _id:1, a: 21 }
db.coll.update({_id:1}, [ {$set:{a:{$cond:{
if: ,
then: , else: }}}}], {upsert:true})
#MDBLocal
{ _id: 1 }
{ _id: 1, a: 10 }
{ _id: 1, a: 100 }
---
{ _id: 1, a: "10" }
{ _id:1, a: 21 }
{ _id: 1, a: 11 }
{ _id: 1, a: 101 }
{ _id:1, a: 21 }
db.coll.update({_id:1}, [ {$set:{a:{$cond:{
if: {$eq:[{$type:"$a"},"missing"]},
then: , else: }}}}], {upsert:true})
#MDBLocal
{ _id: 1 }
{ _id: 1, a: 10 }
{ _id: 1, a: 100 }
---
{ _id: 1, a: "10" }
{ _id:1, a: 21 }
{ _id: 1, a: 11 }
{ _id: 1, a: 101 }
{ _id:1, a: 21 }
db.coll.update({_id:1}, [ {$set:{a:{$cond:{
if: {$eq:[{$type:"$a"},"missing"]},
then: 21, else: {$sum:["$a",1]} }}}}], {upsert:true})
#MDBLocal
{ _id: 1 }
{ _id: 1, a: 10 }
{ _id: 1, a: 100 }
---
{ _id: 1, a: "10" }
{ _id:1, a: 21 }
{ _id: 1, a: 11 }
{ _id: 1, a: 100 }
{ _id:1, a: 21 }
db.coll.update({_id:1}, [ {$set:{a:{$cond:{
if: {$eq:[{$type:"$a"},"missing"]},
then: 21, else: {$sum:["$a",1]} }}}}], {upsert:true})
#MDBLocal
{ _id: 1 }
{ _id: 1, a: 10 }
{ _id: 1, a: 100 }
---
{ _id: 1, a: "10" }
{ _id:1, a: 21 }
{ _id: 1, a: 11 }
{ _id: 1, a: 100 }
{ _id:1, a: 21 }
db.coll.update({_id:1}, [ {$set:{a:{$min:[100, {$cond:{
if: {$eq:[{$type:"$a"},"missing"]},
then: 21, else: {$sum:["$a",1]} }}]} }}], {upsert:true})
#MDBLocal
{ _id: 1 }
{ _id: 1, a: 10 }
{ _id: 1, a: 100 }
---
{ _id: 1, a: "10" }
{ _id:1, a: 21 }
{ _id: 1, a: 11 }
{ _id: 1, a: 100 }
{ _id:1, a: 21 }
{ _id:1, a: 1 }
db.coll.update({_id:1}, [ {$set:{a:{$min:[100, {$cond:{
if: {$eq:[{$type:"$a"},"missing"]},
then: 21, else: {$sum:["$a",1]} }}]} }}], {upsert:true})
#MDBLocal
{ _id: 1 }
{ _id: 1, a: 10 }
{ _id: 1, a: 100 }
---
{ _id: 1, a: "10" }
{ _id:1, a: 21 }
{ _id: 1, a: 11 }
{ _id: 1, a: 100 }
{ _id:1, a: 21 }
{ _id:1, a: 1 }
db.coll.update({_id:1}, [ {$set:{a:{$min:[100, {$cond:{
if: {$eq:[{$type:"$a"},"missing"]},
then: 21, else: {$sum:["$a",1]} }}]}, prev_a:"$a" }}],
{upsert:true})
#MDBLocal
{ _id: 1 }
{ _id: 1, a: 10 }
{ _id: 1, a: 100 }
---
{ _id: 1, a: "10" }
{ _id:1, a: 21 }
{ _id: 1, a: 11, prev_a: 10 }
{ _id: 1, a: 100, prev_a: 100 }
{ _id:1, a: 21 }
{ _id:1, a: 1, prev_a: "10" }
db.coll.update({_id:1}, [ {$set:{a:{$min:[100, {$cond:{
if: {$eq:[{$type:"$a"},"missing"]},
then: 21, else: {$sum:["$a",1]} }}]}, prev_a:"$a" }}],
{upsert:true})
#MDBLocal
Set Defaults
#MDBLocal
Set Defaults
{_id: 1, a: 5, b: 12}
{_id: 2, a: 15, c: "abc"}
{_id: 3, b: 99, c: "xyz"}
If a or b are missing, set to 0, if c is missing -> "unset"
#MDBLocal
Set Defaults
{_id: 1, a: 5, b: 12}
{_id: 2, a: 15, c: "abc"}
{_id: 3, b: 99, c: "xyz"}
If a or b are missing, set to 0, if c is missing -> "unset"
db.coll.update({}, [
{$replaceWith:{
}}
], {multi:true})
#MDBLocal
Set Defaults
{_id: 1, a: 5, b: 12}
{_id: 2, a: 15, c: "abc"}
{_id: 3, b: 99, c: "xyz"}
If a or b are missing, set to 0, if c is missing -> "unset"
db.coll.update({}, [
{$replaceWith:{$mergeObjects:[
]}}
], {multi:true})
#MDBLocal
Set Defaults
{_id: 1, a: 5, b: 12}
{_id: 2, a: 15, c: "abc"}
{_id: 3, b: 99, c: "xyz"}
If a or b are missing, set to 0, if c is missing -> "unset"
db.coll.update({}, [
{$replaceWith:{$mergeObjects:[
{ a:0, b:0, c:"unset" },
"$$ROOT"
]}}
], {multi:true})
#MDBLocal
Set Defaults
{_id: 1, a: 5, b: 12}
{_id: 2, a: 15, c: "abc"}
{_id: 3, b: 99, c: "xyz"}
If a or b are missing, set to 0, if c is missing -> "unset"
db.coll.update({}, [
{$replaceWith:{$mergeObjects:[
{ a:0, b:0, c:"unset" },
"$$ROOT"
]}}
], {multi:true})
{_id: 1, a: 5, b: 12, c: "unset"}
{_id: 2, a: 15, b: 0, c: "abc"}
{_id: 3, a: 0, b: 99, c: "xyz"}
#MDBLocal
{ id: 1,
d: ISODate("2019-06-04T00:00:00"),
h: [
{ hour:"11", value: 296 },
{ hour:"12", value: 300 }
]}
id: X, d:Y, hour:Z, value: VAL
db.coll.update({id:X, d:Y},
[ {$set:{h:{$cond:{
if:
then:
else:
}}}}],
{upsert:true})
#MDBLocal
{ id: 1,
d: ISODate("2019-06-04T00:00:00"),
h: [
{ hour:"11", value: 296 },
{ hour:"12", value: 300 }
]}
id: X, d:Y, hour:Z, value: VAL
db.coll.update({id:X, d:Y},
[ {$set:{h:{$cond:{
if: {$in:[Z,"$h.hour"]},
then:{$map:{
input:"$h",
in: {$cond:{ if:{$ne:["$$this.hour",Z]}, then:"$$this",
else: {hour: Z, value: {$sum:[ "$$this.value", VAL]}}
}}}},
else:{$concatArrays:["$h",[{hour:Z,value:VAL}]]}
}}}}],
{upsert:true})
if:
then:
else:
#MDBLocal
if:
then:
else:
{ id: 1,
d: ISODate("2019-06-04T00:00:00"),
h: [
{ hour:"11", value: 296 },
{ hour:"12", value: 300 }
]}
id: X, d:Y, hour:Z, value: VAL
db.coll.update({id:X, d:Y}, [ {$set: {h:{$ifNull:["$h", [] ]}} },
{$set:{h:{$cond:{
if: {$in:[Z,"$h.hour"]},
then:{$map:{
input:"$h",
in: {$cond:{ if:{$ne:["$$this.hour",Z]}, then:"$$this",
else: {hour: Z, value: {$sum:[ "$$this.value", VAL]}}
}}}},
else:{$concatArrays:["$h",[{hour:Z,value:VAL}]]}
}}}}],
{upsert:true})
#MDBLocal
Recap:
Updates can be specified with aggregation pipeline
All fields from existing document can be accessed
Slightly slower, but a lot more powerful
#MDBLocal
THE FUTURE OF AGGREGATION
Better performance & optimizations
More stages & expressions
More options for output
Compass helper for aggregate
Unify different languages
#MDBLocal
THE FUTURE OF AGGREGATION
Better performance & optimizations
More stages & expressions
More options for output
Compass helper for aggregate
Unify different languages
#MDBLocal
THE FUTURE OF AGGREGATION
More options for output
#MDBLocal
More Options for Output
#MDBLocal
Prior to MongoDB 4.2
$out
coll
new_coll
$out
#MDBLocal
Prior to MongoDB 4.2
$out
coll
new_coll
$out
db.coll.aggregate( [ {pipeline}, ...
{$out: "new_coll"} ]);
#MDBLocal
Prior to MongoDB 4.2
$out
coll
new_coll
$out
db.coll.aggregate( [ {pipeline}, ...
{$out: "new_coll"} ]);
new_coll
○ must be unsharded
○ overwrites existing
New $merge stage
in aggregation pipeline
#MDBLocal
MongoDB 4.2
$merge
coll
coll2
$merge
#MDBLocal
MongoDB 4.2
$merge
db.coll.aggregate( [
{pipeline}, ...,
{$merge: { ... }
]);
coll
coll2
$merge
#MDBLocal
MongoDB 4.2
$merge
db.coll.aggregate( [
{pipeline}, ...,
{$merge: { ... }
]);
coll2
can exist
same or different 'db'
can be sharded
coll
coll2
$merge
#MDBLocal
coll
coll2
$merge
{ } { } { } { }
{ } { } { } { }
MongoDB 4.2
#MDBLocal
{
$merge: {
into: <target>
}
}
$merge syntax
#MDBLocal
{$merge: "collection2"}
$merge syntax
{
$merge: {
into: <target>
}
}
#MDBLocal
{$merge: {into: {db: "db2", coll: "collection2"}}
$merge syntax
{
$merge: {
into: <target>
}
}
#MDBLocal
{
$merge: {
into: <target>
}
}
$merge syntax
#MDBLocal
{
$merge: {
into: <target>,
on: <fields>
}
}
on: "_id"
on: [ "_id", "shardkey(s)" ]
must be unique
$merge syntax
#MDBLocal
{
$merge: {
into: <target>,
on: <fields>
}
}
$merge syntax
#MDBLocal
Actions
source target
#MDBLocal
Actions
nothing matched:
source target
#MDBLocal
Actions
nothing matched: usually insert
source target
#MDBLocal
Actions
nothing matched: usually insert
document matched:
source target
#MDBLocal
Actions
nothing matched: usually insert
document matched: overwrite? update? ???
source target
#MDBLocal
Actions
nothing matched: usually insert
document matched: update
source target
#MDBLocal
Actions
nothing matched: usually insert
document matched: update (merge)
source target
#MDBLocal
$merge syntax
{
$merge: {
into: <target>,
whenNotMatched:
whenMatched:
}
}
#MDBLocal
$merge syntax
{
$merge: {
into: <target>,
whenNotMatched:"insert",
whenMatched:
}
}
#MDBLocal
$merge syntax
{
$merge: {
into: <target>,
whenNotMatched:"insert",
whenMatched:"merge"
}
}
#MDBLocal
$merge syntax
{
$merge: {
into: <target>,
whenNotMatched:"insert"|"discard"|"fail",
whenMatched:"merge"
}
}
#MDBLocal
$merge syntax
{
$merge: {
into: <target>,
whenNotMatched:"insert"|"discard"|"fail",
whenMatched:"merge"|"replace"|"keepExisting"|"fail"|[...]
}
}
#MDBLocal
$merge syntax
{
$merge: {
into: <target>,
whenMatched:[...]
}
}
#MDBLocal
$merge syntax
{
$merge: {
into: <target>,
whenMatched:[<custom pipeline>]
}
}
#MDBLocal
$merge example
{
$merge: {
into: <target>,
whenMatched:[
{$addFields:{
}}
]
}
}
#MDBLocal
$merge example
{
$merge: {
into: <target>,
whenMatched:[
{$addFields:{
total:{$sum:["$total","$$new.total"]}
}}
]
}
}
#MDBLocal
$merge example
{
$merge: {
into: <target>,
whenMatched:[
{$set:{
total:{$sum:["$total","$$new.total"]}
}}
]
}
}
#MDBLocal
$merge example
{
$merge: {
into: <target>,
whenMatched:[
{$set:{
total:{$sum:["$total","$$new.total"]}
}}
]
}
}
#MDBLocal
$merge example
{
$merge: {
into: <target>,
whenMatched:[
{$set:{
total:{$sum:["$total","$$new.total"]}
}}
]
}
}
Incoming Target
{
_id: "37",
total: 64,
f1: "x"
}
{
_id: "37",
total: 245,
f1: "yyy"
}
Result:
{
}
#MDBLocal
$merge example
{
$merge: {
into: <target>,
whenMatched:[
{$set:{
total:{$sum:["$total","$$new.total"]}
}}
]
}
}
Incoming Target
{
_id: "37",
total: 64,
f1: "x"
}
{
_id: "37",
total: 245,
f1: "yyy"
}
Result:
{
_id: "37",
total: 309,
f1: "yyy"
}
#MDBLocal
$merge example 2
{
$merge: {
into: <target>,
whenMatched:[
{$replaceWith:{$mergeObjects:[
"$$new",
{total:{$sum:["$$new.total", "$total"]}}
]}}
]
}
}
#MDBLocal
$merge example 2
{
$merge: {
into: <target>,
whenMatched:[
{$replaceWith:{$mergeObjects:[
"$$new",
{total:{$sum:["$$new.total", "$total"]}}
]}}
]
}
}
Incoming Target
{
_id: "37",
total: 64,
f1: "x"
}
{
_id: "37",
total: 245,
f1: "yyy"
}
Result:
{
}
#MDBLocal
$merge example 2
{
$merge: {
into: <target>,
whenMatched:[
{$replaceWith:{$mergeObjects:[
"$$new",
{total:{$sum:["$$new.total", "$total"]}}
]}}
]
}
}
Incoming Target
{
_id: "37",
total: 64,
f1: "x"
}
{
_id: "37",
total: 245,
f1: "yyy"
}
Result:
{
_id: "37",
total: 309,
f1: "x"
}
#MDBLocal
$merge syntax
{
$merge: {
into: <target>,
whenMatched:[...]
}
}
#MDBLocal
$merge syntax
{
$merge: {
into: <target>,
let: { ... },
whenMatched:[ ...]
}
}
#MDBLocal
$merge syntax
{
$merge: {
into: <target>,
let: {new: "$$ROOT"},
whenMatched:[ ...]
}
}
EXAMPLES
APPEND from TEMP collection
#MDBLocal
temp
real
data
real
Using $merge to append loaded and
cleansed records loaded into db
#MDBLocal
aggregate 'temp' and append valid records to 'data'
db.temp.aggregate( [
{ ... } /* pipeline to massage and cleanse data in temp */,
{$merge:{
into: "data",
whenMatched: "fail"
}}
]);
#MDBLocal
aggregate 'temp' and append valid records to 'data'
db.temp.aggregate( [
{ ... } /* pipeline to massage and cleanse data in temp */,
{$merge:{
into: "data",
whenMatched: "fail"
}}
]);
Similar to SQL's INSERT INTO T1 SELECT * from T2
EXAMPLES
Maintain Single View
#MDBLocal
mflix
users
users
mfriendbook
users
sv
Using $merge to populate/update
user fields from other services
#MDBLocal
mflix
users
users
mfriendbook
users
sv
Using $merge to populate/update
user fields from other services
sv.users
{
_id: "user253",
dob: ISODate(...),
f1: "yyy"
}
#MDBLocal
$merge updates fields from mflix.users collection into
sv.users collection. Our "_id" field is unique username
mflix_pipeline = [
{ "$project" : {
"_id" : "$username",
"mflix" : "$$ROOT"
}},
{ "$merge" : {
"into" : {
"db": "sv",
"collection" : "users"
},
"whenNotMatched" : "discard"
}}
]
(in mflix)
sv.users
{
_id: "user253",
dob: ISODate(...),
f1: "yyy"
}
#MDBLocal
$merge updates fields from mflix.users collection into
sv.users collection. Our "_id" field is unique username
mflix_pipeline = [
{ "$project" : {
"_id" : "$username",
"mflix" : "$$ROOT"
}},
{ "$merge" : {
"into" : {
"db": "sv",
"collection" : "users"
},
"whenNotMatched" : "discard"
}}
]
(in mflix) db.users.aggregate(mflix_pipeline)
sv.users
{
_id: "user253",
dob: ISODate(...),
f1: "yyy",
mflix: { ... }
}
#MDBLocal
$merge updates fields from mfriendbook.users collection into
sv.users collection. Our "_id" field is unique username
mfriendbook_pipeline = [
{ "$project" : {
"_id" : "$username",
"mfriendbook" : "$$ROOT"
}},
{ "$merge" : {
"into" : {
"db": "sv",
"collection" : "users"
},
"whenNotMatched" : "discard"
}}
]
(in mfriendbook)
sv.users
{
_id: "user253",
dob: ISODate(...),
f1: "yyy",
mflix: { ... }
}
#MDBLocal
$merge updates fields from mfriendbook.users collection into
sv.users collection. Our "_id" field is unique username
mfriendbook_pipeline = [
{ "$project" : {
"_id" : "$username",
"mfriendbook" : "$$ROOT"
}},
{ "$merge" : {
"into" : {
"db": "sv",
"collection" : "users"
},
"whenNotMatched" : "discard"
}}
]
(in mfriendbook) db.users.aggregate(mfriendbook_pipeline)
sv.users
{
_id: "user253",
dob: ISODate(...),
f1: "yyy",
mflix: { ... },
mfriendbook: { ... }
}
EXAMPLES
Populate ROLLUPS into summary table
registrations
real
regsummary
real
Using $merge to incrementally
update periodic rollups in summary
#MDBLocal
$merge to create/update periodic
rollups in summary collection (for all days)
db.regsummary.createIndex({event:1, date:1}, {unique: true});
#MDBLocal
$merge to create/update periodic
rollups in summary collection (for all days)
db.regsummary.createIndex({event:1, date:1}, {unique: true});
db.registrations.aggregate([
{$match: {event_id: "MDBW19"}},
{$group:{
_id:{$dateToString:{date:"$date",format:"%Y-%m-%d"}},
count: {$sum:1}
}},
{$project: {_id:0,event:"MDBW19",date:"$_id",total:"$count"}},
{$merge: {
into: "regsummary",
on: ["event", "date"]
}}
])
#MDBLocal
$merge to create/update periodic
rollups in summary collection (for all days)
db.regsummary.createIndex({event:1, date:1}, {unique: true});
db.registrations.aggregate([
{$match: {event_id: "MDBW19"}},
{$group:{
_id:{$dateToString:{date:"$date",format:"%Y-%m-%d"}},
count: {$sum:1}
}},
{$project: {_id:0,event:"MDBW19",date:"$_id",total:"$count"}},
{$merge: {
into: "regsummary",
on: ["event", "date"]
}}
])
{ "event" : "MDBW19", "date" : "2019-05-19", "total" : 33 }
{ "event" : "MDBW19", "date" : "2019-05-20", "total" : 15 }
{ "event" : "MDBW19", "date" : "2019-05-21", "total" : 24 }
#MDBLocal
$merge to incrementally update periodic rollups in
summary collection (for single day)
#MDBLocal
$merge to incrementally update periodic rollups in
summary collection (for single day)
db.registrations.aggregate([
{$match: {
event_id: "MDBW19",
date:{$gte:ISODate("2019-05-22"),$lt:ISODate("2019-05-23")}
}},
{$count: "total"},
{$addFields: {event:"MDBW19", "date":"2019-05-22"}},
{$merge: {
into: "regsummary",
on: ["event", "date"]
}}
])
#MDBLocal
$merge to incrementally update periodic rollups in
summary collection (for single day)
db.registrations.aggregate([
{$match: {
event_id: "MDBW19",
date:{$gte:ISODate("2019-05-22"),$lt:ISODate("2019-05-23")}
}},
{$count: "total"},
{$addFields: {event:"MDBW19", "date":"2019-05-22"}},
{$merge: {
into: "regsummary",
on: ["event", "date"]
}}
])
{ "event" : "MDBW19", "date" : "2019-05-19", "total" : 33 }
{ "event" : "MDBW19", "date" : "2019-05-20", "total" : 15 }
{ "event" : "MDBW19", "date" : "2019-05-21", "total" : 24 }
{ "event" : "MDBW19", "date" : "2019-05-22", "total" : 34 }
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++

Weitere ähnliche Inhalte

Was ist angesagt?

Aggregation Framework
Aggregation FrameworkAggregation Framework
Aggregation FrameworkMongoDB
 
How to leverage what's new in MongoDB 3.6
How to leverage what's new in MongoDB 3.6How to leverage what's new in MongoDB 3.6
How to leverage what's new in MongoDB 3.6Maxime Beugnet
 
MongoDB .local Paris 2020: La puissance du Pipeline d'Agrégation de MongoDB
MongoDB .local Paris 2020: La puissance du Pipeline d'Agrégation de MongoDBMongoDB .local Paris 2020: La puissance du Pipeline d'Agrégation de MongoDB
MongoDB .local Paris 2020: La puissance du Pipeline d'Agrégation de MongoDBMongoDB
 
Aggregation in MongoDB
Aggregation in MongoDBAggregation in MongoDB
Aggregation in MongoDBKishor Parkhe
 
MongoDB Aggregation Framework
MongoDB Aggregation FrameworkMongoDB Aggregation Framework
MongoDB Aggregation FrameworkCaserta
 
MongoDB Aggregation Framework
MongoDB Aggregation FrameworkMongoDB Aggregation Framework
MongoDB Aggregation FrameworkTyler Brock
 
MongoDB Europe 2016 - Advanced MongoDB Aggregation Pipelines
MongoDB Europe 2016 - Advanced MongoDB Aggregation PipelinesMongoDB Europe 2016 - Advanced MongoDB Aggregation Pipelines
MongoDB Europe 2016 - Advanced MongoDB Aggregation PipelinesMongoDB
 
MongoDB Europe 2016 - ETL for Pros – Getting Data Into MongoDB The Right Way
MongoDB Europe 2016 - ETL for Pros – Getting Data Into MongoDB The Right WayMongoDB Europe 2016 - ETL for Pros – Getting Data Into MongoDB The Right Way
MongoDB Europe 2016 - ETL for Pros – Getting Data Into MongoDB The Right WayMongoDB
 
Webinar: Exploring the Aggregation Framework
Webinar: Exploring the Aggregation FrameworkWebinar: Exploring the Aggregation Framework
Webinar: Exploring the Aggregation FrameworkMongoDB
 
The Aggregation Framework
The Aggregation FrameworkThe Aggregation Framework
The Aggregation FrameworkMongoDB
 
Agg framework selectgroup feb2015 v2
Agg framework selectgroup feb2015 v2Agg framework selectgroup feb2015 v2
Agg framework selectgroup feb2015 v2MongoDB
 
MongoDB World 2016 : Advanced Aggregation
MongoDB World 2016 : Advanced AggregationMongoDB World 2016 : Advanced Aggregation
MongoDB World 2016 : Advanced AggregationJoe Drumgoole
 
Powerful Analysis with the Aggregation Pipeline
Powerful Analysis with the Aggregation PipelinePowerful Analysis with the Aggregation Pipeline
Powerful Analysis with the Aggregation PipelineMongoDB
 
Aggregation Framework MongoDB Days Munich
Aggregation Framework MongoDB Days MunichAggregation Framework MongoDB Days Munich
Aggregation Framework MongoDB Days MunichNorberto Leite
 
The Aggregation Framework
The Aggregation FrameworkThe Aggregation Framework
The Aggregation FrameworkMongoDB
 
Embedding a language into string interpolator
Embedding a language into string interpolatorEmbedding a language into string interpolator
Embedding a language into string interpolatorMichael Limansky
 
Mongodb Aggregation Pipeline
Mongodb Aggregation PipelineMongodb Aggregation Pipeline
Mongodb Aggregation Pipelinezahid-mian
 
MongoDB Aggregation
MongoDB Aggregation MongoDB Aggregation
MongoDB Aggregation Amit Ghosh
 
MongoDB .local Toronto 2019: Tips and Tricks for Effective Indexing
MongoDB .local Toronto 2019: Tips and Tricks for Effective IndexingMongoDB .local Toronto 2019: Tips and Tricks for Effective Indexing
MongoDB .local Toronto 2019: Tips and Tricks for Effective IndexingMongoDB
 
Aggregation Framework in MongoDB Overview Part-1
Aggregation Framework in MongoDB Overview Part-1Aggregation Framework in MongoDB Overview Part-1
Aggregation Framework in MongoDB Overview Part-1Anuj Jain
 

Was ist angesagt? (20)

Aggregation Framework
Aggregation FrameworkAggregation Framework
Aggregation Framework
 
How to leverage what's new in MongoDB 3.6
How to leverage what's new in MongoDB 3.6How to leverage what's new in MongoDB 3.6
How to leverage what's new in MongoDB 3.6
 
MongoDB .local Paris 2020: La puissance du Pipeline d'Agrégation de MongoDB
MongoDB .local Paris 2020: La puissance du Pipeline d'Agrégation de MongoDBMongoDB .local Paris 2020: La puissance du Pipeline d'Agrégation de MongoDB
MongoDB .local Paris 2020: La puissance du Pipeline d'Agrégation de MongoDB
 
Aggregation in MongoDB
Aggregation in MongoDBAggregation in MongoDB
Aggregation in MongoDB
 
MongoDB Aggregation Framework
MongoDB Aggregation FrameworkMongoDB Aggregation Framework
MongoDB Aggregation Framework
 
MongoDB Aggregation Framework
MongoDB Aggregation FrameworkMongoDB Aggregation Framework
MongoDB Aggregation Framework
 
MongoDB Europe 2016 - Advanced MongoDB Aggregation Pipelines
MongoDB Europe 2016 - Advanced MongoDB Aggregation PipelinesMongoDB Europe 2016 - Advanced MongoDB Aggregation Pipelines
MongoDB Europe 2016 - Advanced MongoDB Aggregation Pipelines
 
MongoDB Europe 2016 - ETL for Pros – Getting Data Into MongoDB The Right Way
MongoDB Europe 2016 - ETL for Pros – Getting Data Into MongoDB The Right WayMongoDB Europe 2016 - ETL for Pros – Getting Data Into MongoDB The Right Way
MongoDB Europe 2016 - ETL for Pros – Getting Data Into MongoDB The Right Way
 
Webinar: Exploring the Aggregation Framework
Webinar: Exploring the Aggregation FrameworkWebinar: Exploring the Aggregation Framework
Webinar: Exploring the Aggregation Framework
 
The Aggregation Framework
The Aggregation FrameworkThe Aggregation Framework
The Aggregation Framework
 
Agg framework selectgroup feb2015 v2
Agg framework selectgroup feb2015 v2Agg framework selectgroup feb2015 v2
Agg framework selectgroup feb2015 v2
 
MongoDB World 2016 : Advanced Aggregation
MongoDB World 2016 : Advanced AggregationMongoDB World 2016 : Advanced Aggregation
MongoDB World 2016 : Advanced Aggregation
 
Powerful Analysis with the Aggregation Pipeline
Powerful Analysis with the Aggregation PipelinePowerful Analysis with the Aggregation Pipeline
Powerful Analysis with the Aggregation Pipeline
 
Aggregation Framework MongoDB Days Munich
Aggregation Framework MongoDB Days MunichAggregation Framework MongoDB Days Munich
Aggregation Framework MongoDB Days Munich
 
The Aggregation Framework
The Aggregation FrameworkThe Aggregation Framework
The Aggregation Framework
 
Embedding a language into string interpolator
Embedding a language into string interpolatorEmbedding a language into string interpolator
Embedding a language into string interpolator
 
Mongodb Aggregation Pipeline
Mongodb Aggregation PipelineMongodb Aggregation Pipeline
Mongodb Aggregation Pipeline
 
MongoDB Aggregation
MongoDB Aggregation MongoDB Aggregation
MongoDB Aggregation
 
MongoDB .local Toronto 2019: Tips and Tricks for Effective Indexing
MongoDB .local Toronto 2019: Tips and Tricks for Effective IndexingMongoDB .local Toronto 2019: Tips and Tricks for Effective Indexing
MongoDB .local Toronto 2019: Tips and Tricks for Effective Indexing
 
Aggregation Framework in MongoDB Overview Part-1
Aggregation Framework in MongoDB Overview Part-1Aggregation Framework in MongoDB Overview Part-1
Aggregation Framework in MongoDB Overview Part-1
 

Ähnlich wie MongoDB .local San Francisco 2020: Aggregation Pipeline Power++

MongoDB .local Munich 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pip...
MongoDB .local Munich 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pip...MongoDB .local Munich 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pip...
MongoDB .local Munich 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pip...MongoDB
 
[MongoDB.local Bengaluru 2018] Tutorial: Pipeline Power - Doing More with Mon...
[MongoDB.local Bengaluru 2018] Tutorial: Pipeline Power - Doing More with Mon...[MongoDB.local Bengaluru 2018] Tutorial: Pipeline Power - Doing More with Mon...
[MongoDB.local Bengaluru 2018] Tutorial: Pipeline Power - Doing More with Mon...MongoDB
 
Aggregation Pipeline Power++: MongoDB 4.2 파이프 라인 쿼리, 업데이트 및 구체화된 뷰 소개 [MongoDB]
Aggregation Pipeline Power++: MongoDB 4.2 파이프 라인 쿼리, 업데이트 및 구체화된 뷰 소개 [MongoDB]Aggregation Pipeline Power++: MongoDB 4.2 파이프 라인 쿼리, 업데이트 및 구체화된 뷰 소개 [MongoDB]
Aggregation Pipeline Power++: MongoDB 4.2 파이프 라인 쿼리, 업데이트 및 구체화된 뷰 소개 [MongoDB]MongoDB
 
MongoDB World 2019: Exploring your MongoDB Data with Pirates (R) and Snakes (...
MongoDB World 2019: Exploring your MongoDB Data with Pirates (R) and Snakes (...MongoDB World 2019: Exploring your MongoDB Data with Pirates (R) and Snakes (...
MongoDB World 2019: Exploring your MongoDB Data with Pirates (R) and Snakes (...MongoDB
 
Introduction to MongoDB for C# developers
Introduction to MongoDB for C# developersIntroduction to MongoDB for C# developers
Introduction to MongoDB for C# developersTaras Romanyk
 
Webinar: Data Processing and Aggregation Options
Webinar: Data Processing and Aggregation OptionsWebinar: Data Processing and Aggregation Options
Webinar: Data Processing and Aggregation OptionsMongoDB
 
Neatly Hashing a Tree: FP tree-fold in Perl5 & Perl6
Neatly Hashing a Tree: FP tree-fold in Perl5 & Perl6Neatly Hashing a Tree: FP tree-fold in Perl5 & Perl6
Neatly Hashing a Tree: FP tree-fold in Perl5 & Perl6Workhorse Computing
 
Query for json databases
Query for json databasesQuery for json databases
Query for json databasesBinh Le
 
Webinar: General Technical Overview of MongoDB for Dev Teams
Webinar: General Technical Overview of MongoDB for Dev TeamsWebinar: General Technical Overview of MongoDB for Dev Teams
Webinar: General Technical Overview of MongoDB for Dev TeamsMongoDB
 
Modern Application Foundations: Underscore and Twitter Bootstrap
Modern Application Foundations: Underscore and Twitter BootstrapModern Application Foundations: Underscore and Twitter Bootstrap
Modern Application Foundations: Underscore and Twitter BootstrapHoward Lewis Ship
 
10gen Presents Schema Design and Data Modeling
10gen Presents Schema Design and Data Modeling10gen Presents Schema Design and Data Modeling
10gen Presents Schema Design and Data ModelingDATAVERSITY
 
Couchbase presentation - by Patrick Heneise
Couchbase presentation - by Patrick HeneiseCouchbase presentation - by Patrick Heneise
Couchbase presentation - by Patrick Heneiseitnig
 
Herding types with Scala macros
Herding types with Scala macrosHerding types with Scala macros
Herding types with Scala macrosMarina Sigaeva
 
2013-03-23 - NoSQL Spartakiade
2013-03-23 - NoSQL Spartakiade2013-03-23 - NoSQL Spartakiade
2013-03-23 - NoSQL SpartakiadeJohannes Hoppe
 
Webinar: Back to Basics: Thinking in Documents
Webinar: Back to Basics: Thinking in DocumentsWebinar: Back to Basics: Thinking in Documents
Webinar: Back to Basics: Thinking in DocumentsMongoDB
 
MongoDB Aggregations Indexing and Profiling
MongoDB Aggregations Indexing and ProfilingMongoDB Aggregations Indexing and Profiling
MongoDB Aggregations Indexing and ProfilingManish Kapoor
 

Ähnlich wie MongoDB .local San Francisco 2020: Aggregation Pipeline Power++ (20)

MongoDB .local Munich 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pip...
MongoDB .local Munich 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pip...MongoDB .local Munich 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pip...
MongoDB .local Munich 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pip...
 
[MongoDB.local Bengaluru 2018] Tutorial: Pipeline Power - Doing More with Mon...
[MongoDB.local Bengaluru 2018] Tutorial: Pipeline Power - Doing More with Mon...[MongoDB.local Bengaluru 2018] Tutorial: Pipeline Power - Doing More with Mon...
[MongoDB.local Bengaluru 2018] Tutorial: Pipeline Power - Doing More with Mon...
 
Aggregation Pipeline Power++: MongoDB 4.2 파이프 라인 쿼리, 업데이트 및 구체화된 뷰 소개 [MongoDB]
Aggregation Pipeline Power++: MongoDB 4.2 파이프 라인 쿼리, 업데이트 및 구체화된 뷰 소개 [MongoDB]Aggregation Pipeline Power++: MongoDB 4.2 파이프 라인 쿼리, 업데이트 및 구체화된 뷰 소개 [MongoDB]
Aggregation Pipeline Power++: MongoDB 4.2 파이프 라인 쿼리, 업데이트 및 구체화된 뷰 소개 [MongoDB]
 
MongoDB World 2019: Exploring your MongoDB Data with Pirates (R) and Snakes (...
MongoDB World 2019: Exploring your MongoDB Data with Pirates (R) and Snakes (...MongoDB World 2019: Exploring your MongoDB Data with Pirates (R) and Snakes (...
MongoDB World 2019: Exploring your MongoDB Data with Pirates (R) and Snakes (...
 
Introduction to MongoDB for C# developers
Introduction to MongoDB for C# developersIntroduction to MongoDB for C# developers
Introduction to MongoDB for C# developers
 
Webinar: Data Processing and Aggregation Options
Webinar: Data Processing and Aggregation OptionsWebinar: Data Processing and Aggregation Options
Webinar: Data Processing and Aggregation Options
 
Neatly Hashing a Tree: FP tree-fold in Perl5 & Perl6
Neatly Hashing a Tree: FP tree-fold in Perl5 & Perl6Neatly Hashing a Tree: FP tree-fold in Perl5 & Perl6
Neatly Hashing a Tree: FP tree-fold in Perl5 & Perl6
 
Query for json databases
Query for json databasesQuery for json databases
Query for json databases
 
Querying mongo db
Querying mongo dbQuerying mongo db
Querying mongo db
 
Webinar: General Technical Overview of MongoDB for Dev Teams
Webinar: General Technical Overview of MongoDB for Dev TeamsWebinar: General Technical Overview of MongoDB for Dev Teams
Webinar: General Technical Overview of MongoDB for Dev Teams
 
MongoDB 3.2 - Analytics
MongoDB 3.2  - AnalyticsMongoDB 3.2  - Analytics
MongoDB 3.2 - Analytics
 
Modern Application Foundations: Underscore and Twitter Bootstrap
Modern Application Foundations: Underscore and Twitter BootstrapModern Application Foundations: Underscore and Twitter Bootstrap
Modern Application Foundations: Underscore and Twitter Bootstrap
 
MongoDB
MongoDB MongoDB
MongoDB
 
10gen Presents Schema Design and Data Modeling
10gen Presents Schema Design and Data Modeling10gen Presents Schema Design and Data Modeling
10gen Presents Schema Design and Data Modeling
 
Couchbase presentation - by Patrick Heneise
Couchbase presentation - by Patrick HeneiseCouchbase presentation - by Patrick Heneise
Couchbase presentation - by Patrick Heneise
 
Herding types with Scala macros
Herding types with Scala macrosHerding types with Scala macros
Herding types with Scala macros
 
2013-03-23 - NoSQL Spartakiade
2013-03-23 - NoSQL Spartakiade2013-03-23 - NoSQL Spartakiade
2013-03-23 - NoSQL Spartakiade
 
Webinar: Back to Basics: Thinking in Documents
Webinar: Back to Basics: Thinking in DocumentsWebinar: Back to Basics: Thinking in Documents
Webinar: Back to Basics: Thinking in Documents
 
MongoDB Aggregations Indexing and Profiling
MongoDB Aggregations Indexing and ProfilingMongoDB Aggregations Indexing and Profiling
MongoDB Aggregations Indexing and Profiling
 
MongoDB Meetup
MongoDB MeetupMongoDB Meetup
MongoDB Meetup
 

Mehr von MongoDB

MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
 
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
 
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
 
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
 
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
 
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
 
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 MongoDB SoCal 2020: MongoDB Atlas Jump Start MongoDB SoCal 2020: MongoDB Atlas Jump Start
MongoDB SoCal 2020: MongoDB Atlas Jump StartMongoDB
 
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
 
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
 
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
 
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
 
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
 
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
 
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
 
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
 
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
 
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB
 
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB
 
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB
 
MongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDB
MongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDBMongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDB
MongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDBMongoDB
 

Mehr von MongoDB (20)

MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
 
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
 
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
 
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
 
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
 
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
 
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 MongoDB SoCal 2020: MongoDB Atlas Jump Start MongoDB SoCal 2020: MongoDB Atlas Jump Start
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
 
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
 
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
 
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
 
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
 
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
 
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
 
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
 
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
 
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
 
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
 
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
 
MongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDB
MongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDBMongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDB
MongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDB
 

Kürzlich hochgeladen

Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Enterprise Knowledge
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationRadu Cotescu
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxKatpro Technologies
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEarley Information Science
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024The Digital Insurer
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...gurkirankumar98700
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsTop 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsRoshan Dwivedi
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityPrincipled Technologies
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 

Kürzlich hochgeladen (20)

Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsTop 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 

MongoDB .local San Francisco 2020: Aggregation Pipeline Power++

  • 1. @ #MDBlocal How MongoDB 4.2 Pipeline Powers Queries, Updates and Views Asya Kamsky, Principal MongoDB Knowitall asya999 AGGREGATION POWER++ SAN FRANCISCO
  • 3. #MDBW17 Analytics with MongoDB Aggregation Framework @asya999 by Asya Kamsky, Lead MongoDB Maven PIPELINE POWER
  • 5. #MDBLocal ps ax | grep mongod | head 1 *nix command line pipe PIPELINE
  • 6. #MDBLocal $match $group | $sort| Input stream {} {} {} {} Result {} {} ... PIPELINE MongoDB document pipeline
  • 8. Stage 1 Stage 2 Stage 3 Stage 4 {} {} {} {} {} {} {} {} DATA PIPELINE {} {} {} {} {"$stage":{ ... }} START Collection View Special stage STAGES
  • 9. {title: "The Great Gatsby", language: "English", subjects: "Long Island"} {title: "The Great Gatsby", language: "English", subjects: "New York"} {title: "The Great Gatsby", language: "English", subjects: "1920s"} {title: "The Great Gatsby", language: "English", subjects: [ "Long Island", "New York", "1920s"] }, {"$match":{"language":"English"}} $match { _id:"Long Island", count: 1 }, $group { _id: "New York", count: 2 }, $unwind { _id: "1920s", count: 1 }, $sort $skip$limit $project {"$unwind":"$subjects"} {"$group":{"_id":"$subjects", "count":{"$sum:1}} { _id: "Harlem", count: 1 }, { _id: "Long Island", count: 1 }, { _id: "New York", count: 2 }, { _id: "1920s", count: 1 }, {title: "Open City", language: "English", subjects: [ "New York" "Harlem" ] } { title: "The Great Gatsby", language: "English", subjects: [ "Long Island", "New York", "1920s"] }, { title: "War and Peace", language: "Russian", subjects: [ "Russia", "War of 1812", "Napoleon"] }, { title: "Open City", language: "English", subjects: [ "New York", "Harlem" ] }, {title: "Open City", language: "English", subjects: "New York"} {title: "Open City", language: "English", subjects: "Harlem"} { _id: "Harlem", count: 1 }, {"$sort:{"count":-1} {"$limit":3} {"$project":...}
  • 10.
  • 12. {title: "The Great Gatsby", language: "English". subjects: "Long Island"} {title: "The Great Gatsby", language: "English", subjects: "New York"} {title: "The Great Gatsby", language: "English", subjects: "1920s"} {title: "The Great Gatsby", language: "English", subjects: [ "Long Island", "New York", "1920s"] }, {"$match":{"language":"English"}} $match { _id:"Long Island", count: 1 }, $group { _id: "New York", count: 2 }, $unwind { _id: "1920s", count: 1 }, $sort $skip$limit $project {"$unwind":"$subjects"} {"$group":{"_id":"$subjects", "count":{"$sum:1}} { _id: "Harlem", count: 1 }, { _id: "Long Island", count: 1 }, { _id: "New York", count: 2 }, { _id: "1920s", count: 1 }, {title: "Open City", language: "English", subjects: [ "New York" "Harlem" ] } { title: "The Great Gatsby", language: "English", subjects: [ "Long Island", "New York", "1920s"] }, { title: "War and Peace", language: "Russian", subjects: [ "Russia", "War of 1812", "Napoleon"] }, { title: "Open City", language: "English", subjects: [ "New York", "Harlem" ] }, {title: "Open City", language: "English", subjects: "New York"} {title: "Open City", language: "English", subjects: "Harlem"} { _id: "Harlem", count: 1 }, {"$sort:{"count":-1} {"$limit":3} {"$project":...} $group $sort 1
  • 13. #MDBLocal INPUT STAGE RESULTSSTAGE Each document is streamed through in RAM STREAMING RESOURCE USE
  • 14. #MDBLocal INPUT STAGE RESULTSSTAGE BLOCKING RESOURCE USE Everything has to be kept in RAM (or spill)
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20. $sort$match $group start=ISODate("...") end=ISODate("...") { user: "303900", ipaddr: "71.56.112.56", ts:ISODate("2017-05-08T...") } {$match:{ts:{$gte:start,$lt:end}}}, {$sort:{ts:1}}, {$group:{_id:"$user",ips:{$push:{ip:"$ipaddr", ts:"$ts"}}, diffIps:{$addToSet:"$ipaddr"}}}, {$match:{"diffIps.1":{$exists:true}}}, {$addFields:{diffs: {$filter:{ input:{$map:{ input: {$range:[0,{$subtract:[{$size:"$ips"},1]}]}, as:"i", in:{$let:{vars:{ip1:{$arrayElemAt:["$ips","$$i"]}, ip2:{$arrayElemAt:["$ips",{$add:["$$i",1]}]}}, in:{ diff:{$cond:{ if:{$ne:["$$ip1.ip","$$ip2.ip"]}, then:{$divide:[{$subtract:["$$ip2.ts","$$ip1.ts"]},60000]}, else: 9999 }}, ip1:"$$ip1.ip", t1:"$$ip1.ts", ip2:"$$ip2.ip", t2:"$$ip2.ts" }}}}}, cond:{$lt:["$$this.diff",10]} }}}}, {$match:{"diffs":{$ne:[]}}}, {$project:{_id:0, user:"$_id", suspectLogins:"$diffs"}} { "user" : "35237073", "suspectLogins" : [ {"diff": 4.8333333333, "ip1": "106.220.151.16", "t1":"2017-05-08T06:58", "ip2": "223.182.113.15" "t2":"2017-05-08T07:03" }, {"diff": 8.3, "ip1": "223.182.113.15", "t1":"2017-05-08T07:03", "ip2": "49.206.217.26", "t2":"2017-05-08T07:11" } ] } $match $addFields $match $project
  • 23. #MDBW17 Analytics with MongoDB Aggregation Framework @asya999 by Asya Kamsky, Lead MongoDB Maven PIPELINE POWER https://github.com/asya999/mdbw17
  • 27. #MDBLocal THE FUTURE OF AGGREGATION Better performance & optimizations More stages & expressions More options for output Compass helper for aggregate Unify different languages
  • 28. #MDBLocal THE FUTURE OF AGGREGATION Better performance & optimizations More stages & expressions More options for output Compass helper for aggregate Unify different languages
  • 29. #MDBLocal THE FUTURE OF AGGREGATION Better performance & optimizations More stages & expressions More options for output Compass helper for aggregate Unify different languages
  • 30. #MDBLocal THE FUTURE OF AGGREGATION More options for output Unify different languages
  • 31. #MDBLocal THE PRESENT OF AGGREGATION More options for output Unify different languages
  • 33. #MDBLocal Unify Different Languages {children: [ {name:"Max", dob:"1994-12-01", dep:true}, {name:"Sam", dob:"1997-09-28", dep:true}, {name:"Kim", dob:"2000-02-29", dep:true} ]} AGGREGATION
  • 34. #MDBLocal Unify Different Languages {children: [ {name:"Max", dob:"1994-12-01", dep:true}, {name:"Sam", dob:"1997-09-28", dep:true}, {name:"Kim", dob:"2000-02-29", dep:true} ]} AGGREGATION db.c.aggregate([ {$addFields:{ numChildren:{$size:"$children"}, numDependents:{$size:{ $filter:{ input:"$children.dep", cond: "$$this" } }} }}, ... ])
  • 35. #MDBLocal Unify Different Languages {children: [ {name:"Max", dob:"1994-12-01", dep:true}, {name:"Sam", dob:"1997-09-28", dep:true}, {name:"Kim", dob:"2000-02-29", dep:true} ]} AGGREGATION FIND db.c.aggregate([ {$addFields:{ numChildren:{$size:"$children"}, numDependents:{$size:{ $filter:{ input:"$children.dep", cond: "$$this" } }} }}, ... ])
  • 36. #MDBLocal Unify Different Languages {children: [ {name:"Max", dob:"1994-12-01", dep:true}, {name:"Sam", dob:"1997-09-28", dep:true}, {name:"Kim", dob:"2000-02-29", dep:true} ]} AGGREGATION FIND db.c.find ( {$expr:{ $lt:[ {$size:{$filter:{ input: "$children.dep", cond: "$$this" }}}, 2 ] }} )
  • 37. #MDBLocal Unify Different Languages {children: [ {name:"Max", dob:"1994-12-01", dep:true}, {name:"Sam", dob:"1997-09-28", dep:true}, {name:"Kim", dob:"2000-02-29", dep:true} ]} AGGREGATION FIND UPDATE db.c.find ( {$expr:{ $lt:[ {$size:{$filter:{ input: "$children.dep", cond: "$$this" }}}, 2 ] }} )
  • 38. #MDBLocal Unify Different Languages {children: [ {name:"Max", dob:"1994-12-01", dep:true}, {name:"Sam", dob:"1997-09-28", dep:true}, {name:"Kim", dob:"2000-02-29", dep:true} ]} AGGREGATION FIND UPDATE db.c.update( {$expr:{ $anyElementTrue:{$map:{ input:"$children", in: {$and:[ {$lt:["$$this.dob","1997-01-22"]}, "$$this.dep" ]} }} }}, {$set:{ audit:true }} )
  • 43. #MDBLocal Update in 4.2 { } OR [ ] <update>
  • 44. #MDBLocal Update in 4.2 { <same> } [ ] <update>
  • 45. #MDBLocal Update in 4.2 { <same> } [ <aggregation-pipeline> ] <update>
  • 47. #MDBLocal { $addFields: { } } { $project: { } } { $replaceRoot: { } } { $set: { } } { $unset: [ ] } { $replaceWith: { } }
  • 48. #MDBLocal db.coll.update({_id:1}, {$inc:{a:1}}, {upsert:true}) { _id: 1 } { _id: 1, a: 10 } { _id: 1, a: 100 } --- { _id: 1, a: "10" } { _id: 1, a: 1 } { _id: 1, a: 11 } { _id: 1, a: 101 } { _id: 1, a: 1 } "errmsg" : "Cannot apply to a value of non-numeric type."
  • 49. #MDBLocal { _id: 1 } { _id: 1, a: 10 } { _id: 1, a: 100 } --- { _id: 1, a: "10" } { _id: 1, a: 1 } { _id: 1, a: 11 } { _id: 1, a: 101 } { _id: 1, a: 1 } { _id: 1, a: 1 } db.coll.update({_id:1}, [ {$set:{a:{$sum:["$a",1]}}} ], {upsert:true})
  • 50. #MDBLocal { _id: 1 } { _id: 1, a: 10 } { _id: 1, a: 100 } --- { _id: 1, a: "10" } { _id: 1, a: 1 } { _id: 1, a: 11 } { _id: 1, a: 101 } { _id: 1, a: 1 } "errmsg" : "$add only supports numeric or date types, not string" db.coll.update({_id:1}, [ {$set:{a:{$add:["$a",1]}}} ], {upsert:true})
  • 51. #MDBLocal { _id: 1 } { _id: 1, a: 10 } { _id: 1, a: 100 } --- { _id: 1, a: "10" } { _id:1, a: 21 } { _id: 1, a: 11 } { _id: 1, a: 101 } { _id:1, a: 21 } db.coll.update({_id:1}, [ {$set:{a:{$ }} ], {upsert:true})
  • 52. #MDBLocal { _id: 1 } { _id: 1, a: 10 } { _id: 1, a: 100 } --- { _id: 1, a: "10" } { _id:1, a: 21 } { _id: 1, a: 11 } { _id: 1, a: 101 } { _id:1, a: 21 } db.coll.update({_id:1}, [ {$set:{a:{$cond:{ if: , then: , else: }}}}], {upsert:true})
  • 53. #MDBLocal { _id: 1 } { _id: 1, a: 10 } { _id: 1, a: 100 } --- { _id: 1, a: "10" } { _id:1, a: 21 } { _id: 1, a: 11 } { _id: 1, a: 101 } { _id:1, a: 21 } db.coll.update({_id:1}, [ {$set:{a:{$cond:{ if: {$eq:[{$type:"$a"},"missing"]}, then: , else: }}}}], {upsert:true})
  • 54. #MDBLocal { _id: 1 } { _id: 1, a: 10 } { _id: 1, a: 100 } --- { _id: 1, a: "10" } { _id:1, a: 21 } { _id: 1, a: 11 } { _id: 1, a: 101 } { _id:1, a: 21 } db.coll.update({_id:1}, [ {$set:{a:{$cond:{ if: {$eq:[{$type:"$a"},"missing"]}, then: 21, else: {$sum:["$a",1]} }}}}], {upsert:true})
  • 55. #MDBLocal { _id: 1 } { _id: 1, a: 10 } { _id: 1, a: 100 } --- { _id: 1, a: "10" } { _id:1, a: 21 } { _id: 1, a: 11 } { _id: 1, a: 100 } { _id:1, a: 21 } db.coll.update({_id:1}, [ {$set:{a:{$cond:{ if: {$eq:[{$type:"$a"},"missing"]}, then: 21, else: {$sum:["$a",1]} }}}}], {upsert:true})
  • 56. #MDBLocal { _id: 1 } { _id: 1, a: 10 } { _id: 1, a: 100 } --- { _id: 1, a: "10" } { _id:1, a: 21 } { _id: 1, a: 11 } { _id: 1, a: 100 } { _id:1, a: 21 } db.coll.update({_id:1}, [ {$set:{a:{$min:[100, {$cond:{ if: {$eq:[{$type:"$a"},"missing"]}, then: 21, else: {$sum:["$a",1]} }}]} }}], {upsert:true})
  • 57. #MDBLocal { _id: 1 } { _id: 1, a: 10 } { _id: 1, a: 100 } --- { _id: 1, a: "10" } { _id:1, a: 21 } { _id: 1, a: 11 } { _id: 1, a: 100 } { _id:1, a: 21 } { _id:1, a: 1 } db.coll.update({_id:1}, [ {$set:{a:{$min:[100, {$cond:{ if: {$eq:[{$type:"$a"},"missing"]}, then: 21, else: {$sum:["$a",1]} }}]} }}], {upsert:true})
  • 58. #MDBLocal { _id: 1 } { _id: 1, a: 10 } { _id: 1, a: 100 } --- { _id: 1, a: "10" } { _id:1, a: 21 } { _id: 1, a: 11 } { _id: 1, a: 100 } { _id:1, a: 21 } { _id:1, a: 1 } db.coll.update({_id:1}, [ {$set:{a:{$min:[100, {$cond:{ if: {$eq:[{$type:"$a"},"missing"]}, then: 21, else: {$sum:["$a",1]} }}]}, prev_a:"$a" }}], {upsert:true})
  • 59. #MDBLocal { _id: 1 } { _id: 1, a: 10 } { _id: 1, a: 100 } --- { _id: 1, a: "10" } { _id:1, a: 21 } { _id: 1, a: 11, prev_a: 10 } { _id: 1, a: 100, prev_a: 100 } { _id:1, a: 21 } { _id:1, a: 1, prev_a: "10" } db.coll.update({_id:1}, [ {$set:{a:{$min:[100, {$cond:{ if: {$eq:[{$type:"$a"},"missing"]}, then: 21, else: {$sum:["$a",1]} }}]}, prev_a:"$a" }}], {upsert:true})
  • 61. #MDBLocal Set Defaults {_id: 1, a: 5, b: 12} {_id: 2, a: 15, c: "abc"} {_id: 3, b: 99, c: "xyz"} If a or b are missing, set to 0, if c is missing -> "unset"
  • 62. #MDBLocal Set Defaults {_id: 1, a: 5, b: 12} {_id: 2, a: 15, c: "abc"} {_id: 3, b: 99, c: "xyz"} If a or b are missing, set to 0, if c is missing -> "unset" db.coll.update({}, [ {$replaceWith:{ }} ], {multi:true})
  • 63. #MDBLocal Set Defaults {_id: 1, a: 5, b: 12} {_id: 2, a: 15, c: "abc"} {_id: 3, b: 99, c: "xyz"} If a or b are missing, set to 0, if c is missing -> "unset" db.coll.update({}, [ {$replaceWith:{$mergeObjects:[ ]}} ], {multi:true})
  • 64. #MDBLocal Set Defaults {_id: 1, a: 5, b: 12} {_id: 2, a: 15, c: "abc"} {_id: 3, b: 99, c: "xyz"} If a or b are missing, set to 0, if c is missing -> "unset" db.coll.update({}, [ {$replaceWith:{$mergeObjects:[ { a:0, b:0, c:"unset" }, "$$ROOT" ]}} ], {multi:true})
  • 65. #MDBLocal Set Defaults {_id: 1, a: 5, b: 12} {_id: 2, a: 15, c: "abc"} {_id: 3, b: 99, c: "xyz"} If a or b are missing, set to 0, if c is missing -> "unset" db.coll.update({}, [ {$replaceWith:{$mergeObjects:[ { a:0, b:0, c:"unset" }, "$$ROOT" ]}} ], {multi:true}) {_id: 1, a: 5, b: 12, c: "unset"} {_id: 2, a: 15, b: 0, c: "abc"} {_id: 3, a: 0, b: 99, c: "xyz"}
  • 66. #MDBLocal { id: 1, d: ISODate("2019-06-04T00:00:00"), h: [ { hour:"11", value: 296 }, { hour:"12", value: 300 } ]} id: X, d:Y, hour:Z, value: VAL db.coll.update({id:X, d:Y}, [ {$set:{h:{$cond:{ if: then: else: }}}}], {upsert:true})
  • 67. #MDBLocal { id: 1, d: ISODate("2019-06-04T00:00:00"), h: [ { hour:"11", value: 296 }, { hour:"12", value: 300 } ]} id: X, d:Y, hour:Z, value: VAL db.coll.update({id:X, d:Y}, [ {$set:{h:{$cond:{ if: {$in:[Z,"$h.hour"]}, then:{$map:{ input:"$h", in: {$cond:{ if:{$ne:["$$this.hour",Z]}, then:"$$this", else: {hour: Z, value: {$sum:[ "$$this.value", VAL]}} }}}}, else:{$concatArrays:["$h",[{hour:Z,value:VAL}]]} }}}}], {upsert:true}) if: then: else:
  • 68. #MDBLocal if: then: else: { id: 1, d: ISODate("2019-06-04T00:00:00"), h: [ { hour:"11", value: 296 }, { hour:"12", value: 300 } ]} id: X, d:Y, hour:Z, value: VAL db.coll.update({id:X, d:Y}, [ {$set: {h:{$ifNull:["$h", [] ]}} }, {$set:{h:{$cond:{ if: {$in:[Z,"$h.hour"]}, then:{$map:{ input:"$h", in: {$cond:{ if:{$ne:["$$this.hour",Z]}, then:"$$this", else: {hour: Z, value: {$sum:[ "$$this.value", VAL]}} }}}}, else:{$concatArrays:["$h",[{hour:Z,value:VAL}]]} }}}}], {upsert:true})
  • 69. #MDBLocal Recap: Updates can be specified with aggregation pipeline All fields from existing document can be accessed Slightly slower, but a lot more powerful
  • 70. #MDBLocal THE FUTURE OF AGGREGATION Better performance & optimizations More stages & expressions More options for output Compass helper for aggregate Unify different languages
  • 71. #MDBLocal THE FUTURE OF AGGREGATION Better performance & optimizations More stages & expressions More options for output Compass helper for aggregate Unify different languages
  • 72. #MDBLocal THE FUTURE OF AGGREGATION More options for output
  • 74. #MDBLocal Prior to MongoDB 4.2 $out coll new_coll $out
  • 75. #MDBLocal Prior to MongoDB 4.2 $out coll new_coll $out db.coll.aggregate( [ {pipeline}, ... {$out: "new_coll"} ]);
  • 76. #MDBLocal Prior to MongoDB 4.2 $out coll new_coll $out db.coll.aggregate( [ {pipeline}, ... {$out: "new_coll"} ]); new_coll ○ must be unsharded ○ overwrites existing
  • 77. New $merge stage in aggregation pipeline
  • 79. #MDBLocal MongoDB 4.2 $merge db.coll.aggregate( [ {pipeline}, ..., {$merge: { ... } ]); coll coll2 $merge
  • 80. #MDBLocal MongoDB 4.2 $merge db.coll.aggregate( [ {pipeline}, ..., {$merge: { ... } ]); coll2 can exist same or different 'db' can be sharded coll coll2 $merge
  • 81. #MDBLocal coll coll2 $merge { } { } { } { } { } { } { } { } MongoDB 4.2
  • 84. #MDBLocal {$merge: {into: {db: "db2", coll: "collection2"}} $merge syntax { $merge: { into: <target> } }
  • 86. #MDBLocal { $merge: { into: <target>, on: <fields> } } on: "_id" on: [ "_id", "shardkey(s)" ] must be unique $merge syntax
  • 87. #MDBLocal { $merge: { into: <target>, on: <fields> } } $merge syntax
  • 91. #MDBLocal Actions nothing matched: usually insert document matched: source target
  • 92. #MDBLocal Actions nothing matched: usually insert document matched: overwrite? update? ??? source target
  • 93. #MDBLocal Actions nothing matched: usually insert document matched: update source target
  • 94. #MDBLocal Actions nothing matched: usually insert document matched: update (merge) source target
  • 95. #MDBLocal $merge syntax { $merge: { into: <target>, whenNotMatched: whenMatched: } }
  • 96. #MDBLocal $merge syntax { $merge: { into: <target>, whenNotMatched:"insert", whenMatched: } }
  • 97. #MDBLocal $merge syntax { $merge: { into: <target>, whenNotMatched:"insert", whenMatched:"merge" } }
  • 98. #MDBLocal $merge syntax { $merge: { into: <target>, whenNotMatched:"insert"|"discard"|"fail", whenMatched:"merge" } }
  • 99. #MDBLocal $merge syntax { $merge: { into: <target>, whenNotMatched:"insert"|"discard"|"fail", whenMatched:"merge"|"replace"|"keepExisting"|"fail"|[...] } }
  • 100. #MDBLocal $merge syntax { $merge: { into: <target>, whenMatched:[...] } }
  • 101. #MDBLocal $merge syntax { $merge: { into: <target>, whenMatched:[<custom pipeline>] } }
  • 102. #MDBLocal $merge example { $merge: { into: <target>, whenMatched:[ {$addFields:{ }} ] } }
  • 103. #MDBLocal $merge example { $merge: { into: <target>, whenMatched:[ {$addFields:{ total:{$sum:["$total","$$new.total"]} }} ] } }
  • 104. #MDBLocal $merge example { $merge: { into: <target>, whenMatched:[ {$set:{ total:{$sum:["$total","$$new.total"]} }} ] } }
  • 105. #MDBLocal $merge example { $merge: { into: <target>, whenMatched:[ {$set:{ total:{$sum:["$total","$$new.total"]} }} ] } }
  • 106. #MDBLocal $merge example { $merge: { into: <target>, whenMatched:[ {$set:{ total:{$sum:["$total","$$new.total"]} }} ] } } Incoming Target { _id: "37", total: 64, f1: "x" } { _id: "37", total: 245, f1: "yyy" } Result: { }
  • 107. #MDBLocal $merge example { $merge: { into: <target>, whenMatched:[ {$set:{ total:{$sum:["$total","$$new.total"]} }} ] } } Incoming Target { _id: "37", total: 64, f1: "x" } { _id: "37", total: 245, f1: "yyy" } Result: { _id: "37", total: 309, f1: "yyy" }
  • 108. #MDBLocal $merge example 2 { $merge: { into: <target>, whenMatched:[ {$replaceWith:{$mergeObjects:[ "$$new", {total:{$sum:["$$new.total", "$total"]}} ]}} ] } }
  • 109. #MDBLocal $merge example 2 { $merge: { into: <target>, whenMatched:[ {$replaceWith:{$mergeObjects:[ "$$new", {total:{$sum:["$$new.total", "$total"]}} ]}} ] } } Incoming Target { _id: "37", total: 64, f1: "x" } { _id: "37", total: 245, f1: "yyy" } Result: { }
  • 110. #MDBLocal $merge example 2 { $merge: { into: <target>, whenMatched:[ {$replaceWith:{$mergeObjects:[ "$$new", {total:{$sum:["$$new.total", "$total"]}} ]}} ] } } Incoming Target { _id: "37", total: 64, f1: "x" } { _id: "37", total: 245, f1: "yyy" } Result: { _id: "37", total: 309, f1: "x" }
  • 111. #MDBLocal $merge syntax { $merge: { into: <target>, whenMatched:[...] } }
  • 112. #MDBLocal $merge syntax { $merge: { into: <target>, let: { ... }, whenMatched:[ ...] } }
  • 113. #MDBLocal $merge syntax { $merge: { into: <target>, let: {new: "$$ROOT"}, whenMatched:[ ...] } }
  • 115. #MDBLocal temp real data real Using $merge to append loaded and cleansed records loaded into db
  • 116. #MDBLocal aggregate 'temp' and append valid records to 'data' db.temp.aggregate( [ { ... } /* pipeline to massage and cleanse data in temp */, {$merge:{ into: "data", whenMatched: "fail" }} ]);
  • 117. #MDBLocal aggregate 'temp' and append valid records to 'data' db.temp.aggregate( [ { ... } /* pipeline to massage and cleanse data in temp */, {$merge:{ into: "data", whenMatched: "fail" }} ]); Similar to SQL's INSERT INTO T1 SELECT * from T2
  • 119. #MDBLocal mflix users users mfriendbook users sv Using $merge to populate/update user fields from other services
  • 120. #MDBLocal mflix users users mfriendbook users sv Using $merge to populate/update user fields from other services sv.users { _id: "user253", dob: ISODate(...), f1: "yyy" }
  • 121. #MDBLocal $merge updates fields from mflix.users collection into sv.users collection. Our "_id" field is unique username mflix_pipeline = [ { "$project" : { "_id" : "$username", "mflix" : "$$ROOT" }}, { "$merge" : { "into" : { "db": "sv", "collection" : "users" }, "whenNotMatched" : "discard" }} ] (in mflix) sv.users { _id: "user253", dob: ISODate(...), f1: "yyy" }
  • 122. #MDBLocal $merge updates fields from mflix.users collection into sv.users collection. Our "_id" field is unique username mflix_pipeline = [ { "$project" : { "_id" : "$username", "mflix" : "$$ROOT" }}, { "$merge" : { "into" : { "db": "sv", "collection" : "users" }, "whenNotMatched" : "discard" }} ] (in mflix) db.users.aggregate(mflix_pipeline) sv.users { _id: "user253", dob: ISODate(...), f1: "yyy", mflix: { ... } }
  • 123. #MDBLocal $merge updates fields from mfriendbook.users collection into sv.users collection. Our "_id" field is unique username mfriendbook_pipeline = [ { "$project" : { "_id" : "$username", "mfriendbook" : "$$ROOT" }}, { "$merge" : { "into" : { "db": "sv", "collection" : "users" }, "whenNotMatched" : "discard" }} ] (in mfriendbook) sv.users { _id: "user253", dob: ISODate(...), f1: "yyy", mflix: { ... } }
  • 124. #MDBLocal $merge updates fields from mfriendbook.users collection into sv.users collection. Our "_id" field is unique username mfriendbook_pipeline = [ { "$project" : { "_id" : "$username", "mfriendbook" : "$$ROOT" }}, { "$merge" : { "into" : { "db": "sv", "collection" : "users" }, "whenNotMatched" : "discard" }} ] (in mfriendbook) db.users.aggregate(mfriendbook_pipeline) sv.users { _id: "user253", dob: ISODate(...), f1: "yyy", mflix: { ... }, mfriendbook: { ... } }
  • 126. registrations real regsummary real Using $merge to incrementally update periodic rollups in summary
  • 127. #MDBLocal $merge to create/update periodic rollups in summary collection (for all days) db.regsummary.createIndex({event:1, date:1}, {unique: true});
  • 128. #MDBLocal $merge to create/update periodic rollups in summary collection (for all days) db.regsummary.createIndex({event:1, date:1}, {unique: true}); db.registrations.aggregate([ {$match: {event_id: "MDBW19"}}, {$group:{ _id:{$dateToString:{date:"$date",format:"%Y-%m-%d"}}, count: {$sum:1} }}, {$project: {_id:0,event:"MDBW19",date:"$_id",total:"$count"}}, {$merge: { into: "regsummary", on: ["event", "date"] }} ])
  • 129. #MDBLocal $merge to create/update periodic rollups in summary collection (for all days) db.regsummary.createIndex({event:1, date:1}, {unique: true}); db.registrations.aggregate([ {$match: {event_id: "MDBW19"}}, {$group:{ _id:{$dateToString:{date:"$date",format:"%Y-%m-%d"}}, count: {$sum:1} }}, {$project: {_id:0,event:"MDBW19",date:"$_id",total:"$count"}}, {$merge: { into: "regsummary", on: ["event", "date"] }} ]) { "event" : "MDBW19", "date" : "2019-05-19", "total" : 33 } { "event" : "MDBW19", "date" : "2019-05-20", "total" : 15 } { "event" : "MDBW19", "date" : "2019-05-21", "total" : 24 }
  • 130. #MDBLocal $merge to incrementally update periodic rollups in summary collection (for single day)
  • 131. #MDBLocal $merge to incrementally update periodic rollups in summary collection (for single day) db.registrations.aggregate([ {$match: { event_id: "MDBW19", date:{$gte:ISODate("2019-05-22"),$lt:ISODate("2019-05-23")} }}, {$count: "total"}, {$addFields: {event:"MDBW19", "date":"2019-05-22"}}, {$merge: { into: "regsummary", on: ["event", "date"] }} ])
  • 132. #MDBLocal $merge to incrementally update periodic rollups in summary collection (for single day) db.registrations.aggregate([ {$match: { event_id: "MDBW19", date:{$gte:ISODate("2019-05-22"),$lt:ISODate("2019-05-23")} }}, {$count: "total"}, {$addFields: {event:"MDBW19", "date":"2019-05-22"}}, {$merge: { into: "regsummary", on: ["event", "date"] }} ]) { "event" : "MDBW19", "date" : "2019-05-19", "total" : 33 } { "event" : "MDBW19", "date" : "2019-05-20", "total" : 15 } { "event" : "MDBW19", "date" : "2019-05-21", "total" : 24 } { "event" : "MDBW19", "date" : "2019-05-22", "total" : 34 }