Develop Reference

Print only specific parts of json file

I am wondering what I am doing wrong when trying to print the data of name of the following code in python.
import urllib.request, json
with urllib.request.urlopen("<THIS IS A URL IN THE ORIGINAL SCRIPT>") as url:
data = json.loads(url.read().decode())
print (data['Departure']['Product']['name'])
print (data['Departure']['Stops']['Stop'][0]['depTime'])
And this is the api I am fetching the data from:
{
"Departure" : [ {
"Product" : {
"name" : "Länstrafik - Buss 201",
"num" : "201",
"catCode" : "7",
"catOutS" : "BLT",
"catOutL" : "Länstrafik - Buss",
"operatorCode" : "254",
"operator" : "JLT",
"operatorUrl" : "http://www.jlt.se"
},
"Stops" : {
"Stop" : [ {
"name" : "Gislaved Lundåkerskolan",
"id" : "740040260",
"extId" : "740040260",
"routeIdx" : 12,
"lon" : 13.530096,
"lat" : 57.298178,
"depTime" : "20:55:00",
"depDate" : "2019-03-05"
}

data["Departure"] is a list, and you are indexing into it like it's a dictionary.

You wrote the dictionary sample confusingly. Here's how I think it looks:
d = {
"Departure" : [ {
"Product" : {
"name" : "Länstrafik - Buss 201",
"num" : "201",
"catCode" : "7",
"catOutS" : "BLT",
"catOutL" : "Länstrafik - Buss",
"operatorCode" : "254",
"operator" : "JLT",
"operatorUrl" : "http://www.jlt.se"
},
"Stops" : {
"Stop" : [ {
"name" : "Gislaved Lundåkerskolan",
"id" : "740040260",
"extId" : "740040260",
"routeIdx" : 12,
"lon" : 13.530096,
"lat" : 57.298178,
"depTime" : "20:55:00",
"depDate" : "2019-03-05"
}]}}]}
And here's how you can print depTime
print(d["Departure"][0]["Stops"]["Stop"][0]["depTime"])
The important part you missed is d["Departure"][0] because d["Departure"] is a list.

As Kyle said in the previous answer, data["Departure"] is a list, but you're trying to use it as a dictionary. There are 2 possible solutions.
Change data["Departure"]["Stops"]["Stop"] etc. to data["Departure"][0]["Stops"]["Stop"] etc.
Change the JSON file to make departure into a dictionary, which would allow you to keep your original code. This would make the final JSON snippet look like this:
"Departure" : {
"Product" : {
"name" : "Länstrafik - Buss 201",
"num" : "201",
"catCode" : "7",
"catOutS" : "BLT",
"catOutL" : "Länstrafik - Buss",
"operatorCode" : "254",
"operator" : "JLT",
"operatorUrl" : "http://www.jlt.se"
},
"Stops" : {
"name" : "Gislaved Lundåkerskolan",
"id" : "740040260",
"extId" : "740040260",
"routeIdx" : 12,
"lon" : 13.530096,
"lat" : 57.298178,
"depTime" : "20:55:00",
"depDate" : "2019-03-05"
}
}

How to extract data from json into a string

I am not able to extract the "Data" "12639735;7490484;3469776;9164745;650;0"
from this file using python:
In php it's piece of cake for me but I cannot master it in python.
Other answers from Stackexchange didn't give me the answer.
Here is the contents of the file test.json:
{
"ActTime" : 1494535483,
"ServerTime" : "2017-05-11 22:44:43",
"Sunrise" : "05:44",
"Sunset" : "21:14",
"result" : [
{
"AddjMulti" : 1.0,
"AddjMulti2" : 1.0,
"AddjValue" : 0.0,
"AddjValue2" : 0.0,
"BatteryLevel" : 255,
"Counter" : "20130.221",
"CounterDeliv" : "12634.521",
"CounterDelivToday" : "0.607 kWh",
"CounterToday" : "1.623 kWh",
"CustomImage" : 0,
"Data" : "12639735;7490484;3469776;9164745;650;0",
"Description" : "",
"Favorite" : 1,
"HardwareID" : 3,
"HardwareName" : "Slimme Meter",
"HardwareType" : "P1 Smart Meter USB",
"HardwareTypeVal" : 4,
"HaveTimeout" : false,
"ID" : "1",
"LastUpdate" : "2017-05-11 22:44:39",
"Name" : "Elektriciteitsmeter",
"Notifications" : "false",
"PlanID" : "0",
"PlanIDs" : [ 0 ],
"Protected" : false,
"ShowNotifications" : true,
"SignalLevel" : "-",
"SubType" : "Energy",
"SwitchTypeVal" : 0,
"Timers" : "false",
"Type" : "P1 Smart Meter",
"TypeImg" : "counter",
"Unit" : 1,
"Usage" : "650 Watt",
"UsageDeliv" : "0 Watt",
"Used" : 1,
"XOffset" : "0",
"YOffset" : "0",
"idx" : "1"
}
],
"status" : "OK",
"title" : "Devices"
}

This should work
import json
with open('test.json') as f:
contents = json.load(f)
print(contents['result'][0]['Data'])
Similar questions have been asked before: Parsing values from a JSON file using Python?

Got it.
url = "http://192.168.2.1:8080/json.htm?type=devices&rid=1"
response = urllib.urlopen(url)
str = json.loads(response.read())
for i in str["result"]:
datastring = i["Data"]
elementstring = i["Data"].split(';')
counter = 0
for j in elementstring:
if counter == 4:
usage = j
counter += 1
delivery = get_num(i["UsageDeliv"])

MongoDB aggregate compare with previous document

I have this query in Motor:
history = yield self.db.stat.aggregate([
{'$match': {'user_id': user.get('uid')}},
{'$sort': {'date_time': -1}},
{'$project': {'user_id': 1, 'cat_id': 1, 'doc_id': 1, 'date_time': 1}},
{'$group': {
'_id': '$user_id',
'info': {'$push': {'doc': '$doc_id', 'date': '$date_time', 'cat': '$cat_id'}},
'total': {'$sum': 1}
}},
{'$unwind': '$info'},
])
Documents in stat collection look like this:
{
"_id" : ObjectId("5788fa45bc54f428d8e77903"),
"vrr_id" : 2,
"date_time" : ISODate("2016-07-15T14:59:17.411Z"),
"ip" : "10.79.0.230",
"cat_id" : "rsl01",
"vrr_group" : ObjectId("55f6d1b5aaab934a00bae1a4"),
"col" : [
"dledu"
],
"vrr_type" : "TH",
"doc_type" : "local",
"user_id" : "696230",
"page" : null,
"method" : "OpenView",
"branch" : 9,
"sc" : 200,
"doc_id" : "004894802",
"spec" : 0
}
/* 40 */
{
"_id" : ObjectId("5788fa45bc54f428d8e77904"),
"vrr_id" : 2,
"date_time" : ISODate("2016-07-15T14:59:17.500Z"),
"ip" : "10.79.0.230",
"cat_id" : "rsl01",
"vrr_group" : ObjectId("55f6d1b5aaab934a00bae1a4"),
"col" : [
"autoref"
],
"vrr_type" : "TH",
"doc_type" : "open",
"user_id" : "696230",
"page" : null,
"method" : "OpenView",
"branch" : 9,
"sc" : 200,
"doc_id" : "000000002",
"spec" : "07"
}
I want to compare date_time field with date_time from previous document and if they are not equal (or not in timedelta within 5 seconds), include it in result.
Filtering this in Python was easy, is it possible in Mongo? How can I achieve this?

If you include some example documents from the "stat" collection I can give a more reliable answer. But with the information you've provided, I can guess. Add a stage something like:
{'$group': {'_id': '$info.date', 'info': {'$first': '$info'}}}
That gives you each document in the result list that has a distinct "date" from the previous document.
That said, if all you need is a distinct list of dates, this is simpler and faster:
db.stats.distinct("date_time")

Why 2700 records (320KB each) should take 30 seconds to be fetched?

I have 2700 records in MongoDB. Each document has a size of approximately 320KB. The engine I use is wiredTiger and the total size of collection is about 885MB.
My MongoDB config is as below:
systemLog:
destination: file
path: /usr/local/var/log/mongodb/mongo.log
logAppend: true
storage:
dbPath: /usr/local/var/mongodb
engine: wiredTiger
wiredTiger:
engineConfig:
cacheSizeGB: 1
statisticsLogDelaySecs: 0
journalCompressor: snappy
collectionConfig:
blockCompressor: snappy
indexConfig:
prefixCompression: false
net:
bindIp: 127.0.0.1
My connection is via socket:
mongo_client = MongoClient('/tmp/mongodb-27017.sock')
And collection stats reveal this result:
db.mycol.stats()
{
"ns" : "bi.mycol",
"count" : 2776,
"size" : 885388544,
"avgObjSize" : 318944,
"storageSize" : 972476416,
"capped" : false,
"wiredTiger" : {
"metadata" : {
"formatVersion" : 1
},
"creationString" : "allocation_size=4KB,app_metadata=(formatVersion=1),block_allocation=best,block_compressor=snappy,cache_resident=0,checkpoint=(WiredTigerCheckpoint.9=(addr=\"01e30275da81e4b9e99f78e30275db81e4c61d1e01e30275dc81e40fab67d5808080e439f6afc0e41e80bfc0\",order=9,time=1444566832,size=511762432,write_gen=13289)),checkpoint_lsn=(24,52054144),checksum=uncompressed,collator=,columns=,dictionary=0,format=btree,huffman_key=,huffman_value=,id=5,internal_item_max=0,internal_key_max=0,internal_key_truncate=,internal_page_max=4KB,key_format=q,key_gap=10,leaf_item_max=0,leaf_key_max=0,leaf_page_max=32KB,leaf_value_max=1MB,memory_page_max=10m,os_cache_dirty_max=0,os_cache_max=0,prefix_compression=0,prefix_compression_min=4,split_deepen_min_child=0,split_deepen_per_child=0,split_pct=90,value_format=u,version=(major=1,minor=1)",
"type" : "file",
"uri" : "statistics:table:collection-0-6630292038312816605",
"LSM" : {
"bloom filters in the LSM tree" : 0,
"bloom filter false positives" : 0,
"bloom filter hits" : 0,
"bloom filter misses" : 0,
"bloom filter pages evicted from cache" : 0,
"bloom filter pages read into cache" : 0,
"total size of bloom filters" : 0,
"sleep for LSM checkpoint throttle" : 0,
"chunks in the LSM tree" : 0,
"highest merge generation in the LSM tree" : 0,
"queries that could have benefited from a Bloom filter that did not exist" : 0,
"sleep for LSM merge throttle" : 0
},
"block-manager" : {
"file allocation unit size" : 4096,
"blocks allocated" : 0,
"checkpoint size" : 511762432,
"allocations requiring file extension" : 0,
"blocks freed" : 0,
"file magic number" : 120897,
"file major version number" : 1,
"minor version number" : 0,
"file bytes available for reuse" : 460734464,
"file size in bytes" : 972476416
},
"btree" : {
"column-store variable-size deleted values" : 0,
"column-store fixed-size leaf pages" : 0,
"column-store internal pages" : 0,
"column-store variable-size leaf pages" : 0,
"pages rewritten by compaction" : 0,
"number of key/value pairs" : 0,
"fixed-record size" : 0,
"maximum tree depth" : 4,
"maximum internal page key size" : 368,
"maximum internal page size" : 4096,
"maximum leaf page key size" : 3276,
"maximum leaf page size" : 32768,
"maximum leaf page value size" : 1048576,
"overflow pages" : 0,
"row-store internal pages" : 0,
"row-store leaf pages" : 0
},
"cache" : {
"bytes read into cache" : 3351066029,
"bytes written from cache" : 0,
"checkpoint blocked page eviction" : 0,
"unmodified pages evicted" : 8039,
"page split during eviction deepened the tree" : 0,
"modified pages evicted" : 0,
"data source pages selected for eviction unable to be evicted" : 1,
"hazard pointer blocked page eviction" : 1,
"internal pages evicted" : 0,
"pages split during eviction" : 0,
"in-memory page splits" : 0,
"overflow values cached in memory" : 0,
"pages read into cache" : 10519,
"overflow pages read into cache" : 0,
"pages written from cache" : 0
},
"compression" : {
"raw compression call failed, no additional data available" : 0,
"raw compression call failed, additional data available" : 0,
"raw compression call succeeded" : 0,
"compressed pages read" : 10505,
"compressed pages written" : 0,
"page written failed to compress" : 0,
"page written was too small to compress" : 0
},
"cursor" : {
"create calls" : 7,
"insert calls" : 0,
"bulk-loaded cursor-insert calls" : 0,
"cursor-insert key and value bytes inserted" : 0,
"next calls" : 0,
"prev calls" : 2777,
"remove calls" : 0,
"cursor-remove key bytes removed" : 0,
"reset calls" : 16657,
"search calls" : 16656,
"search near calls" : 0,
"update calls" : 0,
"cursor-update value bytes updated" : 0
},
"reconciliation" : {
"dictionary matches" : 0,
"internal page multi-block writes" : 0,
"leaf page multi-block writes" : 0,
"maximum blocks required for a page" : 0,
"internal-page overflow keys" : 0,
"leaf-page overflow keys" : 0,
"overflow values written" : 0,
"pages deleted" : 0,
"page checksum matches" : 0,
"page reconciliation calls" : 0,
"page reconciliation calls for eviction" : 0,
"leaf page key bytes discarded using prefix compression" : 0,
"internal page key bytes discarded using suffix compression" : 0
},
"session" : {
"object compaction" : 0,
"open cursor count" : 7
},
"transaction" : {
"update conflicts" : 0
}
},
"nindexes" : 2,
"totalIndexSize" : 208896,
"indexSizes" : {
"_id_" : 143360,
"date_1" : 65536
},
"ok" : 1
}
How can I understand that MongoDB uses swap? How to infer where exactly is the bottleneck?
EDIT:
The way I fetch data in python is:
for doc in mycol.find({'date': {"$lte": '2016-12-12', '$gte': '2012-09-09'}}, {'_id': False}):
doc['uids'] = set(doc['uids'])
records.append(doc)
date field is indexed.
EDIT 2:
These are the result when fetching data:
CPU core1: ~65%
CPU core2: ~65%
CPU core3: ~65%
CPU core4: ~65%
RAM: 7190/8190MB
swap: 1140/2048MB
EDIT 3:
MongoDB log is:
2015-10-11T17:25:08.317+0330 I NETWORK [initandlisten] connection accepted from anonymous unix socket #18 (2 connections now open)
2015-10-11T17:25:08.321+0330 I NETWORK [initandlisten] connection accepted from anonymous unix socket #19 (3 connections now open)
2015-10-11T17:25:36.501+0330 I QUERY [conn19] getmore bi.mycol cursorid:10267473126 ntoreturn:0 keyUpdates:0 writeConflicts:0 numYields:3 nreturned:14 reslen:4464998 locks:{} 199ms
2015-10-11T17:25:37.665+0330 I QUERY [conn19] getmore bi.mycol cursorid:10267473126 ntoreturn:0 keyUpdates:0 writeConflicts:0 numYields:5 nreturned:14 reslen:4464998 locks:{} 281ms
2015-10-11T17:25:50.331+0330 I NETWORK [conn19] end connection anonymous unix socket (2 connections now open)
2015-10-11T17:25:50.363+0330 I NETWORK [conn18] end connection anonymous unix socket (1 connection now open)
EDIT 4:
Sample data is:
{"date": "2012-09-12", "uids": [1,2,3,4,...,30000]}
NB: I have 30k UIDs inside of uids field.
EDIT 5:
Explaining query display that it has used IXSCAN stage:
$ db.mycol.find({'date': {"$lte": '2018-11-27', '$gte': '2011-04-23'}}, {'_id': 0}).explain("executionStats")
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "bi.mycol",
"indexFilterSet" : false,
"parsedQuery" : {
"$and" : [
{
"date" : {
"$lte" : "2018-11-27"
}
},
{
"date" : {
"$gte" : "2011-04-23"
}
}
]
},
"winningPlan" : {
"stage" : "PROJECTION",
"transformBy" : {
"_id" : 0
},
"inputStage" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"date" : 1
},
"indexName" : "date_1",
"isMultiKey" : false,
"direction" : "forward",
"indexBounds" : {
"date" : [
"[\"2011-04-23\", \"2018-11-27\"]"
]
}
}
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 2776,
"executionTimeMillis" : 2312,
"totalKeysExamined" : 2776,
"totalDocsExamined" : 2776,
"executionStages" : {
"stage" : "PROJECTION",
"nReturned" : 2776,
"executionTimeMillisEstimate" : 540,
"works" : 2777,
"advanced" : 2776,
"needTime" : 0,
"needFetch" : 0,
"saveState" : 31,
"restoreState" : 31,
"isEOF" : 1,
"invalidates" : 0,
"transformBy" : {
"_id" : 0
},
"inputStage" : {
"stage" : "FETCH",
"nReturned" : 2776,
"executionTimeMillisEstimate" : 470,
"works" : 2777,
"advanced" : 2776,
"needTime" : 0,
"needFetch" : 0,
"saveState" : 31,
"restoreState" : 31,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 2776,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 2776,
"executionTimeMillisEstimate" : 0,
"works" : 2776,
"advanced" : 2776,
"needTime" : 0,
"needFetch" : 0,
"saveState" : 31,
"restoreState" : 31,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"date" : 1
},
"indexName" : "date_1",
"isMultiKey" : false,
"direction" : "forward",
"indexBounds" : {
"date" : [
"[\"2011-04-23\", \"2018-11-27\"]"
]
},
"keysExamined" : 2776,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0,
"matchTested" : 0
}
}
}
},
"serverInfo" : {
"host" : "MySys.local",
"port" : 27017,
"version" : "3.0.0",
"gitVersion" : "nogitversion"
},
"ok" : 1
}
EDIT 6:
OS: Mac osX Yosemite
MongoDB version: 3.0.0
Total RAM: 8G
Filesystem: Mac OS Extended (Journaled)

The methods I used to improve performance:
First of all instead of using for loop to traverse query and fetch data, I give the cursor to Pandas rather than creating a large list object in python:
cursor = mycol.find({'date': {"$lte": end_date, '$gte': start_date}}, {'_id': False})
df = pandas.DataFrame(list(cursor))
Performance got much better it now takes 10 seconds at most rather than 30 seconds.
Instead of using doc['uids'] = set(doc['uids']) which take around 6 seconds I didn't change default list to set and handled duplicates with dataframe itself.

You have two problems here:
Use isodate instead of string date for faster index lookups, as string dates do a lexographic string comparison, whereas isodates does a numeric one.
Since your total records are low, the type of index should not be a big problem, the problem might be size of documents and their network transfer plus deserialization.
Try a query with not selecting the uid field i.e.
for doc in mycol.find({'date': {"$lte": '2016-12-12', '$gte': '2012-09-09'}}, {'_id': False,'uid':False}):
Your query time will improve by a huge margin.You will then need to investigate the transfer times between your application and mongodb servers, and also benchmark for single document fetching using find_one() to see how much time deserializations are taking.

Get child dict values use Mongo Map/Reduce

I have a mongo collection, i want get total value of 'number_of_ad_clicks' by given sitename, timestamp and variant id. Because we have large data so it would be better use map/reduce. Could any guys give me any suggestion?
Here is my collection json format
{ "_id" : ObjectId( "4e3c280ecacbd1333b00f5ff" ),
"timestamp" : "20110805",
"variants" : { "94" : { "number_of_ad_clicks" : 41,
"number_of_search_keywords" : 9,
"total_duration" : 0,
"os" : { "os_2" : 2,
"os_1" : 1,
"os_0" : 0 },
"countries" : { "ge" : 6,
"ca" : 1,
"fr" : 8,
"uk" : 4,
"us" : 6 },
"screen_resolutions" : { "(320, 240)" : 1,
"(640, 480)" : 5,
"(1024, 960)" : 5,
"(1280, 768)" : 5 },
"widgets" : { "widget_1" : 1,
"widget_0" : 0 },
"languages" : { "ua_uk" : 8,
"ca_en" : 2,
"ca_fr" : 2,
"us_en" : 5 },
"search_keywords" : { "search_keyword_8" : 8,
"search_keyword_5" : 5,
"search_keyword_4" : 4,
"search_keyword_7" : 7,
"search_keyword_6" : 6,
"search_keyword_1" : 1,
"search_keyword_3" : 3,
"search_keyword_2" : 2 },
"number_of_pageviews" : 18,
"browsers" : { "browser_4" : 4,
"browser_0" : 0,
"browser_1" : 1,
"browser_2" : 2,
"browser_3" : 3 },
"keywords" : { "keyword_5" : 5,
"keyword_4" : 4,
"keyword_1" : 1,
"keyword_0" : 0,
"keyword_3" : 3,
"keyword_2" : 2 },
"number_of_keyword_clicks" : 83,
"number_of_visits" : 96 } },
"site_name" : "fonter.com",
"number_of_variants" : 1 }
Here is my try. but failed.
He is my try.
m = function() {
emit(this.query, {variants: this.variants});
}
r = function(key , vals) {
var clicks = 0 ;
for(var i = 0; i < vals.length(); i++){
clicks = vals[i]['number_of_ad_clicks'];
}
return clicks;
}
res = db.variant_daily_collection.mapReduce(m, r, {out : "myoutput", "query":{"site_name": 'fonter.com', 'timestamp': '20110805'}})
db.myoutput.find()
could somebody any suggestion?
Thank you very much, i try you solution but nothing return.
I invoke the mapreduce in the following, is there any thing wrong?
res = db.variant_daily_collection.mapReduce(map, reduce, {out : "myoutput", "query":{"site_name": 'facee.com', 'timestamp': '20110809', 'variant_id': '305'}})
db.myoutput.find()

The emit function emits both a key and a value.
If you are used to SQL think of key as your GROUP BY and value as your SUM(), AVG(), etc..
In your case you want to "group by": site_name, timestamp and variant id. It looks like you may have more than one variant, so you will need to loop through the variants, like this:
map = function() {
for(var i in variants){
var key = {};
key.timestamp = this.timestamp;
key.site_name = this.site_name;
key.variant_id = i; // that's the "94" string.
var value = {};
value.clicks = this.variants[i].number_of_ad_clicks;
emit(key, value);
}
}
The reduce function will get an array of values each one like this { clicks: 41 }. The function needs to return one object that looks the same.
So if you get values = [ {clicks:21}, {clicks:10}, {clicks:5} ] you must output {clicks:36}.
So you do something like this:
reduce = function(key , vals) {
var returnValue = { clicks: 0 }; // initializing to zero
for(var i = 0; i < vals.length(); i++){
returnValue.clicks += vals[i].clicks;
}
return returnValue;
}
Note that the value from map has the same shape as the return from reduce.