I am using NebulaGraph Explorer Workflow to create DAG pipelines on NebulaGraph today.
I am creating a DAG with three tasks like this:
┌──────────────────────────────────────────┐
│ │
│ MATCH ()-[e]->() │
│ WITH e LIMIT 10000 │
│ WITH e AS e │
│ WHERE e.goals > 10 │
│ AND toFloat(e.goals)/e.caps > 0.2 │
│ RETURN src(e), dst(e) │
│ │ │
└──────────┼───────────────────────────────┘
│
▼
┌──────────────────────────────────┐
│ │
│ MATCH (v0)-[:belongto]-(v1) │
│ WHERE id(v0) == ${src} │ # here I mapped src(e) from last task into ${src}
│ RETURN id(v0), id(v1) │
│ │ │ │
└───────────┼────────┼─────────────┘
│ │
┌──────────▼────────▼────────────┐
│ │
│ Betweenness Centrality │
│ │
│ │
└────────────────────────────────┘
While it failed in the second task:
Process exited with status 1
/root/nebula-analytics-3.3.0-centos7.x86_64/3rd/mpich/bin/mpiexec.hydra -n 1 -hosts 192.168.8.237 /root/nebula-analytics-3.3.0-centos7.x86_64/bin/exec_ngql --ngql=MATCH (v0)-[:belongto]-(v1)
WHERE id(v0) == "Álvaro Morata","Romelu Lukaku","Neymar","Naïm Sliti","Mehdi Taremi","Mario Götze","Kylian Mbappé","Kasper Dolberg","Jordan Morris","Joel Campbell","Ivan Perišić","Enner Valencia (captain","Christian Eriksen","Alphonso Davies","Ali Assadalla","Akram Afif","İlkay Gündoğan","Youssef En-Nesyri","Serge Gnabry","Sardar Azmoun","Richarlison","Raúl Jiménez","Mohammed Muntari","Almoez Ali","Memphis Depay","Marcus Rashford","Luis Suárez","Lucas Cavallini","Leroy Sané","Karl Toko Ekambi","Hirving Lozano","Haris Seferovic","Hakim Ziyech","Gareth Bale (captain","Gabriel Jesus","Dušan Tadić (captain","Christian Pulisic","Bruno Fernandes","Andrej Kramarić","Aleksandar Mitrović","Aaron Ramsey","Thomas Partey","Robert Lewandowski (captain","Raheem Sterling","Lionel Messi (captain","Kwon Chang-hoon","Junior Hoilett","Hassan Al-Haydos (captain","Cristiano Ronaldo (captain","André Silva","André Ayew (captain","Alireza Jahanbakhsh","Ángel Di María","Wahbi Khazri","Vincent Aboubakar (captain","Thomas Müller","Takumi Minamino","Leon Goretzka","Krzysztof Piątek","Karim Benzema","Jordan Ayew","Harry Kane (captain","Ferran Torres","Cyle Larin","Arkadiusz Milik","Antoine Griezmann","Xherdan Shaqiri","Son Heung-min (captain","Salem Al-Dawsari","Olivier Giroud","Michy Batshuayi","Lautaro Martínez","Kevin De Bruyne","Karim Ansarifard","Jonathan David","Hwang Ui-jo","Eric Maxim Choupo-Moting","Edinson Cavani","Eden Hazard (captain"
RETURN id(v0), id(v1) --threads=1 --datasource_user=root --datasink_hdfs_url=hdfs://192.168.8.168:9000/ll_test/analytics/1999/tasks/query_2/ --datasource_graphd=192.168.8.131:9669 --datasource_space=fifa_2020 --datasource_graphd_timeout=60000
I20230109 02:52:37.758909 2224521 base.hpp:179] thread support level provided by MPI:
I20230109 02:52:37.759177 2224521 base.hpp:182] MPI_THREAD_MULTIPLE
I20230109 02:52:37.759189 2224521 base.hpp:215] threads: 1
I20230109 02:52:37.759192 2224521 base.hpp:216] sockets: 2
I20230109 02:52:37.759194 2224521 base.hpp:217] partitions: 1
I20230109 02:52:37.759402 2224521 license.cc:519] [part-0]Signature validation started
I20230109 02:52:37.759697 2224521 license.cc:547] [part-0]Signature validation succeed
I20230109 02:52:37.759745 2224521 license.cc:717] [part-0][License] Trial license detected, hardware checking is skipped.
I20230109 02:52:37.759830 2224521 license.cc:623] [part-0]The number of cpus of the current machine is 4
I20230109 02:52:37.759858 2224521 license.cc:253] [License] Expiration timestamp in UTC: 4826275199
I20230109 02:52:37.759866 2224521 license.cc:259] [License] Timezone difference: 0 seconds
I20230109 02:52:37.759869 2224521 license.cc:263] [License] Expiration timestamp in local time zone: 4826275199
I20230109 02:52:37.759872 2224521 license.cc:607] [part-0][License] Expiration check passed
I20230109 02:52:37.810142 2224521 exec_ngql.cc:52] stmt:MATCH (v0)-[:belongto]-(v1)
WHERE id(v0) == "Álvaro Morata","Romelu Lukaku","Neymar","Naïm Sliti","Mehdi Taremi","Mario Götze","Kylian Mbappé","Kasper Dolberg","Jordan Morris","Joel Campbell","Ivan Perišić","Enner Valencia (captain","Christian Eriksen","Alphonso Davies","Ali Assadalla","Akram Afif","İlkay Gündoğan","Youssef En-Nesyri","Serge Gnabry","Sardar Azmoun","Richarlison","Raúl Jiménez","Mohammed Muntari","Almoez Ali","Memphis Depay","Marcus Rashford","Luis Suárez","Lucas Cavallini","Leroy Sané","Karl Toko Ekambi","Hirving Lozano","Haris Seferovic","Hakim Ziyech","Gareth Bale (captain","Gabriel Jesus","Dušan Tadić (captain","Christian Pulisic","Bruno Fernandes","Andrej Kramarić","Aleksandar Mitrović","Aaron Ramsey","Thomas Partey","Robert Lewandowski (captain","Raheem Sterling","Lionel Messi (captain","Kwon Chang-hoon","Junior Hoilett","Hassan Al-Haydos (captain","Cristiano Ronaldo (captain","André Silva","André Ayew (captain","Alireza Jahanbakhsh","Ángel Di María","Wahbi Khazri","Vincent Aboubakar (captain","Thomas Müller","Takumi Minamino","Leon Goretzka","Krzysztof Piątek","Karim Benzema","Jordan Ayew","Harry Kane (captain","Ferran Torres","Cyle Larin","Arkadiusz Milik","Antoine Griezmann","Xherdan Shaqiri","Son Heung-min (captain","Salem Al-Dawsari","Olivier Giroud","Michy Batshuayi","Lautaro Martínez","Kevin De Bruyne","Karim Ansarifard","Jonathan David","Hwang Ui-jo","Eric Maxim Choupo-Moting","Edinson Cavani","Eden Hazard (captain"
RETURN id(v0), id(v1)
I20230109 02:52:37.810698 2224521 exec_ngql.cc:55] session execute failed, statment: MATCH (v0)-[:belongto]-(v1)
WHERE id(v0) == "Álvaro Morata","Romelu Lukaku","Neymar","Naïm Sliti","Mehdi Taremi","Mario Götze","Kylian Mbappé","Kasper Dolberg","Jordan Morris","Joel Campbell","Ivan Perišić","Enner Valencia (captain","Christian Eriksen","Alphonso Davies","Ali Assadalla","Akram Afif","İlkay Gündoğan","Youssef En-Nesyri","Serge Gnabry","Sardar Azmoun","Richarlison","Raúl Jiménez","Mohammed Muntari","Almoez Ali","Memphis Depay","Marcus Rashford","Luis Suárez","Lucas Cavallini","Leroy Sané","Karl Toko Ekambi","Hirving Lozano","Haris Seferovic","Hakim Ziyech","Gareth Bale (captain","Gabriel Jesus","Dušan Tadić (captain","Christian Pulisic","Bruno Fernandes","Andrej Kramarić","Aleksandar Mitrović","Aaron Ramsey","Thomas Partey","Robert Lewandowski (captain","Raheem Sterling","Lionel Messi (captain","Kwon Chang-hoon","Junior Hoilett","Hassan Al-Haydos (captain","Cristiano Ronaldo (captain","André Silva","André Ayew (captain","Alireza Jahanbakhsh","Ángel Di María","Wahbi Khazri","Vincent Aboubakar (captain","Thomas Müller","Takumi Minamino","Leon Goretzka","Krzysztof Piątek","Karim Benzema","Jordan Ayew","Harry Kane (captain","Ferran Torres","Cyle Larin","Arkadiusz Milik","Antoine Griezmann","Xherdan Shaqiri","Son Heung-min (captain","Salem Al-Dawsari","Olivier Giroud","Michy Batshuayi","Lautaro Martínez","Kevin De Bruyne","Karim Ansarifard","Jonathan David","Hwang Ui-jo","Eric Maxim Choupo-Moting","Edinson Cavani","Eden Hazard (captain"
RETURN id(v0), id(v1)
errorCode: -1004, errorMsg: SyntaxError: syntax error near `E id(v0)'
I am following the docs chapter 4, while it seems something went wrong anyway.
I tried to use the GO clause instead of MATCH in the second task, but it complained of similar errors.
Could anyone help answer at where I could be wrong?
I have this dataset:
text sentiment
randomstring positive
randomstring negative
randomstring netrual
random mixed
Then if I run a countmap i have:
"mixed" -> 600
"positive" -> 2000
"negative" -> 3300
"netrual" -> 780
I want to random sample from this dataset in a way that I have records of all smallest class (mixed = 600) and the same amount of each of other classes (positive=600, negative=600, neutral = 600)
I know how to do this in pandas:
df_teste = [data.loc[data.sentiment==i]\
.sample(n=int(data['sentiment']
.value_counts().nsmallest(1)[0]),random_state=SEED) for i in data.sentiment.unique()]
df_teste = pd.concat(df_teste, axis=0, ignore_index=True)
But I am having a hard time to do this in Julia.
Note: I don´t want to hardcode which of the class is the lowest one, so I am looking for a solution that infer that from the countmap or freqtable, if possible.
Why do you want a countmap or freqtable solution if you seem do want to use a data frame in the end?
This is how you would do this with DataFrames.jl (but without StatsBase.jl and FreqTables.jl as they are not needed for this):
julia> using Random
julia> using DataFrames
julia> df = DataFrame(text = [randstring() for i in 1:6680],
sentiment = shuffle!([fill("mixed", 600);
fill("positive", 2000);
fill("ngative", 3300);
fill("neutral", 780)]))
6680×2 DataFrame
Row │ text sentiment
│ String String
──────┼─────────────────────
1 │ R3W1KL5b positive
2 │ uCCpNrat ngative
3 │ fwqYTCWG ngative
⋮ │ ⋮ ⋮
6678 │ UJiNrlcw ngative
6679 │ 7aiNOQ1o neutral
6680 │ mbIOIQmQ ngative
6674 rows omitted
julia> gdf = groupby(df, :sentiment);
julia> min_len = minimum(nrow, gdf)
600
julia> df_sampled = combine(gdf) do sdf
return sdf[randperm(nrow(sdf))[1:min_len], :]
end
2400×2 DataFrame
Row │ sentiment text
│ String String
──────┼─────────────────────
1 │ positive O0QsyrJZ
2 │ positive 7Vt70PSh
3 │ positive ebFd8m4o
⋮ │ ⋮ ⋮
2398 │ neutral Kq8Wi2Vv
2399 │ neutral yygOzKuC
2400 │ neutral NemZu7R3
2394 rows omitted
julia> combine(groupby(df_sampled, :sentiment), nrow)
4×2 DataFrame
Row │ sentiment nrow
│ String Int64
─────┼──────────────────
1 │ positive 600
2 │ ngative 600
3 │ mixed 600
4 │ neutral 600
If your data is very large and you need the operation to be very fast there are more efficient ways to do it, but in most situations this should be fast enough and the solution does not require any extra packages.
I am using Julia CSV and I am trying to read data with DateTime in the form 10/17/2012 12:00:00 AM i tried
dfmt = dateformat"mm/dd/yyyy HH:MM:SS"
data =CSV.File("./Fremont_Bridge_Bicycle_Counter.csv", dateformat=dfmt) |> DataFrame
println(first(data,8))
but the thing is that I think the AM and PM makes the string not recognized as a date can someone help show how to pass this as a date
You can use the p specifier, which matches AM or PM. With that, your date format would look like this:
dfmt = dateformat"mm/dd/yyyy HH:MM:SS p"
You can see that the parsing is correct:
julia> DateTime("10/17/2012 12:00:00 AM", dfmt)
2012-10-17T00:00:00
To see all the possible format characters, check out the docstring of Dates.DateFormat, which is accessible in the REPL through ?DateFormat.
With the file Fremont_Bridge_Bicycle_Counter.csv
N1, N2, fecha
hola, 3, 10/03/2020 10:30:00
pepe, 5, 10/03/2020 11:40:50
juan, 5, 03/04/2020 20:10:12
And with the julia code:
using DataFrames, Dates, CSV
dfmt = dateformat"mm/dd/yyyy HH:MM:SS p"
data =CSV.File("./Fremont_Bridge_Bicycle_Counter.csv", dateformat=dfmt) |> DataFrame
println(first(data,8))
It gives the right result:
3×3 DataFrame
│ Row │ N1 │ N2 │ fecha │
│ │ String │ Int64 │ DateTime │
├─────┼────────┼───────┼─────────────────────┤
│ 1 │ hola │ 3 │ 2020-10-03T10:30:00 │
│ 2 │ pepe │ 5 │ 2020-10-03T11:40:50 │
│ 3 │ juan │ 5 │ 2020-03-04T20:10:12 │
I am prettry new to Julia and I am just playing around, and suddenly
the following code starts throwing errors, but it has worked in the past.
using SQLite
db = SQLite.DB("db")
data = SQLite.Query(db,"SELECT * FROM d")
throws:
ERROR: LoadError: MethodError: no method matching
SQLite.Query(::SQLite.DB, ::String)
can someone please enlighten me wha the problem is? Thank you.
I also tried with lower case: query.
Here is a short MWE of differences using SQLLite (v0.9.0 vs v1.0.0) with the current Julia version (1.3.1).
You do not have the table so you need to create it first:
using SQLite
using DataFrames
db = SQLite.DB("db")
# v0.9.0
SQLite.Query(db,"CREATE TABLE d (col1 INT, col2 varchar2(100))")
# v1.0.0
DBInterface.execute(db,"CREATE TABLE d (col1 INT, col2 varchar2(100))")
Now you can check if the table exits:
julia> SQLite.tables(db) |> DataFrame
1×1 DataFrames.DataFrame
│ Row │ name │
│ │ String⍰ │
├─────┼─────────┤
│ 1 │ d │
Let's insert some rows (note how one should sepearate data from SQL code via precompiled statements):
stmt = SQLite.Stmt(db, "INSERT INTO d (col1, col2) VALUES (?, ?)")
#v0.9.0
SQLite.execute!(stmt; values=(1, "Hello world"))
SQLite.execute!(stmt; values=(2, "Goodbye world"))
#v1.0.0
DBInterface.execute(stmt, (1, "Hello world"))
DBInterface.execute(stmt, (2, "Goodbye world"))
Now let us get the data
v0.9.0
julia> data = SQLite.Query(db,"SELECT * FROM d") |> DataFrame
3×2 DataFrame
│ Row │ col1 │ col2 │
│ │ Int64⍰ │ String⍰ │
├─────┼────────┼───────────────┤
│ 1 │ 1 │ Hello world │
│ 2 │ 2 │ Goodbye world │
v1.0.0
julia> data = DBInterface.execute(db, "select * from d") |> DataFrame
3×2 DataFrame
│ Row │ col1 │ col2 │
│ │ Int64⍰ │ String⍰ │
├─────┼────────┼───────────────┤
│ 1 │ 1 │ Hello world │
│ 2 │ 2 │ Goodbye world │
I have SecurityLog with fields like DstIP_s and want to display records matching my trojanDst table
let trojanDst = datatable (DstIP_s:string)
[ "1.1.1.1","2.2.2.2","3.3.3.3"
];
SecurityLog |
| join trojanDst on DstIP_s
I am getting query could not be parsed error ?
The query you posted has a redundant pipe (|) before the join.
From an efficiency standpoint, make sure the left side of the join is the smaller one, as suggested here: https://learn.microsoft.com/en-us/azure/kusto/query/best-practices#join-operator
This is too long for a comment. As #Yoni L pointed the problem is doubled pipe operator.
For anyone with SQL background join may be a bit counterintuitive(in reality it is kind=innerunique):
JOIN operator:
kind unspecified, kind=innerunique
Only one row from the left side is matched for each value of the on
key. The output contains a row for each match of this row with rows
from the right.
Kind=inner
There's a row in the output for every combination of matching rows
from left and right.
let t1 = datatable(key:long, value:string)
[
1, "a",
1, "b"
];
let t2 = datatable(key:long, value:string)
[
1, "c",
1, "d"
];
t1| join t2 on key;
Output:
┌─────┬───────┬──────┬────────┐
│ key │ value │ key1 │ value1 │
├─────┼───────┼──────┼────────┤
│ 1 │ a │ 1 │ c │
│ 1 │ a │ 1 │ d │
└─────┴───────┴──────┴────────┘
Demo
SQL style JOIN version:
let t1 = datatable(key:long, value:string)
[
1, "a",
1, "b"
];
let t2 = datatable(key:long, value:string)
[
1, "c",
1, "d"
];
t1| join kind=inner t2 on key;
Output:
┌─────┬───────┬──────┬────────┐
│ key │ value │ key1 │ value1 │
├─────┼───────┼──────┼────────┤
│ 1 │ b │ 1 │ c │
│ 1 │ a │ 1 │ c │
│ 1 │ b │ 1 │ d │
│ 1 │ a │ 1 │ d │
└─────┴───────┴──────┴────────┘
Demo
There are many join types in KQL such as innerunique, inner, leftouter, rightouter, fullouter, anti and more. here you can find the full list