The aws_ext python package contains some useful functions (built on top of boto3) for managing some aws services. At the moment only some utilities for the Aws Glue Data catalog Installation pip install aws_ext Usage import boto3 import aws_ext session = boto3.session.Session() GLUE from aws_ext import glue_databases glue_client = session.client("glue") Extracting tables with (too) many versions glue_databases.get_tables_with_many_versions(glue_client, database_name="mydb", threshold=1) Deleting old tables versions glue_databases.delete_old_tables_versions(glue_client, database_name="mydb", keep=1, dryrun=True)

My #prolog solution for “Where is gold?” proposed by dmcommunity.org challenge Jun 2021: 0 means empty, 1 means gold ?- solution(Box1,Box2,Box3). Box1 = Box3, Box3 = 0, Box2 = 1. below the code :-use_module(library(clpfd)). sentence1( 1,_Box2,_Box3). not_sentence1(0,_Box2,_Box3). sentence2( _Box1,0,_Box3). not_sentence2(_Box1,1,_Box3). sentence3( 0,_Box2,_Box3). not_sentence3(1,_Box2,_Box3). true_only_one_sentence(Box1, Box2, Box3):- ( sentence1(Box1,Box2,Box3), not_sentence2(Box1,Box2,Box3), not_sentence3(Box1,Box2,Box3) ) ; ( not_sentence1(Box1,Box2,Box3), sentence2(Box1,Box2,Box3), not_sentence3(Box1,Box2,Box3) ) ; ( not_sentence1(Box1,Box2,Box3), not_sentence2(Box1,Box2,Box3), sentence3(Box1,Box2,Box3) ). solution(Box1, Box2, Box3):- Box1 in 0..1, /* 0 empty, 1 gold */ Box2 in 0.

Qliksense datasource for GraphQL Apollo server apollo-datasource-qliksense is a Qliksense datasource for the GraphQL Apollo server. “Apollo is the industry-standard GraphQL implementation, providing the data graph layer that connects modern apps to the cloud.” Installation npm install apollo-datasource-qliksense Usage Below a very basic sample File index.js file: const resolvers = require('./resolvers'); const { ApolloServer, gql } = require('apollo-server'); const { makeExecutableSchema } = require('graphql-tools'); const { QliksenseDataSource } = require('apollo-datasource-qliksense'); const qliksense = new QliksenseDataSource("https://myqlikserver.

qSense qsense is an python library and command line tool for qliksense Some useful commands export_remove_old_apps Export (published or passing any other filter) applications to qvd files qsense export_delete_old_apps qliksense.redaelli.org ~/certificates/client.pem --target_path '/tmp' --modified_days=300 --last_reload_days=300 deallocate_analyzer_licenses_for_professionals Deallocate analyzer license fom users with a professional license qsense deallocate_analyzer_licenses_for_professionals qliksense.redaelli.org ~/certificates/client.pem --nodryrun delete_removed_exernally_users Delete users that were removed externally (from active directory?) qsense delete_removed_exernally_users qliksense.redaelli.org ~/certificates/client.pem GROUP --nodryrun

My #swi #prolog solution for “Benchmark ‘Medical Services’” proposed by dmcommunity.org challenge Feb 2021 can be tested running a public docker image docker run -p 8888:8888 -d --name matteoredaelli/dmcommunity_org_2021_02:latest wget https://github.com/DMCommunity/dmcommunity_shared/raw/master/MedicalServices.json curl -XPOST -d @MedicalServices.json -H "Accept: application/json" -H "Content-type: application/json" http://localhost:8888/many The decision table is a set of prolog facts like decision_table('Office','acupuncture','PL123','L','Y','N','2015-01-01','2023-12-31','N','N','N'). decision_table('Outpatient','acupuncture','PL123','L','Y','N','2015-01-01','2023-12-31','N','N','N'). decision_table('Inpatient','acupuncture','PL123','L','N','N','2015-01-01','2023-12-31','N','N','N'). The core of the solution is inside the rules.pl file parse_input(json([placeOfService=PlaceOfService, type=Type, plan=Plan, groupSize=GroupSize, inNetwork=InNetwork, isCovered=IsCovered, dateOfService=DateOfService, coveredInFull=_, copay=_, coInsurance=_]), json([placeOfService=PlaceOfService, type=Type, plan=Plan, groupSize=GroupSize, inNetwork=InNetwork, isCovered=IsCovered, dateOfService=DateOfService, coveredInFull=CoveredInFull, copay=Copay, coInsurance=CoInsurance])):- decision_table(PlaceOfService, Type, Plan, GroupSize, InNetwork, IsCovered, DateOfService1, DateOfService2, CoveredInFull, Copay, CoInsurance), %% check date: must be between the two dates in decision_table atom_string(DateOfService, DateOfServiceString), atom_string(DateOfService1, DateOfService1String), atom_string(DateOfService2, DateOfService2String), DateOfService1String @=< DateOfServiceString, DateOfServiceString @=< DateOfService2String.

Below my #swi #prolog solution for “Virtual Chess tournament” proposed by dmcommunity.org challenge Dec 2020 Player 1 is Fischer, player 2 is Kasparov and player 3 is Karpov. I can find two solutions (Score1,Wins1,Draws1,Loses1 are Fischer’s score, number of wins, draws and loses,..) is: ?- time(solution([[Score1,Wins1,Draws1,Loses1],[Score2,Wins2,Draws2,Loses2],[Score3,Wins3,Draws3,Loses3]])). % 1,297,058 inferences, 0.136 CPU in 0.136 seconds (100% CPU, 9557549 Lips) Score1 = 15, Wins1 = 4, Draws1 = 7, Loses1 = Draws2, Draws2 = 3, Score2 = 13, Wins2 = 5, Loses2 = 6, Score3 = 14, Wins3 = Loses3, Loses3 = 2, Draws3 = 10 ; % 152,142 inferences, 0.

Below my #prolog solution for “Calculator with Two Buttons” proposed by dmcommunity.org challenge Nov 2020 swipl nov2020.pl ?- shortest_path(0,5034,Path), length(Path,Len), findall(Op,member([_,Op,_], Path), Ops). Path = [[0,+,1],[1,+,2],[2,+,3],[3,+,4],[4,+,5],[5,*,50],[50,*,500],[500,+,501],[501,+,502],[502,+,503],[503,*,5030],[5030,+,5031],[5031,+,5032],[5032,+,5033],[5033,+,5034]], Len = 15, Ops = [+,+,+,+,+,*,*,+,+,+,*,+,+,+,+]. Below my nov2020.pl script :- use_module(library(clpfd)). shortest_path(From, To, Path):- From #>= 0, shortest_path(From, To, 0, Path). shortest_path(From, To, MaxDepth, Path):- path(From, To, MaxDepth, Path),! . shortest_path(From, To, MaxDepth, Path):- MaxDepth1 #= MaxDepth + 1, shortest_path(From, To, MaxDepth1, Path). path(From, To, _MaxDepth, [[From, Op, To]]):- step(From, Op, To).

It is easy, you just need to add to the configuration file /var/lib/postgresql/data/pg_hba.conf host all all 0.0.0.0/0 ldap ldapserver="myldapserver" ldapbasedn="OU=USERS,DC=group,DC=redaelli,DC=org" ldapbinddn="CN=matteo,OU=USERS,DC=group,DC=redaelli,DC=org" ldapbindpasswd="MySillyPwd" ldapsearchattribute="sAMAccountName" ldapscheme="ldaps" And inside your database yu need to create a role for the Active director users and then grant them to the required databases.

setup In these examples I use kerberos authentication but you could also use basic authentication with -D and -w options For semplicity I put common options into the environment and I have created an alias export LDAP_OPTS="-h myldap.group.example.com -LLL -Y GSSAPI -b dc=group,dc=example,dc=com -o ldif-wrap=no " alias adsearch="/usr/bin/ldapsearch ${LDAP_OPTS}" Extract locked users adsearch “(&(objectCategory=Person)(objectClass=User)(lockoutTime>=1))” dn Expired passwords date -d “1601/1/1+$(expr 132469210596077795 / 10000000 )Seconds” “(&(objectclass=user)(objectcategory=person)(!pwdlastset=0)(pwdlastset<={date})(!userAccountControl:1.2.840.113556.1.4.803:=65536))” Extracting the flat list of members of a group How to recursively retreiving teh list of members of a group

Complex web sites read and write data from/to several backend systems using different interfaces (sql, soap , rest, rpc,..). But it could be simpler and useful to create a single endpoint and interface for all the backends. With GraphQL the frontend applications get from the backends only the list of fields they need and do not receive the static list of the fields provided by the soap/rest services. I played with graphql and Walmart lacinia implementing one GraphQL backend for LDAP/Active Directory and one for Qliksense Repository rest api.