Distributed SQL Planner based on optiq as planner and python 2.7.
usage: jupiter.py [-h] [-n] [-j] SERVERS DB SQL
Use Optiq to get a relational plan from SQL statement.
positional arguments:
SERVERS Servers configuration file
DB SQLite database to use as schema source
SQL SQL query (quote it as a single string)
optional arguments:
-h, --help show this help message and exit
-n, --no-opt Convert SQL to a relational plan but do not optimize
-j, --json Output the result as JSON
The default configuration in config/servers uses a database of patients, that can be generated:
$ python db/genpatients.py
Database test.db created with 100000 patients
In order to optimize a query, execute jupiter.py using the name of the database that will be used to read the schema, and the query you want to optimize as parameters.
$ ./jupiter.py config/servers test.db 'SELECT * FROM hospital'
=== Parsed Tree ===
1: {u'cumulativeCost': {u'rows': 200000.0, u'cpu': 100001.0, u'io': 300000.0}, u'inputs': [u'0'], u'relOp': u'JdbcToEnumerableConverter', u'cols': [u'patient', u'disease', u'treatment'], u'rowCount': 100000.0, u'cost': {u'rows': 100000.0, u'cpu': 0.0, u'io': 0.0}, u'id': u'1'}
0: {u'cumulativeCost': {u'rows': 100000.0, u'cpu': 100001.0, u'io': 300000.0}, u'inputs': [], u'relOp': u'JdbcTableScan', u'cols': [u'patient', u'disease', u'treatment'], u'rowCount': 100000.0, u'cost': {u'rows': 100000.0, u'cpu': 100001.0, u'io': 300000.0}, u'table': [u'main', u'hospital'], u'id': u'0'}
=== Plans ===
[Plan cost=420.001 {H:400.001, H->*:20} nodes={0:H, 1:CL}]
=== Best Plan ===
[Plan cost=420.001 {H:400.001, H->*:20} nodes={0:H, 1:CL}]
1CL: {u'cumulativeCost': {u'rows': 200000.0, u'cpu': 100001.0, u'io': 300000.0}, u'inputs': [u'0'], u'relOp': u'JdbcToEnumerableConverter', u'cols': [u'patient', u'disease', u'treatment'], u'rowCount': 100000.0, u'cost': {u'rows': 100000.0, u'cpu': 0.0, u'io': 0.0}, u'id': u'1'} Profile(v=set([u'patient', u'disease', u'treatment']), e=set([]), iv=set([]), ie=set([]), sim=[])
0H: {u'cumulativeCost': {u'rows': 100000.0, u'cpu': 100001.0, u'io': 300000.0}, u'inputs': [], u'relOp': u'JdbcTableScan', u'cols': [u'patient', u'disease', u'treatment'], u'rowCount': 100000.0, u'cost': {u'rows': 100000.0, u'cpu': 100001.0, u'io': 300000.0}, u'table': [u'main', u'hospital'], u'id': u'0'} Profile(v=set([u'patient', u'disease', u'treatment']), e=set([]), iv=set([]), ie=set([]), sim=[])
The configuration file is located at config/servers. The configuration define for each server the tables, the cost for cpu, io, network-in and network-out and the authorization that other server have on each table.
There must be a server named CL which is the client on which the root of the relational algebra tree is executed.
The server named * is used as a default when a specific authorization for a server is not found.
[
{
"id": "CL",
"cpu": 0.002,
"io": 0.002,
"netin": 0.0,
"netout": 0.0,
"tables": []
},
{
"id": "H",
"cpu": 0.001,
"io": 0.001,
"netin": 0.0002,
"netout": 0.0002,
"tables": [
{
"name": "hospital",
"columns": ["patient", "disease", "treatment"],
"authorizations": [
{
"server": "H",
"v": ["patient", "disease", "treatment"],
"e": []
},
{
"server": "I",
"v": ["patient"],
"e": ["disease", "treatment"]
},
{
"server": "D",
"v": [],
"e": ["patient", "disease", "treatment"]
},
{
"server": "CL",
"v": ["patient", "disease", "treatment"],
"e": []
},
{
"server": "*",
"v": [],
"e": ["patient", "disease", "treatment"]
}
]
}
]
},
{
"id": "I",
"cpu": 0.0012,
"io": 0.0012,
"netin": 0.0005,
"netout": 0.0005,
"tables": [
{
"name": "insurance",
"columns": ["customer", "premium"],
"authorizations": [
{
"server": "H",
"v": [],
"e": ["customer", "premium"]
},
{
"server": "I",
"v": ["customer", "premium"],
"e": []
},
{
"server": "D",
"v": [],
"e": ["customer", "premium"]
},
{
"server": "CL",
"v": ["customer", "premium"],
"e": []
},
{
"server": "*",
"v": [],
"e": []
}
]
}
]
},
{
"id": "D",
"cpu": 0.002,
"io": 0.0008,
"netin": 0.0005,
"netout": 0.0005,
"tables": [
{
"name": "dhs",
"columns": ["SSN", "employer", "salary"],
"authorizations": [
{
"server": "H",
"v": [],
"e": ["SSN", "employer", "salary"]
},
{
"server": "I",
"v": ["SSN", "employer"],
"e": ["salary"]
},
{
"server": "D",
"v": ["SSN", "employer", "salary"],
"e": []
},
{
"server": "CL",
"v": ["SSN", "employer", "salary"],
"e": []
},
{
"server": "*",
"v": [],
"e": ["SSN", "salary"]
}
]
}
]
}
]
- The names of the columns must be unique.
- It works with projections, single selections and aggregates.
Jupiter invokes the optiq planner to generate a plan that is optimal on a single machine, then jupiter analyzes which servers are allowed to execute the every relational node and it select the combination that satisfies the authorization constraints while minimizing the total cost (based on the configuration).
-
Join two tables and project columns:
./jupiter.py config/servers test.db 'SELECT patient, disease, premium FROM hospital AS H JOIN insurance AS I ON H.patient = I.customer' -
Join two tables, project columns and filter the results:
./jupiter.py config/servers test.db 'SELECT patient, disease, premium FROM hospital AS H JOIN insurance AS I ON H.patient = I.customer WHERE H.patient > 1000'
You can generate a SQLite TPC-H database. To do that check the README in the tpch folder.
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