Blaze: an interface to all the things

about me

  • MA Psychology
    • Computational Neuroscience
  • Pandas core dev
  • blaze, odo, et al @ContinuumIO

How do i interact with all the things?

lists of things

In [79]: gb = toolz.groupby('passenger_count',
   ....:                    (x for x in dicts if x['passenger_count'] < 6))

In [80]: {k: sum(y['fare_amount'] for y in v) / len(v) for k, v in gb.items()}
Out[80]:
{0: 37.166666666666664,
 1: 12.068150330050502,
 2: 11.974952289774347,
 3: 11.876616991857428,
 4: 12.034261638063919,
 5: 11.938438450392496}

databases

In [91]: d = sa.Table('nyc',
   ....:              sa.MetaData(bind=sa.create_engine('postgresql://localhost')),
   ....:              autoload=True)

In [92]: sel = sa.select([d.c.passenger_count, sa.func.avg(d.c.fare_amount)])

In [93]: result = sel.where(d.c.passenger_count < 6).group_by(d.c.passenger_count)

In [94]: result.execute().fetchall()
Out[94]:
[(4, 12.8474578145854),
 (5, 12.4340166757418),
 (1, 12.1857106698121),
 (2, 13.0082269224776),
 (3, 12.620676158505),
 (0, 23.5708381330686)]

kdb

In [4]: from qpython.qconnection import QConnection

In [5]: c = QConnection(port=5000, host='localhost', pandas=True)

In [6]: c.open()

In [7]: r = c.sync('select avg trip_time_in_secs by passenger_count from trip')

In [8]: r.head()
Out[8]:
                 trip_time_in_secs
passenger_count
0                       122.071500
1                       806.607092
2                       852.223353
3                       850.614843
4                       885.621065

Yay string programming!

spark

In [21]: sdf[sdf.passenger_count < 6].groupby('passenger_count').agg({'fare_amount': 'avg'}).collect()
Out[21]:
[Row(passenger_count=0, AVG(fare_amount)=50.0),
 Row(passenger_count=1, AVG(fare_amount)=12.085692890743172),
 Row(passenger_count=2, AVG(fare_amount)=11.904175317185697),
 Row(passenger_count=3, AVG(fare_amount)=11.670360110803324),
 Row(passenger_count=4, AVG(fare_amount)=12.041448058761805),
 Row(passenger_count=5, AVG(fare_amount)=12.00365975807073)]

Dataframes

In [37]: df[df.passenger_count < 6].groupby('passenger_count').fare_amount.mean()
Out[37]:
passenger_count
0    37.166667
1    12.068150
2    11.974952
3    11.876617
4    12.034262
5    11.938438
Name: fare_amount, dtype: float64

let's drive them all!

blaze =
exprs+

all the things

10,000 foot view

expressions

  • Separate data from computation
  • Write down what you want to compute without worrying about how it will be computed

use case: drive Q from Python

Q overview

Who's hearD of q?

for something other than a binary store.

Who's used q?

the language

  • Written by Arthur Whitney
  • Derived from and written in K
  • K is similar to APL
    • Tons of operators
  • Parses from right to left
    • Unless you're in qsql land

q demo!

Pray to the q gods and the demo gods

The database

  • Column store
  • Handles null values for all types
  • Nice support for slowly changing string values (symbols)
  • Excellent time series support
  • Support for different storage formats that scale to huge tables
  • Matrix math

The GOOD

  • Column store
  • Extremely fast queries
  • Concise querying via qsql
  • Great time series support
  • Nice foreign key syntax

The BAD

  • Hardly anyone knows q
  • Operators > Names
  • Cryptic error messages
  • Large programs are unreadable by anyone except the q gods
    • q gods never make errors
  • Strange API breaks
    • dev to sdev
  • Hard to apply traditional database knowledge

THE ugly

q)x:"racecar"
q)n:count x
q)ispal:all{[x;n;i]x[i]=x[n-i+1]}[x;n]each til _:[n%2]+1
q)ispal x
1b
q)1 % "cat"
0.01010101 0.01030928 0.00862069
q)-1["foo"]
foo
-1
q)1["bar"]
bar1

please q gods, give me whitespace

kDBPY

the stack

  • A SQLAlchemy dialect
  • That can be driven from Blaze
    • With odo for moving data around
  • Using qpython to talk to a q process 

sqlalchemy Dialect

class QDialect(Dialect):
    def visit_select(self, select, **kwargs):
        # ...

    def visit_insert(self, insert, **kwargs):
        # ...

    # and so on
  • Generate (some variant of SQL) using the visitor pattern

sqlalchemy Dialect

>>> import sqlalchemy as sa
>>> engine = sa.create_engine('q://localhost/path/to/db')
>>> meta = sa.MetaData(bind=engine)
>>> t = sa.Table('t', meta, autoload=True)
>>> expr = sa.select([t.c.name, sa.func.avg(t.c.amount)]).group_by(t.c.name)
>>> result = conn.execute().fetchall()  # <- a pandas DataFrame
  • Build up expressions

demo time!

wish/bug list

  • DDL for splayed and partitioned tables not yet ready for primetime
  • Foreign key support in blaze expressions
    • Very close to merging
  • Better time series support all around
  • Transparent insert to special table types
  • Python 3 support
  • Streaming results from q
  • Open source it!

thanks!

Blaze: An Interface to All the Thigns

By Phillip Cloud

Blaze: An Interface to All the Thigns

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