Ibis

Some catchy subtitle about Python and SQL

Important legal information

The information presented here is offered for informational purposes only and should not be used for any other purpose (including, without limitation, the making of investment decisions). Examples provided herein are for illustrative purposes only and are not necessarily based on actual data. Nothing herein constitutes: an offer to sell or the solicitation of any offer to buy any security or other interest; tax advice; or investment advice. This presentation shall remain the property of Two Sigma Investments, LP (“Two Sigma”) and Two Sigma reserves the right to require the return of this presentation at any time.

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Copyright © 2018 TWO SIGMA INVESTMENTS, LP. All rights reserved

About me

  • Studied neuroscience
  • @fb 2016 to 2017
  • Software engineer at Two Sigma
  • Open source data things
  • Not a fan of string programming

EXPLAIN Plan

  • What's an ibis?
  • Why ibis?
  • Demo
  • Design
  • Execution
  • Questions

UPSHOT

  • SQL + Python for analytics
  • String programming

What's an ibis?

An ibis

Another ibis

ibis

  • Python library for analytic SQL
  • Embedded DSL
  • "Pandonic"

Example

In [32]: import ibis

In [33]: ibis.options.interactive = True

In [34]: con = ibis.sqlite.connect('crunchbase.db')

In [35]: con.list_tables()
Out[35]: ['acquisitions', 'companies', 'investments', 'rounds']

In [36]: t = con.table('companies')

In [37]: t.name.nunique()
Out[37]: 51063

In [38]: t.region.nunique()
Out[38]: 1034

In [39]: (t.groupby('region')
    ...:   .aggregate(topk=t.funding_rounds.count())
    ...:   .sort_by(ibis.desc('topk'))
    ...:   .head())
    ...:
Out[39]:
          region  topk
0    SF Bay Area  7165
1           None  5071
2  New York City  2724
3         Boston  1906
4         London  1679

Why IBIS?

SQl & Productivity

SELECT *,
       acq_ipos / num_investments::float AS acq_rate
FROM (
  SELECT
    CASE
      WHEN i.investor_name IS NULL THEN 'NO INVESTOR'
      ELSE i.investor_name
    END AS "Investor name",
    COUNT(DISTINCT c.permalink) AS num_investments,
    COUNT(
      DISTINCT
        CASE
          WHEN c.status IN ('ipo', 'acquired') THEN
            c.permalink
          ELSE NULL
        END
    ) AS acq_ipos
  FROM crunchbase_companies
  LEFT JOIN crunchbase_investments
    ON c.permalink = i.company_permalink
  GROUP BY 1
  ORDER BY 2 DESC
) a
SELECT *,
       acq_ipos / num_investments::float AS acq_rate
FROM (
  SELECT
    CASE
      WHEN i.investor_name IS NULL THEN 'NO INVESTOR'
      ELSE i.investor_name
    END AS "Investor name",
    COUNT(DISTINCT c.permalink) AS num_investments,
    COUNT(
      DISTINCT
        CASE
          WHEN c.status IN ('ipo', 'acquired') THEN
            c.permalink
          ELSE NULL
        END
    ) AS acq_ipos
  FROM crunchbase_companies AS c
  LEFT JOIN crunchbase_investments AS i
    ON c.permalink = i.company_permalink
  GROUP BY 1
  ORDER BY 2 DESC
) a

SQl & Productivity

Lines of SQL Written

Productivity

WHY SQL?

WHY SQL?

SQL

PYTHON

PYTHON

  • Good
    • Easy to learn/use
    • Rich ecosystem
  • Not so good
    • Analytics limited in scale
    • SQL interaction

MAKE SQL PRODUCTIVE FROM PYTHON

"I knoW, I'll just generate STRINGS"

"STRINGLY" Typed

"""
SELECT *,
       acq_ipos/num_investments::FLOAT AS acquitision_rate
FROM (
  SELECT CASE WHEN i.investor_name IS NULL THEN 'NO INVESTOR'
              ELSE i.investor_name END "Investor name",
          {agg}(DISTINCT c.permalink) AS number_of_investments,
          COUNT(DISTINCT
          {case_with_conditions}
          AS {my_name}
  FROM {}
  LEFT JOIN {}
  ON c.permalink = i.company_permalink
  GROUP BY 1
  ORDER BY 2 DESC
) a
""".format(
    generate_left_part_of_join(use_templating='obviously'),
    generate_right_join_v2(sometimes_do_not_use_templating='what else would I do'),
    agg=next(agg_generator()),
    case_with_conditions='CASE {} END'.format(
        'WHEN {} THEN {}'.format(k, v) for k, v in conditions
    ),
    my_name='acqusitions or whatever'
)

"I knoW, I'll just generate STRINGS"

"I knoW, I'll just generate STRINGS write pandas-like code that type checks and eventually generates strings"

recap

  • Python
  • Fail fastly
  • Convenience
  • Performance

DEMO time

Potential drawbacks

  • Performance
  • Abstraction
  • Features

DESIGN

DESIGN PRinCIPLES

  • Separate API from operation
  • Check types ASAP
t.string_col + 1.0

API

Impl

class Expr:
    def __init__(self, impl):
        self.impl = impl

    def op(self):
        return self.impl


class StringValue(Expr):
    def concat(self, other):
        return ops.Concat(
            self, other
        ).to_expr()
class Node:
    def to_expr(self):
        output_type = self.output_type()
        return output_type(self)


class Concat(ops.Node):
    left = Arg(rules.string)
    right = Arg(rules.string)
    
    def output_type(self):
        return rules.shape_like(
            self.args,
            dtype=dt.string
        )
  • Exprs are the API
  • Based on element type, shape
  • Used for type checking
  • Implementation details
  • Know how to make exprs
  • Used for compilation

DESIGN - SEPARATION

DESIGN - operations

  • Nodes (Ops) are implementation
  • Holds arguments
  • Determines the Expr type
class Repeat(ValueOp):
    arg = Arg(rlz.string)
    times = Arg(rlz.integer)

    def output_type(self):
        return rlz.shape_like(
            self.args, dt.string
        )
class StringJoin(ValueOp):
    sep = Arg(rlz.string)
    arg = Arg(
        rlz.list_of(
            rlz.string, min_length=1
        )
    )

    def output_type(self):
        return rlz.shape_like(
            tuple(self.flat_args()),
            dt.string
        )

Execution phases

  • Expression graph construction
  • Compilation
  • Execution

EXPReSSION GRAPH

t.bigint_col.bitwise_and(where=(t.bigint_col == 10) | (t.bigint_col == 40))

COmpilation

t.bigint_col.bitwise_and(where=(t.bigint_col == 10) | (t.bigint_col == 40))
BITWISE_AND(t.bigint_col) WHERE (t.bigint_col = 10 OR t.bigint_col = 40)
BITWISE_AND({}.bigint_col) WHERE ({})
BITWISE_AND({}) WHERE ({})
BITWISE_AND(t.bigint_col) WHERE ({})
BITWISE_AND(t.bigint_col) WHERE ({} OR {})
BITWISE_AND(t.bigint_col) WHERE ({} = {} OR {})
BITWISE_AND(t.bigint_col) WHERE (t.bigint_col = {} OR {})
BITWISE_AND(t.bigint_col) WHERE (t.bigint_col = 10 OR {})
BITWISE_AND(t.bigint_col) WHERE (t.bigint_col = 10 OR t.bigint_col = {})

Execution

  • Give string to driver
  • Convert results into pandas things

lots to do

QUESTIONS?

Ibis

By Phillip Cloud

Ibis

Ibis is a Python library for making it easy to work with different flavors of SQL engines, with a focus on big data systems such as Apache Impala, Google BigQuery and others.

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