Data profiling with Apache Calcite

Data profiling
with Apache Calcite
DataWorks Summit 2017
SAN JOSE, USA
2017/06/14
Julian Hyde

@julianhyde
SQL
Query planning
Query federation
OLAP
Streaming
Hadoop
ASF member
Original author of Apache Calcite
PMC Apache Arrow, Drill, Eagle, Kylin

Overview
Apache Calcite
Motivating problem: Automatically designing summary tables
What is data profiling?
Naive profiling algorithm
Improving the algorithm using sketches, parallelism, information theory
Applying data profiling to other problems

Apache Calcite
Apache top-level project since October, 2015
Query planning framework
➢ Relational algebra, rewrite rules
➢ Cost model & statistics
➢ Federation via adapters
➢ Extensible
Packaging
➢ Library
➢ Optional SQL parser, JDBC server
➢ Community-authored rules, adapters

Planning queries
MySQL
Splunk
join
Key: productId
group
Key: productName
Agg: count
filter
Condition:
action = 'purchase'
sort
Key: c desc
scan
scan
Table: products
select p.productName, count(*) as c
from splunk.splunk as s
join mysql.products as p
on s.productId = p.productId
where s.action = 'purchase'
group by p.productName
order by c desc
Table: splunk

Optimized query
MySQL
Splunk
join
Key: productId
group
Key: productName
Agg: count
filter
Condition:
action = 'purchase'
sort
Key: c desc
scan
scan
Table: splunk
Table: products
select p.productName, count(*) as c
from splunk.splunk as s
join mysql.products as p
on s.productId = p.productId
where s.action = 'purchase'
group by p.productName
order by c desc

Optimizing queries
Problem
10 TB database, disk with 1 GB/s throughput, and a query that reads 1 TB data.
Solutions
1. Sequential scan Query takes 1,000s.
2. Parallelize Spread the data over 100 disks in 25 machines. Query takes 10s.
3. Cache Keep the data in memory. 2nd query: 10ms. 3rd query: 10s.
4. Materialize Summarize the data on disk. All queries: 100ms.
5. Materialize + cache + adapt As above, building summaries on demand.

Optimizing data
A materialized view (“materialization”) is a table that contains the result of a
query. The DBMS maintains it, and uses it to answer queries on other tables.
Challenges:
● Design Which materializations to create?
● Populate Load them with data
● Maintain Incrementally populate when data changes
● Rewrite Transparently rewrite queries to use materializations
● Adapt Design and populate new materializations, drop unused ones
● Express Need a rich algebra, to model how data is derived
create materialized view EmpSummary as
select deptno, COUNT(*) as c, SUM(sal) as s
from Emp
group by deptno

Lattice
Key
z zipcode (43k)
s state (50)
g gender (2)
y year (5)
m month (12)
() 1
(z, s, g, y,
m) 912k
(s, g, y,
m) 6k
(z) 43k (s) 50 (g) 2 (y) 5 (m) 12
raw 1m
(y, m)
60
(g, y) 10
(z, s)
43.4k
(g, y, m)
120
Fewer than you would
expect, because 5m
combinations cannot
occur in 1m row table
Fewer than you
would expect,
because state
depends on zipcode

Algorithm: Design summary tables
Given a database with 30 columns, 10M rows. Find X summary tables with under
Y rows that improve query response time the most.
AdaptiveMonteCarlo algorithm [1]:
● Based on research [2]
● Greedy algorithm that takes a combination of summary tables and tries to
find the table that yields the greatest cost/benefit improvement
● Models “benefit” of the table as query time saved over simulated query load
● The “cost” of a table is its size
[1] org.pentaho.aggdes.algorithm.impl.AdaptiveMonteCarloAlgorithm
[2] Harinarayan, Rajaraman, Ullman (1996). “Implementing data cubes efficiently”

Lattice (optimized) () 1
(z, s, g, y,
m) 912k
(s, g, y,
m) 6k
(z) 43k (s) 50 (g) 2 (y) 5 (m) 12
(z, g, y,
m) 909k
(z, s, y,
m) 831k
raw 1m
(z, s, g,
m) 644k
(z, s, g,
y) 392k
(y, m)
60
(z, s)
43.4k
(z, s, g)
83.6k
(g, y) 10
(g, y, m)
120
(g, m)
24
Key
z zipcode (43k)
s state (50)
g gender (2)
y year (5)
m month (12)

Lattice (optimized) () 1
(z, s, g, y,
m) 912k
(s, g, y,
m) 6k
(z) 43k (s) 50 (g) 2 (y) 5 (m) 12
(z, g, y,
m) 909k
(z, s, y,
m) 831k
raw 1m
(z, s, g,
m) 644k
(z, s, g,
y) 392k
(y, m)
60
(z, s)
43.4k
(z, s, g)
83.6k
(g, y) 10
(g, y, m)
120
(g, m)
24
Key
z zipcode (43k)
s state (50)
g gender (2)
y year (5)
m month (12)
Aggregate Cost
(rows)
Benefit (query
rows saved)
% queries
s, g, y, m 6k 497k 50%
z, s, g 87k 304k 33%
g, y 10 1.5k 25%
g, m 24 1.5k 25%
s, g 100 1.5k 25%
y, m 60 1.5k 25%

Data profiling
Algorithm needs count(distinct a, b, ...) for each combination of attributes:
● Previous example had 25
= 32 possible tables
● Schema with 30 attributes has 230
(about 109
) possible tables
● Algorithm considers a significant fraction of these
● Approximations are OK
Attempts to solve the profiling problem:
1. Compute each combination: scan, sort, unique, count; repeat 230
times!
2. Sketches (HyperLogLog)
3. Sketches + parallelism + information theory (CALCITE-1616)

Sketches
HyperLogLog is an algorithm that computes
approximate distinct count. It can estimate
cardinalities of 109
with a typical error rate of
2%, using 1.5 kB of memory. [3][4]
With 16 MB memory per machine we can
compute 10,000 combinations of attributes
each pass.
So, we’re down from 109
to 105
passes.
[3] Flajolet, Fusy, Gandouet, Meunier (2007). "Hyperloglog: The analysis of a near-optimal cardinality estimation algorithm"
[4] https://github.com/mrjgreen/HyperLogLog

Given Expected cardinality Actual cardinality Surprise
(gender): 2 (state): 50 (gender, state): 100.0 100 0.000
(month): 12 (zipcode): 43,000 (month, zipcode): 441,699.3 442,700 0.001
(state): 50 (zipcode): 43,000 (state, zipcode): 799,666.7 43,400 0.897
(state, zipcode): 43,400
(gender, state): 100
(gender, zipcode): 85,995
(gender, state, zipcode): 86,799
= min(86,799, 892,234, 892,228)
83,567 0.019
● Surprise = abs(actual - expected) / (actual + expected)
● E(card (x, y)) = n . (1 - ((n - 1) / n) ^ p) n = card (x) * card (y), p = row count
Combining probability & information theory

Algorithm
Three ways “surprise” can help:
● If a cardinality is not
surprising, we don’t need to
store it -- we can derive it
● If a combination’s cardinality
is not surprising, it is unlikely
to have surprising children
● If we’re not seeing surprising
results, it’s time to stop
surprise_threshold := 1
queue := {singleton combinations} // (a), (b), ...
while queue is not empty {
batch := remove first 10,000 entries in queue
compute cardinality of each combination in batch
for each actual (computed) cardinality a {
e := expected cardinality of combination
s := surprise(a, e)
if s > surprise_threshold {
store combination and its cardinality
add child combinations to queue // (x, a), (x, b), ...
}
increase surprise_threshold
}
}

Algorithm progress and “surprise” threshold
Progress of algorithm
Rejected as not
sufficiently
surprising
Surprise
threshold rises
as algorithm
progresses
Singleton
combinations
are have surprise
= 1
Surprise thresold
rises after we
hve completed
the first batch

Hierarchies considered
harmful
Hierarchies are a feature of most OLAP systems
Does it makes sense to store (year, quarter,
month, date) and roll up to (year, quarter)?
No -- algorithm can deduce hierarchies; less
configuration means fewer mistakes
Summary optimizer naturally includes attributes
that don’t increase summary cardinality by much
Feel free to specify a “drill path” in slice & dice UI
True hierarchy
(year)
↑
(year, quarter)
↑
(year, quarter, month)
↑
(year, quarter, month, date)
Almost a hierarchy
(nation)
↑
(nation, state)
↑
(nation, state, zipcode)

Other applications of data profiling
Query optimization:
● Planners are poor at estimating selectivity of conditions after N-way join
(especially on real data)
● New join-order benchmark: “Movies made by French directors tend to have
French actors”
● Predict number of reducers in MapReduce & Spark
“Grokking” a data set
Identifying problems in normalization, partitioning, quality
Applications in machine learning?

Further improvements
● Build sketches in parallel
● Run algorithm in a distributed framework (Spark or MapReduce)
● Compute histograms
○ For example, Median age for male/female customers
● Seek out functional dependencies
○ Once you know FDs, a lot of cardinalities are no longer “surprising”
○ FDs occur in denormalized tables, e.g. star schemas
● Smarter criteria for stopping algorithm
● Skew/heavy hitters. Are some values much more frequent than others?
● Conditional cardinalities and functional dependencies
○ Does one partition of the data behave differently from others? (e.g. year=2005, state=LA)

Thank you!
https://issues.apache.org/jira/browse/CALCITE-1788
https://calcite.apache.org
@ApacheCalcite
@julianhyde

Data profiling with Apache Calcite

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