The document discusses big data analytics using a custom SQL engine, specifically highlighting the role of Apache Calcite for optimized query processing over various data sources. It covers the architecture of Zoomdata and the functions of the query planner, emphasizing the importance of query optimization techniques including both heuristic and cost-based approaches. Additionally, it addresses the challenges and advantages of implementing these systems in data analytics environments.

1. Big data analytics using a
custom SQL engine
1
Andrii Tsvielodub, Zoomdata
2018

2. Agenda
1. What is Zoomdata and why we need SQL
2. What a typical Query Planner looks like and what it does
3. What is Apache Calcite and how it works
4. How we started using Apache Calcite in Zoomdata
2

3. Who am I
● Java Developer
● 8+ years of experience
● 3+ years in Zoomdata
● Work in Query Engine team
3

4. What is
Zoomdata
4

5. 5

6. 6

7. 7
Cross-Database Queries

8. Zoomdata Architecture
DB
DB
Zoomdata
Query
Engine
8

9. Zoomdata Architecture
DB
DB
Zoomdata
Query
Engine
9

10. Zoomdata Architecture
10

11. Zoomdata Architecture
11

12. Zoomdata Architecture
DB
DB
Zoomdata
Query
Engine
12

13. Zoomdata Architecture
13
Source

14. Zoomdata Architecture
14
Source

15. Zoomdata Architecture
Relational Algebra | Query Plan Optimization
15
SQL
Query Pushdown | Federated Queries

16. Zoomdata Architecture

17. Apache Calcite
A Foundational Framework
for Optimized Query Processing
Over Heterogeneous Data Sources
17

18. Apache Calcite - Features
● Standard SQL
○ Industry-standard SQL parser, validator and JDBC driver.
● Query optimization
○ Represent your query in relational algebra, transform using planning rules,
and optimize according to a cost model using smart algorithms.
● Any data, anywhere
○ Connect to third-party data sources, browse metadata, and optimize by
pushing the computation to the data.
18

19. Apache Calcite - Used By
19

20. Query Planner
20

21. Process
Manager
Client Communication Manager
Relational Query Processor
Transactional Store Manager
Shared
Components and
Utilities
Admission
Control
Dispatch &
Scheduling
Local Client Protocols Remote Client Protocols
Access Methods Buffer Methods
Lock Manager Log Manager
Catalog
Manager
Memory
Manager
Admin.,
Monitoring &
Utilities
Replication &
Loading
Services
Batch Utilities
Query Parsing & Authorization
Query Rewrite
Query Optimizer
Plan Executor
DDL & Utility Processing
Joseph M. Hellerstein, Michael Stonebraker, James Hamilton. Architecture of a Database System. Foundations and Trends in Databases, 1, 2 (2007).
21

22. Process
Manager
Client Communication Manager
Relational Query Processor
Transactional Store Manager
Shared
Components and
Utilities
Admission
Control
Dispatch &
Scheduling
Local Client Protocols Remote Client Protocols
Access Methods Buffer Methods
Lock Manager Log Manager
Catalog
Manager
Memory
Manager
Admin.,
Monitoring &
Utilities
Replication &
Loading
Services
Batch Utilities
Query Parsing & Authorization
Query Rewrite
Query Optimizer
Plan Executor
DDL & Utility Processing
Joseph M. Hellerstein, Michael Stonebraker, James Hamilton. Architecture of a Database System. Foundations and Trends in Databases, 1, 2 (2007).
22

23. Query Planner Overview Agenda
● Parse SQL query
● Create an optimal logical plan of a query
● Create an optimal physical plan and execute
23

24. Query Planner Overview Agenda
● Parse SQL query
● Create an optimal logical plan of a query
● Create an optimal physical plan and execute
24

25. Sales
tx_id
product_id
sale_date
qty
seller_id
buyer_id
Products
product_id
name
description
price
manufacturer_id
*
1
25

26. What the duck?
select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
26

27. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
27

28. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
Scan
(sales)
Scan
(product)
28

29. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
Scan
(sales)
Scan
(product)
Join
(product_id)
Dataflow
29

30. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
Scan
(sales)
Scan
(product)
Join
(product_id)
Filter
(name = ‘...’)
Dataflow
30

31. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
Scan
(sales)
Scan
(product)
Join
(product_id)
Filter
(name = ‘...’)
Project
(sale_date, qty, price)
Dataflow
31

32. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
Scan
(sales)
Scan
(product)
Join
(product_id)
Filter
(name = ‘...’)
Project
(sale_date, qty, price)
Aggregate
(group = sale_day,
agg = sum(qty * price))
Dataflow
32

33. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
LogicalScan
(sales)
LogicalScan
(product)
LogicalJoin
(product_id)
LogicalFilter
(name = ‘...’)
LogicalProject
(sale_date, qty, price)
LogicalAggregate
(group = sale_day,
agg = sum(qty * price))
Dataflow
33

34. RelBuilder b = ...;
RelNode query = b
.scan("sales").as("s")
.scan("product").as("p")
.join(JoinRelType.INNER, "product_id")
.filter(
b.call(SqlStdOperatorTable.EQUALS,
b.field("p.name"),
b.literal("Giant Rubber Duck")))
.project(
b.call(SqlStdOperatorTable.CEIL,
b.field("s.sale_date"), b.literal("DAY")),
b.call(SqlStdOperatorTable.MULTIPLY,
b.field("s.qty"), b.field("p.price")),
"sale_day", "sale")
.aggregate(
b.groupKey("sale_day"),
b.sum(false, "sale_sum", b.field("sale")))
.build();
LogicalScan
(sales)
LogicalScan
(product)
LogicalJoin
(product_id)
LogicalFilter
(name = ‘...’)
LogicalProject
(sale_date, qty, price)
LogicalAggregate
(group = sale_day,
agg = sum(qty * price))
Dataflow
34

35. Query Planner Overview Agenda
● Parse SQL query
○ SQL query is parsed into a logical plan
○ Logical plan is an abstract model of a query
○ Calcite has classes to build a logical plan
○ You can create the plan even without a SQL query
35

36. Query Planner Overview Agenda
● Parse SQL query
● Create an optimal logical plan of a query
● Create an optimal physical plan and execute
36

37. 37

38. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
Scan
(sales)
Scan
(product)
Join
(product_id)
Filter
(name = ‘...’)
Project
(sale_date, qty, price)
Aggregate
(group = sale_day,
agg = sum(qty * price))
38

39. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
Scan
(sales)
Scan
(product)
Join
(product_id)
Filter
(name = ‘...’)
Project
(sale_date, qty, price)
Aggregate
(group = sale_day,
agg = sum(qty * price))
39

40. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
Scan
(sales)
Scan
(product)
Join
(product_id)
Filter
(name = ‘...’)
Project
(sale_date, qty, price)
Aggregate
(group = sale_day,
agg = sum(qty * price))
Project
(p_id, name,
price)
Project
(p_id, qty,
sale_date)
40

41. ● org.apache.calcite.plan.RelOptRule
● Matches a pattern in plan tree
Apache Calcite - Optimization Rules
● Can check additional conditions, modifies the plan
● Rules can be specified dynamically, based on query properties
41

42. Apache Calcite - Optimization Rules
42

43. Apache Calcite - Query Optimizer
Query Optimizer makes all the difference in a DB.
Two general categories:
● Naive heuristic optimizer
● Cost-based optimizer
43

44. Apache Calcite - Query Optimizer
● Rule-based/Naive heuristic
○ Fast
○ Good fit for rules that
guarantee improvement
○ Limited in plan space
exploration
○ Not all aspects of query can be
optimised, e.g. joins
reordering
44

45. Apache Calcite - Query Optimizer
● Cost-based
○ Iteratively explores all possible changes to the plan
○ Estimates the cost of plans
○ Chooses the cheapest one
○ Stops when search criteria is met
❗ Can take much time
❗ For some queries, it might be faster to
execute it than to find the optimal plan
45

46. Query Planner Overview Agenda
● Parse SQL query
● Create an optimal logical plan of a query
○ Relational Algebra is not that scary
○ Rules can make your plan better (but not always)
○ Calcite has lots of rules and you can write your own
○ There are multiple planning algorithms - simple and cost-based
○ Choose wisely
46

47. Query Planner Overview Agenda
● Parse SQL query
● Create an optimal logical plan of a query
● Create an optimal physical plan and execute
47

48. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
Scan
(sales)
Scan
(product)
Join
(product_id)
Filter
(name = ‘...’)
Project
(sale_date, qty, price)
Aggregate
(group = sale_day,
agg = sum(qty * price))
Project
(p_id, name,
price)
Project
(p_id, qty,
sale_date)
48

49. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
Scan
(sales)
Scan
(product)
SortMergeJoin
(product_id)
Filter
(name = ‘...’)
Project
(sale_date, qty, price)
HashAggregate
(group = sale_day,
agg = sum(qty * price))
Project
(p_id, name,
price)
Project
(p_id, qty,
sale_date)
49

50. Execution in DB
And then the physical plan is executed
50

51. Sales
tx_id
product_id
sale_date
qty
seller_id
buyer_id
Products
product_id
name
description
price
manufacturer_id
*
1
51

52. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
Scan
(sales)
Scan
(product)
Join
(product_id)
Filter
(name = ‘...’)
Project
(sale_date, qty, price)
Aggregate
(group = sale_day,
agg = sum(qty * price))
Project
(p_id, name,
price)
Project
(p_id, qty,
sale_date)
52

53. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
MongoTableScan
(sales)
JdbcTableScan
(product)
EnumerableJoin
(product_id)
JdbcFilter
(name = ‘...’)
EnumerableCalc
(sale_date, qty, price)
EnumerableAggregate
(group = sale_day,
agg = sum(qty * price))
JdbcProject
(p_id, name,
price)
MongoProject
(p_id, qty,
sale_date)
MongoTo
Enumerable
Converter
JdbcTo
Enumerable
Converter
53

54. select
CEIL(s.sale_date TO DAY) sale_day,
sum(s.qty*p.price) sale_sum
from sales s join product p
on p.product_id = s.product_id
where
p.name = 'Giant Rubber Duck'
group by sale_day
MongoTableScan
(sales)
JdbcTableScan
(product)
EnumerableJoin
(product_id)
JdbcFilter
(name = ‘...’)
EnumerableCalc
(sale_date, qty, price)
EnumerableAggregate
(group = sale_day,
agg = sum(qty * price))
JdbcProject
(p_id, name,
price)
MongoProject
(p_id, qty,
sale_date)
MongoTo
Enumerable
Converter
JdbcTo
Enumerable
Converter
54

55. Apache Calcite - Plan Execution
● Standard JDBC ResultSet
● Specialized Iterable that returns the data
55

56. Query Planner Overview Agenda
● Parse SQL query
● Create an optimal logical plan of a query
● Create an optimal physical plan and execute
○ It’s rules again
○ Calling Conventions in Calcite is cool
○ You can convert between conventions
○ Calling Convention knows how to execute
56

57. Stop. Demo time!
57

58. Apache Calcite - Stuff To Remember
● Calcite knows how to parse SQL and
optimize queries
● Physical nodes have Calling Convention,
it says how to execute the plan
● You can mix Calling Conventions in one
plan using Converter Nodes
58

59. Back to
Zoomdata
59

60. Zoomdata Architecture
DB
DB
Zoomdata
Query
Engine
60

61. Query Engine Architecture
Spark adapter
JD
BC
ZD
Rx
Query Engine
61
Connector
adapter

62. 62

63. So what?
● New features - faster
● Optimisations - more reliable
● SQL interop - cool
63

64. ● Steep learning curve
● Big complex codebase
● Obsolete/unfinished code
Apache Calcite - Contra
64

65. Apache Calcite - Pro
○ It really works
○ Friendly community
○ Constantly improving and evolving
○ Good place to start
65

66. Takeaways
66

67. The Theory
● SQL Query is parsed into a Logical Plan
- an abstract model of the query
● Logical Plan is optimised with special rules,
based on relational algebra
● Logical Plan is then converted to a Physical Plan
● Physical Plan can be executed to return the data
67

68. The Theory
● Query optimization is fun
● Relational Algebra is not scary at all
● Solid basis for optimization and execution
● At least you can mention this on interviews
68

69. The Practice
● Apache Calcite knows how to parse SQL
and optimize queries
● Has lots of optimization rules, and
state-of-the-art optimization algos
● Physical nodes have Calling Convention,
it says how to execute the plan
● You can mix Calling Conventions in one
plan to query multiple sources
69

70. The Practice
● When you want to do analytics over federated data
○ Consider using an existing product
○ Consider using existing engine, e.g. Calcite
● But be careful
○ This is not your regular ORM
○ Use FOR SQL not instead of
70

71. Where to start?
● org.apache.calcite.schema.ScannableTable
● org.apache.calcite.rel.RelNode
● org.apache.calcite.plan.RelOptRule
71

72. Further reading
● Apache Calcite Overview
○ https://arxiv.org/pdf/1802.10233.pdf
● Apache Calcite Documentation
○ https://calcite.apache.org/docs/
● Readings in Database Systems, 5th Edition
○ http://www.redbook.io/
72

73. select *
from questions
limit 5
73