October 1, 2026
Scott Routledge
In a previous post, we explained how BodoSQL builds on Apache Calcite, an open source framework for developing data management systems, to transform a user's SQL query into an optimized execution plan. At a high level, this process involves parsing SQL text into an abstract syntax tree (AST), validating the query against the schema, converting the validated query into relational algebra, and finally optimizing that relational plan and converting it into a physical plan. We recommend reading that post first for background on the overall BodoSQL query planning pipeline.
In this post, we focus on that final step: query optimization. We will start with an overview of the BodoSQL optimizer architecture, including its major optimization stages, metadata framework, and cost model. We will then dive deeper into several custom BodoSQL optimizations, showing how BodoSQL builds on Calcite's mature optimizer framework while extending it for large-scale, parallel query execution.
The BodoSQL optimizer transforms a logical plan describing what computation should be performed into an optimized physical plan describing how that computation should be executed by the backend.
Rather than performing this optimization in a single step, BodoSQL uses a pipeline of specialized stages. Early stages normalize and simplify the logical plan and apply rule-based transformations. The Volcano planner then explores alternative plans using cost-based optimization and converts the logical plan into a physical plan. Finally, additional physical-plan passes introduce optimizations targeted specifically at Bodo's execution backend.
Throughout this pipeline, optimizer stages can use BodoSQL's metadata framework to estimate properties of the data, such as row counts and distinct values. During cost-based optimization, these estimates feed into BodoSQL's custom cost model to compare alternative plans and select an efficient physical plan.
The following sections walk through each stage of this pipeline before describing the metadata framework and cost model in more detail.
The preprocessor normalizes and simplifies the query plan in preparation for later optimization passes. It first removes nested subqueries—expressions containing relational plans, such as IN or EXISTS—and rewrites them using relational operators such as joins. If a subquery references columns from the outer query, this rewrite may produce a Correlate node, which a subsequent decorrelation pass attempts to transform into a regular join. The preprocessor also converts Calcite's logical operators into BodoSQL's BodoLogical operator classes.
After preprocessing, BodoSQL applies a series of rule-based transformations to further simplify the plan and perform optimizations that are generally beneficial independent of the cost model. These include removing unused fields, eliminating common subexpressions, pushing filters and fields closer to their data sources, and simplifying projections. Many of these passes use Calcite's HEP planner, which repeatedly applies a specified collection of transformation rules to the plan until it converges. BodoSQL combines Calcite's core rules with custom rules that target patterns important to BodoSQL workloads. We will look at a couple examples of these custom rules in the following section.
After rule-based optimization, BodoSQL uses Calcite's Volcano planner to transform the logical plan into an efficient physical plan. Volcano uses transformation rules to explore alternative logical plans and converter rules to generate executable physical implementations. As alternatives are discovered, Volcano organizes equivalent relational expressions into sets and tracks the lowest-cost physical implementations found for each set. BodoSQL's metadata framework and cost model estimate and compare the execution costs of these alternatives, allowing Volcano to ultimately select the lowest-cost physical plan equivalent to the original logical plan.
After Volcano selects a physical plan, BodoSQL applies additional optimization passes to improve its execution on its backend. Some of these transformations use the HEP planner with custom rules. For example, one rule marks certain BodoPhysicalJoin operators for rebalancing when their output is reused by multiple non-trivial projections, helping redistribute data across parallel workers and mitigate data skew. Other optimizations are implemented as specialized passes over the physical plan, including the SubplanCachingProgram and RuntimeJoinFilterProgram, which identify opportunities for common subplan reuse and runtime join filtering respectively. These optimizations are discussed in more detail in later sections.
BodoSQL uses a custom cost model to compare plan alternatives during Volcano optimization. Each BodoPhysical operator estimates its cost using statistics from the metadata framework, such as input row counts and row sizes. For operators such as projections and aggregates, the cost model also accounts for the cost of evaluating individual expressions and aggregate functions. These cost functions have been tuned based on empirical results from customer workloads, with particular emphasis on selecting efficient join orders. As BodoSQL expands to support new workloads and execution strategies, improving the usefulness of the cost model remains an active area of development.
The effectiveness of the cost model depends heavily on accurate estimates of the data flowing through each operator. Row count is particularly important, but estimating the output size of operators such as joins and aggregates also requires statistics such as the number of distinct values (NDVs) for individual columns.
BodoSQL obtains base-table statistics from the underlying catalog. For Snowflake, BodoSQL can issue metadata queries such as COUNT(*) and APPROX_COUNT_DISTINCT to obtain row counts and approximate NDVs. For Iceberg tables, BodoSQL uses the NDV statistics stored in Iceberg Puffin files. Bodo DataFrames and BodoSQL both generate approximate distinct-value statistics for supported column types when writing Iceberg tables.
The metadata framework then propagates these statistics through the query plan. For example, when pulling estimates up through a filter, BodoSQL applies a selectivity estimate to the input statistics to derive output row counts and NDVs. Other operators, such as aggregates and joins, similarly derive their output statistics from properties of their inputs. Improving these cardinality and distinct-value estimates remains an important area of ongoing planner development.
Now that we have covered the overall architecture of the optimizer, we can take a closer look at how BodoSQL builds on and extends Calcite at different stages of the optimization pipeline. The following examples illustrate how BodoSQL adds custom rules, costing and metadata behavior, and physical-plan transformations to optimize for Bodo's execution engine and workloads.
One of the primary ways BodoSQL extends Calcite is through custom optimizer rules. BodoSQL combines Calcite's core rule set with its own transformations, which run at different stages of the optimization pipeline. These rules target optimization opportunities that are particularly important for BodoSQL workloads or enable behavior specific to Bodo's execution backend.
As an example, we'll look at two custom rules used during the HEP-based FilterPushDownPass: JoinDeriveOrPredicatesRule and IcebergFilterLockRule. Together with Calcite's existing filter and join rules, these transformations expose additional filtering opportunities and push them down to the underlying data source.
The JoinDeriveOrPredicatesRule, introduced in the upcoming 2026.10 release, finds implied predicates in OR-of-AND join conditions that reference only one side of the join. These predicates can then be extracted and pushed down by other optimizer rules, allowing rows to be filtered earlier in the plan.
For example, consider the predicate:
(T1.a = 1 AND T2.b = 2) OR (T1.a = 4 AND T2.b = 5)
This predicate implies:
(T1.a = 1 OR T1.a = 4) AND (T2.b = 2 OR T2.b = 5)
If we call the original statement X and the derived statement Y, since X -> Y, we can create a new expression: X AND Y that is equivalent to X. The important property of Y is that each clause references only one side of the join. This allows subsequent optimizer rules to extract those clauses from the join condition and push them closer to the corresponding table scans.
This is similar to the extract_restriction_or_clauses optimization in PostgreSQL.
As a concrete example, consider this query derived from TPC-H Q7:
select
n1.n_name as supp_nation,
n2.n_name as cust_nation,
l_extendedprice * (1 - l_discount) as volume
from
supplier,
lineitem,
orders,
customer,
nation n1,
nation n2
where
s_suppkey = l_suppkey
and o_orderkey = l_orderkey
and c_custkey = o_custkey
and s_nationkey = n1.n_nationkey
and c_nationkey = n2.n_nationkey
and (
(n1.n_name = 'FRANCE' and n2.n_name = 'GERMANY')
or (n1.n_name = 'GERMANY' and n2.n_name = 'FRANCE')
)
Which computes the discounted sales volume for line items where the supplier is from Germany and the customer is from France, or the supplier is from France and the customer is from Germany.
Before JoinDeriveOrPredicatesRule is applied, the relevant portion of the plan looks like:
BodoLogicalProject(...)
BodoLogicalJoin(condition=[
AND(
C_NATIONKEY = N2.N_NATIONKEY,
OR(
AND(N1.N_NAME = 'FRANCE', N2.N_NAME = 'GERMANY'),
AND(N1.N_NAME = 'GERMANY', N2.N_NAME = 'FRANCE')
)
)
])
BodoLogicalJoin(condition=[S_NATIONKEY = N1.N_NATIONKEY])
BodoLogicalJoin(condition=[C_CUSTKEY = O_CUSTKEY])
...
IcebergTableScan(table=[[TPCH, NATION N1]])
IcebergTableScan(table=[[TPCH, NATION N2]])
JoinDeriveOrPredicatesRule recognizes that the OR predicate implies two additional single-input predicates:
N1.N_NAME = 'FRANCE' OR N1.N_NAME = 'GERMANY',
N2.N_NAME = 'FRANCE' OR N2.N_NAME = 'GERMANY'
and adds these predicates to the join condition:
BodoLogicalJoin(condition=[
AND(
C_NATIONKEY = N2.N_NATIONKEY,
OR(
AND(N1.N_NAME = 'FRANCE', N2.N_NAME = 'GERMANY'),
AND(N1.N_NAME = 'GERMANY', N2.N_NAME = 'FRANCE')
),
OR(N1.N_NAME = 'FRANCE', N1.N_NAME = 'GERMANY'),
OR(N2.N_NAME = 'FRANCE', N2.N_NAME = 'GERMANY'),
)
])
The new structure allows Calcite’s JoinConditionPushRule to extract the two derived predicates and move them onto their respective sides of the join. Calcite also simplifies each OR predicate into an IN/SEARCH expression:
BodoLogicalJoin(condition=[
AND(
C_NATIONKEY = N2.N_NATIONKEY,
OR(
AND(N1.N_NAME = 'FRANCE', N2.N_NAME = 'GERMANY'),
AND(N1.N_NAME = 'GERMANY', N2.N_NAME = 'FRANCE')
)
)
])
BodoLogicalFilter(condition=(N_NAME IN ['GERMANY', 'FRANCE'])
BodoLogicalJoin(condition=[S_NATIONKEY = N1.N_NATIONKEY])
BodoLogicalJoin(condition=[C_CUSTKEY = O_CUSTKEY])
...
IcebergTableScan(table=[[TPCH, NATION N1]])
BodoLogicalFilter(condition=(N_NAME IN ['GERMANY', 'FRANCE'])
IcebergTableScan(table=[[TPCH, NATION N2]])
The filter on N2.N_NAME is already directly above its table scan. The N1.N_NAME filter, however, is still above another join.
Calcite’s FilterIntoJoinRule pushes this filter into the join below it. JoinConditionPushRule and FilterIntoJoinRule can then continue firing as the HEP program iterates, moving the predicate down until it reaches the NATION N1 scan.
At this point, both filters are directly above their respective IcebergTableScan nodes, which matches another custom BodoSQL rule: IcebergFilterLockRule.
IcebergFilterLockRule converts a BodoLogicalFilter directly above an IcebergTableScan into an IcebergFilter. This “locks” the filter to the Iceberg source so later optimization passes cannot pull it back above the scan and undo the pushdown. It also tells the backend that the predicate can be applied as part of the Iceberg read, enabling file pruning when applicable.
The final relevant portion of the plan after FilterPushDownPass is:
BodoLogicalJoin(condition=[
AND(
C_NATIONKEY = N2.N_NATIONKEY,
OR(
AND(N1.N_NAME = 'FRANCE', N2.N_NAME = 'GERMANY'),
AND(N1.N_NAME = 'GERMANY', N2.N_NAME = 'FRANCE')
)
)
])
BodoLogicalJoin(condition=[S_NATIONKEY = N1.N_NATIONKEY])
BodoLogicalJoin(condition=[C_CUSTKEY = O_CUSTKEY])
...
IcebergFilter(condition=(N_NAME IN ['GERMANY', 'FRANCE'])
IcebergTableScan(table=[[TPCH, NATION N1]])
IcebergFilter(condition=(N_NAME IN ['GERMANY', 'FRANCE'])
IcebergTableScan(table=[[TPCH, NATION N2]])
In our testing, adding JoinDeriveOrPredicatesRule improved TPC-H Q7 performance by more than 3×. We’ll share the full TPC-H performance results in a future post.
The previous example showed how BodoSQL extends Calcite with rule-based transformations that expose additional optimization opportunities. Other transformations, however, are not universally beneficial and require the optimizer to compare alternative plans.
To see how BodoSQL makes these cost-based decisions, we will look at the Volcano optimizer pass, where BodoSQL's custom metadata framework and cost model work together with Calcite's transformation rules. As an example, consider Aggregate-Join Transpose, an optimization where the benefit depends heavily on the amount of data flowing through each operator.
BodoSQL uses a modified version of Calcite's AggregateJoinTransposeRule for this optimization. The rule identifies aggregates above joins and generates an alternative plan in which partial aggregations are pushed below the join. When the partial aggregates significantly reduce their inputs, this can greatly reduce the amount of data processed by the join.
Consider the following query, which counts the number of suppliers in each nation:
select
n_name,
count(*) as num_supp
from
supplier,
nation
where
s_nationkey = n_nationkey
group by
n_name
Before the Volcano pass, the simplified logical plan looks like:
BodoLogicalAggregate [GROUP BY N_NAME; COUNT(*)]
└── BodoLogicalJoin [N_NATIONKEY = S_NATIONKEY]
├── IcebergTableScan [NATION; N_NATIONKEY, N_NAME]
└── IcebergTableScan [SUPPLIER; S_NATIONKEY]
When AggregateJoinTransposeRule matches this plan, it generates an alternative logical plan in which partial aggregates are evaluated before the join:
BodoLogicalAggregate [GROUP BY N_NAME; SUM(counts)]
└── BodoLogicalProject [N_NAME, counts=supplier_count * nation_count]
└── BodoLogicalJoin [S_NATIONKEY = N_NATIONKEY]
├── BodoLogicalAggregate [GROUP BY S_NATIONKEY; COUNT()]
│ └── IcebergTableScan [SUPPLIER; S_NATIONKEY]
└── BodoLogicalAggregate [GROUP BY N_NATIONKEY, N_NAME; COUNT()]
└── IcebergTableScan [NATION; N_NATIONKEY, N_NAME]
Volcano does not immediately replace the original plan with the transformed version. Instead, it keeps both as equivalent alternatives. BodoSQL's converter rules then generate physical implementations of these alternatives. Logical and physical expressions participate in the same search, but only physical operators are assigned a cost.
Equivalent alternatives
Original Aggregate Pushdown
│ │
▼ ▼
Logical Aggregate Logical Aggregate
│ │
Logical Join Logical Join
/ \ / \
SUPPLIER NATION Logical Agg. Logical Agg.
│ │
SUPPLIER NATION
│ │
│ Converter rules │ Converter rules
▼ ▼
Physical Aggregate Physical Aggregate
│ │
Physical Join Physical Join
/ \ / \
SUPPLIER NATION Physical Agg. Physical Agg.
│ │
SUPPLIER NATION
Once these physical alternatives become available, Volcano can compare their cumulative costs. BodoSQL's actual cost functions account for many factors, but for this example we will use a simplified model:
Aggregate cost = input rows
Join cost = left input rows + right input rows
Read, Project cost ≈ 0
To estimate these costs, the metadata framework first obtains base-table statistics from the catalog. Assuming the data is stored in Iceberg, these statistics can be read from the table metadata. For this example, assume:
SUPPLIER:
rows = 1,000,000
NDV(S_NATIONKEY) = 25
NATION:
rows = 25
NDV(N_NATIONKEY) = 25
NDV(N_NAME) = 25
The metadata framework then propagates these statistics upward through each candidate plan. For the original plan, BodoSQL estimates the join cardinality using statistics from both inputs and the join type. In this case, the join is estimated to retain 90% of the SUPPLIER rows, producing 900,000 rows. The simplified cumulative cost is then:
C(JOIN) = 1,000,000 + 25 = 1,000,025
C(AGGREGATE) = 900,000
Final Cost = 1,900,025
For the transformed plan, we first estimate the output cardinalities of the partial aggregates. Grouping SUPPLIER by S_NATIONKEY produces approximately its number of distinct nation keys:
rows(AGGREGATE_SUPPLIER)
≈ NDV(S_NATIONKEY)
= 25
The NATION aggregate groups by both N_NATIONKEY and N_NAME. BodoSQL estimates the number of distinct combinations from the NDVs of the grouping columns, while bounding the result by the input row count. Using a simplified version of that calculation:
rows(AGGREGATE_NATION)
= min(
rows(NATION),
NDV(N_NATIONKEY) * NDV(N_NAME) * 0.5
)
= min(25, 25 * 25 * 0.5)
= 25
The join therefore processes only about 25 rows from each side rather than one million supplier rows and outputs an estimated 22 rows. The cumulative cost becomes:
C(AGGREGATE_SUPPLIER) = 1,000,000
C(AGGREGATE_NATION) = 25
C(JOIN) = 25 + 25 = 50
C(FINAL_AGGREGATE) = 22
TOTAL = 1,000,097
The aggregate-pushdown alternative therefore has a substantially lower estimated cost:
Original plan: 1,900,025
Aggregate-pushdown plan: 1,000,097
This example illustrates how the major pieces of Volcano optimization work together: transformation rules generate alternative plans, the metadata framework estimates the amount of data flowing through those plans, the cost model assigns costs to their physical operators, and Volcano uses those costs to select an efficient physical plan.
Once Volcano has selected a physical plan, BodoSQL applies additional optimization passes tailored to its execution backend. These passes operate on the selected physical plan and can introduce execution strategies that are difficult to express through Calcite's standard relational transformations.
One such optimization is subplan caching. After logical optimization and physical plan conversion, the resulting plan may contain identical or similar computations in multiple places. Recomputing these subplans can be expensive, so BodoSQL attempts to identify opportunities to share their computation and execute it only once.
Calcite provides some support for shared computation by allowing identical relational expressions to reference the same RelNode object. However, relying on shared object identity has a few limitations:
To address these limitations, BodoSQL implements a custom subplan caching pass which replaces shared computations with cache nodes. Each cache node stores the root of the cached subplan and tracks how many places in the plan consume its result. References to the cache then appear as leaves in the physical plan, similar to table scans.
Making caching explicit simplifies both optimization and execution. The optimizer can inspect where cached results are consumed and how many consumers they have, while subsequent passes such as RuntimeJoinFilterProgram can provide special handling for cached subplans. The explicit representation also makes caching behavior easier to test and implement in the backend.
Exact-match caching alone, however, misses many opportunities for reuse. Two branches may perform nearly the same expensive computation while differing slightly in the columns, filters, or aggregate functions they require. To handle these cases, BodoSQL supports covering expression caching, which constructs a shared computation capable of producing the data required by multiple similar consumers.
For example, consider the query:
with t0 as (
select A, B, sum(D)
from MY_TABLE
group by A, B
),
t1 as (
select A, B, sum(C)
from MY_TABLE
group by A, B
)
select * from t0
join t1 on
t0.A = t1.B
Before applying subplan caching, the simplified physical plan looks like:
Join
├── Aggregate [GROUP BY A, B; SUM(C)]
│ └── Project [A, B, C]
│ └── MY_TABLE
└── Aggregate [GROUP BY A, B; SUM(D)]
└── Project [A, B, D]
└── MY_TABLE
With ordinary exact match caching, we can’t replace the branches of a join with a shared cache node since the aggregation functions are different. With covering expression caching, BodoSQL can construct a shared aggregate that computes both SUM(C) and SUM(D). Each consumer then projects the portion of the cached result that it requires:
Cached Result
└── Aggregate [GROUP BY A, B; SUM(C), SUM(D)]
└── MY_TABLE
Join
├── Project [A, B, SUM(C)]
│ └── Cached Result
└── Project [A, B, SUM(D)]
└── Cached Result
Finding covering expressions starts with a bottom-up traversal of the plan tree. We first identify identical nodes as potential cache candidates, then walk upward through their consumers. If the parent nodes are identical, we append the common operation to the cached expression and continue. Some operations can still be combined even when they differ. For projections, we take the union of the required columns and computations. For filters, we take the OR of the consumers' filters so the cache retains every row needed by any consumer. For aggregates, we can take the union of the aggregate functions provided the grouping keys are compatible.
As we build the covering expression, we track the projection and filter required by each individual consumer. Once the consumers reach operations that cannot be combined, we materialize the cache and apply each consumer's remaining projection and filter to reconstruct its expected output.
While subplan caching improves execution by identifying and sharing repeated computation, BodoSQL's post-Volcano optimization passes can also reduce the amount of data that reaches expensive operators. One important example is Runtime Join Filters (RTJFs).
RTJFs use information collected from the build side of an inner or right join to filter rows from the probe side that do not match the join condition. This optimization can significantly improve queries involving large tables and selective joins by reducing the amount of data processed before it reaches the join.
During the RTJF stage, BodoSQL traverses the physical plan and assigns each eligible join a unique join ID, identifies the probe-side columns that can be filtered, and pushes these filters down through the plan as far as possible. Once a filter can no longer be pushed down, BodoSQL inserts a runtime join filter node. A single RTJF node may contain filters associated with multiple joins and columns.
At execution time, BodoSQL applies runtime join filtering in two ways. First, individual columns can be filtered using the minimum and maximum values observed on the build side. These filters are particularly valuable when they can be pushed into an I/O operation, allowing data to be eliminated during read. Second, when all equality-join columns for a particular join are available at the filter location, BodoSQL can apply a row-level Bloom filter built from the corresponding keys on the build side. This provides more precise filtering than applying independent bounds to each column.
As an example, consider the following query:
SELECT
l.l_orderkey,
ps.ps_supplycost
FROM lineitem l
JOIN part p
ON l.l_partkey = p.p_partkey
JOIN partsupp ps
ON l.l_partkey = ps.ps_partkey
AND l.l_suppkey = ps.ps_suppkey
This query contains two joins. Join 0 joins LINEITEM and PARTSUPP using the two-column key (PARTKEY, SUPPKEY), while Join 1 joins LINEITEM and PART using PARTKEY. During logical-to-physical conversion, the Volcano planner chooses to first join the larger LINEITEM table with the smaller PART table, then joins that result with PARTSUPP. Both PART and PARTSUPP are selected as the build sides of their respective joins:
Join 0 [L_PARTKEY = PS_PARTKEY, L_SUPPKEY = PS_SUPPKEY]
├── Join 1 [L_PARTKEY = P_PARTKEY]
│ ├── LINEITEM
│ └── PART
└── PARTSUPP
After RTJF generation and pushdown, the plan becomes:
Join 0 [L_PARTKEY = PS_PARTKEY, L_SUPPKEY = PS_SUPPKEY]
├── Join 1 [L_PARTKEY = P_PARTKEY]
│ ├── RuntimeJoinFilter
│ │ ├── Join 0: equalityColumns=[L_PARTKEY, L_SUPPKEY]
│ │ │ allEqualityKeysReady=true
│ │ ├── Join 1: equalityColumns=[L_PARTKEY]
│ │ │ allEqualityKeysReady=true
│ │ └── LINEITEM
│ └── RuntimeJoinFilter
│ ├── Join 0: equalityColumns=[P_PARTKEY]
│ │ allEqualityKeysReady=false
│ └── PART
└── PARTSUPP
Join 0 generates runtime filters for both L_PARTKEY and L_SUPPKEY, while Join 1 generates a filter for L_PARTKEY. When pushing Join 0's filters down through Join 1, the L_PARTKEY filter can also be propagated to the PART side because the join establishes the equivalence L_PARTKEY = P_PARTKEY. However, L_SUPPKEY has no corresponding column on the PART side. As a result, above the PART scan, only one of Join 0's two equality keys is available. The min/max filter for P_PARTKEY can still be pushed into IO, but the multi-column Bloom filter cannot be evaluated because the complete (PARTKEY, SUPPKEY) key is unavailable. This is represented by allEqualityKeysReady=false.
Above the LINEITEM scan, both L_PARTKEY and L_SUPPKEY are available, so allEqualityKeysReady=true for Join 0 and the multi-column Bloom filter can be evaluated. The min/max filters for these columns can additionally be pushed into the LINEITEM read. Join 1 behaves similarly, with its single L_PARTKEY equality key available above the LINEITEM scan.
After the RTJF optimization finishes, the plan is no longer strictly relational, since RTJF nodes inject a runtime dependency between the left and right side of a join. Therefore, the RTJF pass has to be performed after other relational optimizations occur.
Throughout this post, we have seen how BodoSQL transforms a raw logical plan into an optimized physical plan for efficient parallel execution. The optimizer combines several complementary techniques:
The examples in this post also illustrate why BodoSQL extends Calcite rather than treating it as a fixed optimizer. Calcite provides the underlying planner infrastructure and a rich collection of optimization rules, while BodoSQL adds rules, metadata, costing, and physical-plan transformations tailored to large-scale parallel execution.
As the optimizer continues to evolve, and there are several promising directions for future work:
Together, these improvements move toward a broader goal: making more optimizer decisions based on an increasingly accurate model of the data and the cost of executing a plan. As BodoSQL's metadata and cost models improve, they can inform not only which relational plan Volcano selects, but also how later physical optimizations shape that plan for efficient distributed execution.
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