Cost-Based Optimization
CSE-4/562 Spring 2019
February 25, 2019
Textbook: Ch. 16
General Query Optimizers
- Apply blind heuristics (e.g., push down selections)
- Enumerate all possible execution plans by varying (or for a reasonable subset)
- Join/Union Evaluation Order (commutativity, associativity, distributivity)
- Algorithms for Joins, Aggregates, Sort, Distinct, and others
- Data Access Paths
- Estimate the cost of each execution plan
- Pick the execution plan with the lowest cost
Idea 1: Run each plan
If we can't get the exact cost of a plan, what can we do?
Idea 2: Run each plan on a small sample of the data.
Idea 3: Analytically estimate the cost of a plan.
Plan Cost
- CPU Time
- How much time is spent processing.
- # of IOs
- How many random reads + writes go to disk.
- Memory Required
- How much memory do you need.
Remember the Real Goals
- Accurately rank the plans.
- Don't spend more time optimizing than you get back.
- Don't pick a plan that uses more memory than you have.
Accounting
Figure out the cost of each individual operator.
Only count the number of IOs added by each operator.
| Operation | RA | IOs Added (#pages) | Memory (#tuples) |
|---|---|---|---|
| Table Scan | $R$ | $\frac{|R|}{\mathcal P}$ | $O(1)$ |
| Projection | $\pi(R)$ | $0$ | $O(1)$ |
| Selection | $\sigma(R)$ | $0$ | $O(1)$ |
| Union | $R \uplus S$ | $0$ | $O(1)$ |
| Sort (In-Mem) | $\tau(R)$ | $0$ | $O(|R|)$ |
| Sort (On-Disk) | $\tau(R)$ | $\frac{2 \cdot \lfloor log_{\mathcal B}(|R|) \rfloor}{\mathcal P}$ | $O(\mathcal B)$ |
| (B+Tree) Index Scan | $Index(R, c)$ | $\log_{\mathcal I}(|R|) + \frac{|\sigma_c(R)|}{\mathcal P}$ | $O(1)$ |
| (Hash) Index Scan | $Index(R, c)$ | $1$ | $O(1)$ |
- Tuples per Page ($\mathcal P$) – Normally defined per-schema
- Size of $R$ ($|R|$)
- Pages of Buffer ($\mathcal B$)
- Keys per Index Page ($\mathcal I$)
| Operation | RA | IOs Added (#pages) | Memory (#tuples) |
|---|---|---|---|
| Nested Loop Join (Buffer $S$ in mem) | $R \times S$ | $0$ | $O(|S|)$ |
| Nested Loop Join (Buffer $S$ on disk) | $R \times_{disk} S$ | $(1+ |R|) \cdot \frac{|S|}{\mathcal P}$ | $O(1)$ |
| 1-Pass Hash Join | $R \bowtie_{1PH, c} S$ | $0$ | $O(|S|)$ |
| 2-Pass Hash Join | $R \bowtie_{2PH, c} S$ | $\frac{2|R| + 2|S|}{\mathcal P}$ | $O(1)$ |
| Sort-Merge Join | $R \bowtie_{SM, c} S$ | [Sort] | [Sort] |
| (Tree) Index NLJ | $R \bowtie_{INL, c}$ | $|R| \cdot (\log_{\mathcal I}(|S|) + \frac{|\sigma_c(S)|}{\mathcal P})$ | $O(1)$ |
| (Hash) Index NLJ | $R \bowtie_{INL, c}$ | $|R| \cdot 1$ | $O(1)$ |
| (In-Mem) Aggregate | $\gamma_A(R)$ | $0$ | $adom(A)$ |
| (Sort/Merge) Aggregate | $\gamma_A(R)$ | [Sort] | [Sort] |
- Tuples per Page ($\mathcal P$) – Normally defined per-schema
- Size of $R$ ($|R|$)
- Pages of Buffer ($\mathcal B$)
- Keys per Index Page ($\mathcal I$)
- Number of distinct values of $A$ ($adom(A)$)
| Symbol | Parameter | Type |
|---|---|---|
| $\mathcal P$ | Tuples Per Page | Fixed ($\frac{|\text{page}|}{|\text{tuple}|}$) |
| $|R|$ | Size of $R$ | Precomputed$^*$ ($|R|$) |
| $\mathcal B$ | Pages of Buffer | Configurable Parameter |
| $\mathcal I$ | Keys per Index Page | Fixed ($\frac{|\text{page}|}{|\text{key+pointer}|}$) |
| $adom(A)$ | Number of distinct values of $A$ | Precomputed$^*$ ($|\delta_A(R)|$) |
* unless $R$ is a query
Estimating IOs requires Estimating $|Q(R)|$, $|\delta_A(Q(R))|$
Cardinality Estimation
Unlike estimating IOs, cardinality estimation doesn't care about the algorithm, so we'll just be working with raw RA.
Also unlike estimating IOs, we care about the cardinality of $|Q(R)|$ as a whole, rather than the contribution of each individual operator.
| Operator | RA | Estimated Size |
|---|---|---|
| Table | $R$ | $|R|$ |
| Projection | $\pi(Q)$ | $|Q|$ |
| Union | $Q_1 \uplus Q_2$ | $|Q_1| + |Q_2|$ |
| Cross Product | $Q_1 \times Q_2$ | $|Q_1| \times |Q_2|$ |
| Sort | $\tau(Q)$ | $|Q|$ |
| Limit | $\texttt{LIMIT}_N(Q)$ | $N$ |
| Selection | $\sigma_c(Q)$ | $|Q| \times \texttt{SEL}(c, Q)$ |
| Join | $Q_1 \bowtie_c Q_2$ | $|Q_1| \times |Q_2| \times \texttt{SEL}(c, Q_1\times Q_2)$ |
| Distinct | $\delta_A(Q)$ | $\texttt{UNIQ}(A, Q)$ |
| Aggregate | $\gamma_{A, B \leftarrow \Sigma}(Q)$ | $\texttt{UNIQ}(A, Q)$ |
- $\texttt{SEL}(c, Q)$: Selectivity of $c$ on $Q$, or $\frac{|\sigma_c(Q)|}{|Q|}$
- $\texttt{UNIQ}(A, Q)$: # of distinct values of $A$ in $Q$.
Cardinality Estimation
(The Hard Parts)
- $\sigma_c(Q)$ (Cardinality Estimation)
- How many tuples will a condition $c$ allow to pass?
- $\delta_A(Q)$ (Distinct Values Estimation)
- How many distinct values of attribute(s) $A$ exist?
Idea 1: Assume each selection filters down to 10% of the data.
no... really!
... there are problems
Inconsistent estimation
$|\sigma_{c_1}(\sigma_{c_2}(R))| \neq |\sigma_{c_1 \wedge c_2}(R)|$
Too consistent estimation
$|\sigma_{id = 1}(\texttt{STUDENTS})| = |\sigma_{residence = 'NY'}(\texttt{STUDENTS})|$
... but remember that all we need is to rank plans.
Many major databases (Oracle, Postgres, Teradata, etc...) use something like 10% rule if they have nothing better.
(The specific % varies by DBMS. E.g., Teradata uses 10% for the first AND clause, and 75% for every subsequent clause)
(Some) Estimation Techniques
- The 10% rule
- Rules of thumb if you have no other options...
- Uniform Prior
- Use basic statistics to make a very rough guess.
- Sampling / History
- Small, Quick Sampling Runs (or prior executions of the query).
- Histograms
- Using more detailed statistics for improved guesses.
- Constraints
- Using rules about the data for improved guesses.
Uniform Prior
We assume that for $\sigma_c(Q)$ or $\delta_A(Q)$...
- Basic statistics are known about $Q$:
COUNT(*)COUNT(DISTINCT A)(for each A)MIN(A),MAX(A)(for each numeric A)
- Attribute values are uniformly distributed.
- No inter-attribute correlations.
If necessary statistics aren't available (point 1), fall back to the 10% rule.
If statistical assumptions (points 2, 3) aren't perfectly true, we'll still likely be getting a better estimate than the 10% rule.
COUNT(DISTINCT A)
$\texttt{UNIQ}(A, \pi_{A, \ldots}(R)) = \texttt{UNIQ}(A, R)$
$\texttt{UNIQ}(A, \sigma(R)) \approx \texttt{UNIQ}(A, R)$
$\texttt{UNIQ}(A, R \times S) = \texttt{UNIQ}(A, R)$ or $\texttt{UNIQ}(A, S)$
$$max(\texttt{UNIQ}(A, R), \texttt{UNIQ}(A, S)) \leq\\ \texttt{UNIQ}(A, R \uplus S)\\ \leq \texttt{UNIQ}(A, R) + \texttt{UNIQ}(A, S)$$
MIN(A), MAX(A)
$min_A(\pi_{A, \ldots}(R)) = min_A(R)$
$min_A(\sigma_{A, \ldots}(R)) \approx min_A(R)$
$min_A(R \times S) = min_A(R)$ or $min_A(S)$
$min_A(R \uplus S) = min(min_A(R), min_A(S))$
Estimating $\delta_A(Q)$ requires only COUNT(DISTINCT A)
Estimating Selectivity
Selectivity is a probability ($\texttt{SEL}(c, Q) = P(c)$)
| $P(A = x_1)$ | $=$ | $\frac{1}{\texttt{COUNT(DISTINCT A)}}$ |
| $P(A \in (x_1, x_2, \ldots, x_N))$ | $=$ | $\frac{N}{\texttt{COUNT(DISTINCT A)}}$ |
| $P(A \leq x_1)$ | $=$ | $\frac{x_1 - \texttt{MIN(A)}}{\texttt{MAX(A)} - \texttt{MIN(A)}}$ |
| $P(x_1 \leq A \leq x_2)$ | $=$ | $\frac{x_2 - x_1}{\texttt{MAX(A)} - \texttt{MIN(A)}}$ |
| $P(A = B)$ | $=$ | $\textbf{min}\left( \frac{1}{\texttt{COUNT(DISTINCT A)}}, \frac{1}{\texttt{COUNT(DISTINCT B)}} \right)$ |
| $P(c_1 \wedge c_2)$ | $=$ | $P(c_1) \cdot P(c_2)$ |
| $P(c_1 \vee c_2)$ | $=$ | $1 - (1 - P(c_1)) \cdot (1 - P(c_2))$ |
(With constants $x_1$, $x_2$, ...)
Limitations
- Don't always have statistics for $Q$
- For example, $\pi_{A \leftarrow (B \cdot C)}(R)$
- Don't always have clear rules for $c$
- For example, $\sigma_{\texttt{FitsModel}(A, B, C)}(R)$
- Attribute values are not always uniformly distributed.
- For example, $|\sigma_{SPC\_COMMON = 'pin\ oak'}(T)|$ vs $|\sigma_{SPC\_COMMON = 'honeylocust'}(T)|$
- Attribute values are sometimes correlated.
- For example, $\sigma_{(stump < 5) \wedge (diam > 3)}(T)$
...but handles most usage patterns