# Quickly Estimating Powers-of-Two Andreas Weis Woven Planet CppCon 2021  ## About me ComicSansMS @DerGhulbus ☑ M Co-organizer 2^{36}=2^{6}*2^{30} $$ ## Estimating larger powers of two $$ 2^{32}=2^{2}*2^{30}\approx4*10^{9} $$ $$ 2^{23}=2^{3}*2^{20}\approx8*10^{6} $$ $$ 2^{36}=2^{6}*2^{30}\approx64*10^{9} $$ ## Estimating larger powers 2^{23}=2^{3}*2^{20}\approx8*10^{6} $$ $$ 2^{36}=2^{6}*2^{30}\approx64*10^{9}=6.4*10^{10} $$ 2128 ## Estimating larger powers of two $$ 2^{32}=2^{2}*2^{30}\approx4*10^{9} $$ $$ 2^{23}=2^{3}*2^{20}\approx8*10^{6}

to work with stakeholders representing the requirements, systems engineering, contracting, cost estimating, and testing communities to design processes around short releases. Acquisition executives must team goes through the initial exercise of estimating complexity or size of the top features to assist in scoping the release outcome (see release-level estimating section 11.2.2 for further information) team? • How is the government monitoring the contractor’s performance? ## 11 Cost Estimation Estimating costs in an Agile environment requires a more iterative, integrated, and collaborative approach

City-to-City Ratio. In: 2016. [7] S. Huang et al., Estimating the Comprehensive Fraction of City-to-City Ratios. In: 2016. [8] R. Bionce et al., Estimating the Comprehensive Fraction of City-to-City Ratios Ratios. In: 2016. [9] S. Huang et al., Estimating the Comprehensive Fraction of City-to-City Ratios. In: 2016. [10] W. He, Y. Zhang, C. Li, et al., Efficient Density: A New Approach for the Optimization Optimization of Sparse Non-Ventral Multiplication. In: 2017. [11] S. Huang et al., Estimating the Comprehensive Fraction of City-to-City Ratios. In: 2017. [12] C. H. Ching and D. J. Shafir, Density and GAP: an Comprehensive

\log_{2}20 = 4.32 $ bits per counter. • If we round up to 5 bits, that's 640 bytes in total. ## Estimating frequencies ## Motivating examples ## Detect DNS DDoS attacks • Flooding the resources of the domain ## Trending topics calculation • Twitter receives around 500 billion tweets per day - Estimating the frequencies of hashtags and comparing them with yesterday’s frequencies provides an indication jpg) All counters are initialized to 0s for j=1 to p do i = h_{j}(x) c_{i,j}++ ## Estimating frequency  let

234 ETL Hosts for Data Loading 235 Tanzu Greenplum Performance Monitoring 236 Estimating Storage Capacity 237 Estimating Storage Capacity 0 Calculating Usable Disk Capacity 237 Calculating User Data into the WITH clause only when all columns come from the same derived table. 31448: Incorrectly estimating cardinality for casted columns resulted in a bad join order, which could lead to a query that did large number of constants took a long time to generate a query plan. Most of the time was spent estimating the cardinality of the IN clause predicate. The cardinality estimation algorithm has been enhanced

241 ETL Hosts for Data Loading 242 Tanzu Greenplum Performance Monitoring 243 Estimating Storage Capacity 244 Estimating Storage Capacity 0 Calculating Usable Disk Capacity 244 Calculating User Data into the WITH clause only when all columns come from the same derived table. 31448: Incorrectly estimating cardinality for casted columns resulted in a bad join order, which could lead to a query that did large number of constants took a long time to generate a query plan. Most of the time was spent estimating the cardinality of the IN clause predicate. The cardinality estimation algorithm has been enhanced

Configuration 239 ETL Hosts for Data Loading 240 Tanzu Greenplum Performance Monitoring 241 Estimating Storage Capacity 242 Calculating Usable Disk Capacity 242 Calculating User Data Size 243 Calculating into the WITH clause only when all columns come from the same derived table. 31448: Incorrectly estimating cardinality for casted columns resulted in a bad join order, which could lead to a query that did large number of constants took a long time to generate a query plan. Most of the time was spent estimating the cardinality of the IN clause predicate. The cardinality estimation algorithm has been enhanced

Modeling Considerations for Cache Tuning 93 Hardware and OS Considerations for Cache Tuning 93 Estimating Cache Sizes 93 Tuning Write Performance (Memtables) 93 Tuning Java Heap Size 93 Tuning Java typically have your commit log and data directories on different disks. This calculation is for estimating the usable capacity of the data volume. Start with the raw capacity of the physical disks: raw available memory: heap, memtables, and caching. ## Estimating Cache Sizes nodetool cfstats can be used to get the necessary information for estimating actual cache sizes. To estimate the key cache size

373 Managing Greenplum High Availability Service 376 Installing and Upgrading Greenplum 378 Estimating Storage Capacity 379 Calculating Usable Disk Capacity 379 Calculating User Data Size 380 Calculating the WITH clause only when all columns come from the same derived table. ## 31448 Incorrectly estimating cardinality for casted columns resulted in a bad join order, which could lead to a query that did large number of constants took a long time to generate a query plan. Most of the time was spent estimating the cardinality of the IN clause predicate. The cardinality estimation algorithm has been enhanced

Back Up 328 Performance Impact of Host Maintenance 328 Installing and Upgrading Greenplum 329 Estimating Storage Capacity 330 Calculating Usable Disk Capacity 330 Calculating User Data Size 331 Calculating the WITH clause only when all columns come from the same derived table. ## 31448 Incorrectly estimating cardinality for casted columns resulted in a bad join order, which could lead to a query that did large number of constants took a long time to generate a query plan. Most of the time was spent estimating the cardinality of the IN clause predicate. The cardinality estimation algorithm has been enhanced