>Sampling is one of the most widely employed approximations in big data processing. Among various challenges in sampling design, sampling for join is particularly intriguing yet complex. This perplexing problem starts with a classical case where the join of two Bernoulli samples shrinks its output size quadratically and exhibits a strong dependency on the input data, presenting a unique challenge that necessitates adaptive sampling to guarantee both the quantity and quality of the sampled data. The community has made strides in achieving this goal by constructing offline samples and integrating support from indexes or key frequencies. However, when dealing with stream data, due to the need for real-time processing and high-quality analysis, methods developed for processing static data become unavailable. Consequently, a fundamental question arises: Is it possible to achieve adaptive sampling in stream data without relying on offline techniques? To address this problem, we propose FreeSam, which couples hybrid sampling with intra-window join, a key stream join operator. Our focus lies on two widely used metrics: output size, ensuring quantity, and variance, ensuring quality. FreeSam enables adaptability in both the desired quantity and quality of data sampling by offering control on the two-dimensional space spanned by these metrics. Meanwhile, adjustable trade-offs between quality and performance make FreeSam practical for use. Our experiments show that, for every 1% increase in latency limitation, FreeSam can yield a 3.83% increase in the output size while maintaining the level of the estimator's variance. Additionally, we give FreeSam a multi-core implementation and ensure predictability of its latency through both an analytic model and a neural network model. The accuracy of these models is 88.05% and 96.75% respectively.

With the development of big data technology, the amount of business data that Internet companies need to handle has reached the petabyte level, which poses great pressure on the system processing capacity. For example, the peak order volume of Alibaba's Global Shopping Festival in 2020 reached 583,000 orders per second. Even worse, multi-model data are involved in real business. The inability to perform high-throughput, lowlatency transaction processing can result in a poor user experience that can lead to serious financial losses due to customer churn. Although numerous optimizations have been proposed, they can fail in the face of petabytes of data, or be significantly less effective. In this paper, we propose a novel and practical multi-model big data system that can manage petabytes of data. Particularly, we show three special designs for processing the petabytes of data. First, we perform partition to reduce the amount of unnecessary data to be scanned. Second, we adaptively adopt row storage mode for big tables that are frequently updated and column storage mode for tables that are frequently queried to improve the system efficiency. Third, we conduct compression to accelerate IO access speed. We analyze Alibaba's two real PB-level business scenarios, Double 11 and Zhixingtong, and generate workloads and benchmark accordingly to verify our system. Experiments show that our system can efficiently manage petabyte-scale data in real scenarios, providing high-performance querying of terabyte-scale datasets, and be suitable for various workloads.