Operator scheduling in data stream systems

Abstract

Abstract. In many applications involving continuous data streams, data arrival is bursty and data rate fluctuates over time. Systems that seek to give rapid or real-time query responses in such an environment must be prepared to deal gracefully with bursts in data arrival without compromising system performance. We discuss one strategy for processing bursty streams -adaptive, load-aware scheduling of query operators to minimize resource consumption during times of peak load. We show that the choice of an operator scheduling strategy can have significant impact on the runtime system memory usage as well as output latency. Our aim is to design a scheduling strategy that minimizes the maximum runtime system memory while maintaining the output latency within prespecified bounds. We first present Chain scheduling, an operator scheduling strategy for data stream systems that is near-optimal in minimizing runtime memory usage for any collection of single-stream queries involving selections, projections, and foreign-key joins with stored relations. Chain scheduling also performs well for queries with sliding-window joins over multiple streams and multiple queries of the above types. However, during bursts in input streams, when there is a buildup of unprocessed tuples, Chain scheduling may lead to high output latency. We study the online problem of minimizing maximum runtime memory, subject to a constraint on maximum latency. We present preliminary observations, negative results, and heuristics for this problem. A thorough experimental evaluation is provided where we demonstrate the potential benefits of Chain scheduling and its different variants, compare it with competing scheduling strategies, and validate our analytical conclusions.

Uncovering Global Icebergs in Distributed Streams: Results and Implications

“…Streaming processing techniques have been closely studied in the theoretical setting [13][14][15] for various purposes. However, our goal is to come up with practical algorithms for both detecting and estimating the size of icebergs in real data sets.…”

Query Processing Using Negative and Temporal Tuples in Stream Query Engines

“…In consequence, a stream operator processes a packet of tuples in one round of a scheduler assignment [11]. Let us assume that the aggregate operator buffers the result tuples while processing this packet.…”

CAPE1Continuous Query Engine with Heterogeneous-Grained Adaptivity

“…Figure 5 shows how the adaptive framework reduces average tuple delay in the query plan. CAPE currently incorporates a wide variety of scheduling algorithms, ranging from well established ones like Round Robin and FIFO, to those proposed recently for continuous query processing such as Train [1] and Chain [2]. …”

“…The STREAM system [7] cusing on formal continuous query semantics, also applies some adaptation strategies such as runtime modification of the resource allocation and scheduling policy [2]. In addition to this, our system enables finergrained intra-operator adaptivity [4,5] and distributes a query plan to run on multiple machines for scaling purposes [9].…”