Ray is an open source project for parallel and distributed Python. This article was originally posted here. Parallel and distributed computing are a staple of modern applications. We need to leverage multiple cores or multiple machines to speed up applications or to run them at a large scale. The infrastructure for crawling the web and responding to search queries are not single-threaded programs running on someone’s laptop but rather collections of services that communicate and interact with one another. This post will describe how to use Ray to easily build applications that can scale from your laptop to a large cluster. Why Ray? Many tutorials explain how to use Python’s multiprocessing module. Unfortunately the multiprocessing module is severely limited in…
What Is the Role of Machine Learning in Databases?
(This article was authored by Sanjay Krishnan, Zongheng Yang, Joe Hellerstein, and Ion Stoica.) What is the role of machine learning in the design and implementation of a modern database system? This question has sparked considerable recent introspection in the data management community, and the epicenter of this debate is the core database problem of query optimization, where the database system finds the best physical execution path for an SQL query. The au courant research direction, inspired by trends in Computer Vision, Natural Language Processing, and Robotics, is to apply deep learning; let the database learn the value of each execution strategy by executing different query plans repeatedly (an homage to Google’s robot “arm farm”) rather through a pre-programmed analytical…
A History of Postgres
(crossposted from databeta.wordpress.com) The ACM began commissioning a series of reminiscence books on Turing Award winners. Thanks to hard work by editor Michael Brodie, the first one is Mike Stonebraker’s book, which just came out. I was asked to write the chapter on Postgres. I was one of the large and distinguished crew of grad students on the Postgres project, so this was fun. ACM in its wisdom decided that these books would be published in a relatively traditional fashion—i.e. you have to pay for them. The publisher, Morgan-Claypool, has this tip for students and ACM members: Please note that the Bitly link goes to a landing page where Students, ACM Members, and Institutions who have access to the ACM…
Confluo: Millisecond-level Queries on Large-scale Live Data
Confluo is a system for real-time distributed analysis of multiple data streams. Confluo simultaneously supports high throughput concurrent writes, online queries at millisecond timescales, and CPU-efficient ad-hoc queries via a combination of data structures carefully designed for the specialized case of multiple data streams, and an end-to-end optimized system design. We are excited to release Confluo as an open-source C++ project, comprising: Confluo’s data structure library, that supports high throughput ingestion of logs, along with a wide range of online (live aggregates, conditional trigger executions, etc.) and offline (ad-hoc filters, aggregates, etc.) queries, and, A Confluo server implementation, that encapsulates the data structures and exposes its operations via an RPC interface, along with client libraries in C++, Java and Python.…
SQL Query Optimization Meets Deep Reinforcement Learning
We show that deep reinforcement learning is successful at optimizing SQL joins, a problem studied for decades in the database community. Further, on large joins, we show that this technique executes up to 10x faster than classical dynamic programs and 10,000x faster than exhaustive enumeration. This blog post introduces the problem and summarizes our key technique; details can be found in our latest preprint, Learning to Optimize Join Queries With Deep Reinforcement Learning. SQL query optimization has been studied in the database community for almost 40 years, dating all the way back from System R’s classical dynamic programming approach. Central to query optimization is the problem of join ordering. Despite the problem’s rich history, there is still a continuous stream…
Going Fast and Cheap: How We Made Anna Autoscale
Background: In an earlier blog post, we described a system called Anna, which used a shared-nothing, thread-per-core architecture to achieve lightning-fast speeds by avoiding all coordination mechanisms. Anna also used lattice composition to enable a rich variety of coordination-free consistency levels. The first version of Anna blew existing in-memory KVSes out of the water: Anna is up to 700x faster than Masstree, an earlier state-of-the-art research KVS, and up to 800x faster than Intel’s “lock-free” TBB hash table. You can find the previous blog post here and the full paper here. We refer to that version of Anna as “Anna v0.” In this post, we describe how we extended the fastest KVS in the cloud to be extremely cost-efficient and…
MLPerf: SPEC for ML
The RISE Lab at UC Berkeley today joins Baidu, Google, Harvard University, and Stanford University to announce a new benchmark suite for machine learning called MLPerf at the O’Reilly AI conference in New York City (see https://mlperf.org/). The MLPerf effort aims to build a common set of benchmarks that enables the machine learning (ML) field to measure system performance eventually for both training and inference from mobile devices to cloud services. We believe that a widely accepted benchmark suite will benefit the entire community, including researchers, developers, builders of machine learning frameworks, cloud service providers, hardware manufacturers, application providers, and end users. Historical Inspiration. We are motivated in part by the System Performance Evaluation Cooperative (SPEC) benchmark for general-purpose computing that drove rapid,…
Anna: A Crazy Fast, Super-Scalable, Flexibly Consistent KVS 🗺
This article cross-posted from the DataBeta blog. There’s fast and there’s fast. This post is about Anna, a key/value database design from our team at Berkeley that’s got phenomenal speed and buttery smooth scaling, with an unprecedented range of consistency guarantees. Details are in our upcoming ICDE18 paper on Anna. Conventional wisdom (or at least Jeff Dean wisdom) says that you have to redesign your system every time you scale by 10x. As researchers, we asked the counter-cultural question: what would it take to build a key-value store that would excel across many orders of magnitude of scale, from a single multicore box to the global cloud? Turns out this kind of curiosity can lead to a system with pretty interesting practical…
Opaque: Secure Apache Spark SQL
As enterprises move to cloud-based analytics, the risk of cloud security breaches poses a serious threat. Encrypting data at rest and in transit is a major first step. However, data must still be decrypted in memory for processing, exposing it to any attacker who can observe memory contents. This is a challenging problem because security usually implies a tradeoff between performance and functionality. Cryptographic approaches like fully homomorphic encryption provide full functionality to a system, but are extremely slow. Systems like CryptDB utilize lighter cryptographic primitives to provide a practical database, but are limited in functionality. Recent developments in trusted hardware enclaves (such as Intel SGX) provide a much needed alternative. These hardware enclaves provide hardware-enforced shielded execution that allows…
Announcing Ground v0.1
We’re excited to be releasing v0.1 of the Ground project! Ground is a data context service. It is a central repository for all the information surrounding the use of data in an organization. Ground concerns itself with what data an organization has, where that data is, who (both human beings and software systems) is touching that data, and how that data is being modified and described. Above all, Ground aims to be an open-source, vendor neutral system that provides users an unopinionated metamodel and set of APIs that allow them to think about and interact with data context generated in their organization. Ground has many use cases, but we’re focused on two specific ones at present: Data Inventory: large organizations…
Reinforcement Learning brings together RISELab and Berkeley DeepDrive for a joint mini-retreat
On May 2, RISELab and the Berkeley DeepDrive (BDD) lab held a joint, largely student-driven mini-retreat. The event was aimed at exploring research opportunities at the intersection of the BDD and RISE labs. The topical focus of the mini-retreat was emerging AI applications, such as Reinforcement Learning (RL), and computer systems to support such applications. Trevor Darrell kicked off the event with an introduction to the Berkeley DeepDrive lab, followed by Ion Stoica’s overview of RISE. The event offered a great opportunity for researchers from both labs to exchange ideas about their ongoing research activity and discover points of collaboration. Philipp Moritz started the first student talk session with an update on Ray — a distributed execution framework for emerging…
RISELab Announces 3 Open Source Releases
Part of the Berkeley tradition—and the RISELab mission—is to release open source software as part of our research agenda. Six months after launching the lab, we’re excited to announce initial v0.1 releases of three RISElab open-source systems: Clipper, Ground and Ray. Clipper is an open-source prediction-serving system. Clipper simplifies deploying models from a wide range of machine learning frameworks by exposing a common REST interface and automatically ensuring low-latency and high-throughput predictions. In the 0.1 release, we focused on reliable support for serving models trained in Spark and Scikit-Learn. In the next release we will be introducing support for TensorFlow and Caffe2 as well as online-personalization and multi-armed bandits. We are providing active support for early users and will be following Github issues…
Declarative Heterogeneity Handling for Datacenter and ML Resources
Challenge Heterogeneity in datacenter resources has become the fact of life. We identify and categorize a number of different types of heterogeneity. When talking about heterogeneity, we generally refer to static or dynamic attributes associated with individual resources. Previously the levels of heterogeneity were fairly benign and limited to a few different types of processor architectures. Now, however, it has become a common trend to deploy hardware accelerators (e.g., Tesla K40/K80, Google TPU, Intel Xeon PHI) and even FPGAs (e.g., Microsoft Catapult project). Nodes themselves are connected with heterogeneous interconnects, oftentimes with more than one interconnect option available (e.g., 40Gbps ethernet backbone, Infiniband, FPGA torus topology). The workloads we consolidate on top of this diverse hardware differ vastly in their success metrics (completion…
Serverless Scientific Computing
For many scientific and engineering users, cloud infrastructure remains challenging to use. While many of their use cases are embarrassingly parallel, the challenges involved in provisioning and using stateful cloud services keep them trapped on their laptops or large shared workstations. Before getting started, a new cloud user confronts a bewildering number of choices. First, what instance type do they need ? How do they make the compute/memory tradeoff? How large do they want their cluster to be? Can they take advantage of dynamic market-based instances (spot instances) that can disappear at any time? What if they have 1000 small jobs, each of which takes a few minutes — what’s the most cost-effective way of allocating servers? What host operating…