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In this paper we build on our research in data management on native Flash storage. In particular we demonstrate the advantages of intelligent data placement strategies. To effectively manage phsical Flash space and organize the data on it, we utilize novel storage structures such as regions and groups. These are coupled to common DBMS logical structures, thus require no extra overhead for the DBA. The experimental results indicate an improvement of up to 2x, which doubles the longevity of Flash SSD. During the demonstration the audience can experience the advantages of the proposed approach on real Flash hardware.
Blockchains yield to new workloads in database management systems and K/V-stores. Distributed Ledger Technology (DLT) is a technique for managing transactions in ’trustless’ distributed systems. Yet, clients of nodes in blockchain networks are backed by ’trustworthy’ K/V-Stores, like LevelDB or RocksDB in Ethereum, which are based on Log-Structured Merge Trees (LSM Trees). However, LSM-Trees do not fully match the properties of blockchains and enterprise workloads.
In this paper, we claim that Partitioned B-Trees (PBT) fit the properties of this DLT: uniformly distributed hash keys, immutability, consensus, invalid blocks, unspent and off-chain transactions, reorganization and data state / version ordering in a distributed log-structure. PBT can locate records of newly inserted key-value pairs, as well as data of unspent transactions, in separate partitions in main memory. Once several blocks acquire consensus, PBTs evict a whole partition, which becomes immutable, to secondary storage. This behavior minimizes write amplification and enables a beneficial sequential write pattern on modern hardware. Furthermore, DLT implicate some type of log-based versioning. PBTs can serve as MV-store for data storage of logical blocks and indexing in multi-version concurrency control (MVCC) transaction processing.
Near-Data Processing is a promising approach to overcome the limitations of slow I/O interfaces in the quest to analyze the ever-growing amount of data stored in database systems. Next to CPUs, FPGAs will play an important role for the realization of functional units operating close to data stored in non-volatile memories such as Flash.It is essential that the NDP-device understands formats and layouts of the persistent data, to perform operations in-situ. To this end, carefully optimized format parsers and layout accessors are needed. However, designing such FPGA-based Near-Data Processing accelerators requires significant effort and expertise. To make FPGA-based Near-Data Processing accessible to non-FPGA experts, we will present a framework for the automatic generation of FPGA-based accelerators capable of data filtering and transformation for key-value stores based on simple data-format specifications.The evaluation shows that our framework is able to generate accelerators that are almost identical in performance compared to the manually optimized designs of prior work, while requiring little to no FPGA-specific knowledge and additionally providing improved flexibility and more powerful functionality.
Under update intensive workloads (TPC, LinkBench) small updates dominate the write behavior, e.g. 70% of all updates change less than 10 bytes across all TPC OLTP workloads. These are typically performed as in-place updates and result in random writes in page-granularity, causing major write-overhead on Flash storage, a write amplification of several hundred times and lower device longevity.
In this paper we propose an approach that transforms those small in-place updates into small update deltas that are appended to the original page. We utilize the commonly ignored fact that modern Flash memories (SLC, MLC, 3D NAND) can handle appends to already programmed physical pages by using various low-level techniques such as ISPP to avoid expensive erases and page migrations. Furthermore, we extend the traditional NSM page-layout with a delta-record area that can absorb those small updates. We propose a scheme to control the write behavior as well as the space allocation and sizing of database pages.
The proposed approach has been implemented under Shore- MT and evaluated on real Flash hardware (OpenSSD) and a Flash emulator. Compared to In-Page Logging it performs up to 62% less reads and writes and up to 74% less erases on a range of workloads. The experimental evaluation indicates: (i) significant reduction of erase operations resulting in twice the longevity of Flash devices under update-intensive workloads; (ii) 15%-60% lower read/write I/O latencies; (iii) up to 45% higher transactional throughput; (iv) 2x to 3x reduction in overall write
amplification.
A transaction is a demarcated sequence of application operations, for which the following properties are guaranteed by the underlying transaction processing system (TPS): atomicity, consistency, isolation, and durability (ACID). Transactions are therefore a general abstraction, provided by TPS that simplifies application development by relieving transactional applications from the burden of concurrency and failure handling. Apart from the ACID properties, a TPS must guarantee high and robust performance (high transactional throughput and low response times), high reliability (no data loss, ability to recover last consistent state, fault tolerance), and high availability (infrequent outages, short recovery times).
The architectures and workhorse algorithms of a high-performance TPS are built around the properties of the underlying hardware. The introduction of nonvolatile memories (NVM) as novel storage technology opens an entire new problem space, with the need to revise aspects such as the virtual memory hierarchy, storage management and data placement, access paths, and indexing. NVM are also referred to as storage-class memory (SCM).
In the present paper we demonstrate a novel approach to handling small updates on Flash called In-Place Appends (IPA). It allows the DBMS to revisit the traditional write behavior on Flash. Instead of writing whole database pages upon an update in an out-of-place manner on Flash, we transform those small updates into update deltas and append them to a reserved area on the very same physical Flash page. In doing so we utilize the commonly ignored fact that under certain conditions Flash memories can support in-place updates to Flash pages without a preceding erase operation.
The approach was implemented under Shore-MT and evaluated on real hardware. Under standard update-intensive workloads we observed 67% less page invalidations resulting in 80% lower garbage collection overhead, which yields a 45% increase in transactional throughput, while doubling Flash longevity at the same time. The IPA outperforms In-Page Logging (IPL) by more than 50%.
We showcase a Shore-MT based prototype of the above approach, operating on real Flash hardware – the OpenSSD Flash research platform. During the demonstration we allow the users to interact with the system and gain hands on experience of its performance under different demonstration scenarios. These involve various workloads such as TPC-B, TPC-C or TATP.
Database Management Systems (DBMS) need to handle large updatable datasets in on-line transaction processing (OLTP) workloads. Most modern DBMS provide snapshots of data in multi-version concurrency control (MVCC) transaction management scheme. Each transaction operates on a snapshot of the database, which is calculated from a set of tuple versions. High parallelism and resource-efficient append-only data placement on secondary storage is enabled. One major issue in indexing tuple versions on modern hardware technologies is the high write amplification for tree-indexes.
Partitioned B-Trees (PBT) [5] is based on the structure of the ubiquitous B+ Tree [8]. They achieve a near optimal write amplification and beneficial sequential writes on secondary storage. Yet they have not been implemented in a MVCC enabled DBMS to date.
In this paper we present the implementation of PBTs in PostgreSQL extended with SIAS. Compared to PostgreSQL’s B+–Trees PBTs have 50% better transaction throughput under TPC-C and a 30% improvement to standard PostgreSQL with Heap-Only Tuples.
Introduction to the special issue on self‑managing and hardware‑optimized database systems 2022
(2023)
Data management systems have evolved in terms of functionality, performance characteristics, complexity, and variety during the last 40 years. Particularly, the relational database management systems and the big data systems (e.g., Key-Value stores, Document stores, Graph stores and Graph Computation Systems, Spark, MapReduce/Hadoop, or Data Stream Processing Systems) have evolved with novel additions and extensions. However, the systems administration and tasks have become highly complex and expensive, especially given the simultaneous and rapid hardware evolution in processors, memory, storage, or networking. These developments present new open problems and challenges to data management systems as well as new opportunities.
The SMDB (International Workshop on Self-Managing Database Systems) and HardBD&Active (Joint International Workshop on Big Data Management on Emerging Hardware and Data Management on Virtualized Active Systems) workshops organized in conjunction with the IEEE ICDE (International Conference on Data Engineering) offered two distinct platforms for examining the above system-related challenges from different perspectives. The SMDB workshop looks into developing autonomic or self-* features in database and data management systems to tackle complex administrative tasks, while the HardBD&Active workshop focuses on harnessing hardware technologies to enhance efficiency and performance of data processing and management tasks. As a result of these workshops, we are delighted to present the third special issue of DAPD titled “Self-Managing and Hardware-Optimized Database Systems 2022,” which showcases the best contributions from the SMDB 2021/2022 and HardBD&Active 2021/2022 workshops.
We introduce IPA-IDX – an approach to handle index modifications modern storage technologies (NVM, Flash) as physical in-place appends, using simplified physiological log records. IPA-IDX provides similar performance and longevity advantages for indexes as basic IPA [5] does for tables. The selective application of IPA-IDX and basic IPA to certain regions and objects, lowers the GC overhead by over 60%, while keeping the total space overhead to 2%. The combined effect of IPA and IPA-IDX increases performance by 28%.
Nowadays almost every major company has a monitoring system and produces log data to analyse their systems. To perform analysation on the log data and to extract experience for future decisions it is important to transform and synchronize different time series. For synchronizing multiple time series several methods are provided so that they are leading to a synchronized uniform time series. This is achieved by using discretisation and approximation methodics. Furthermore the discretisation through ticks is demonstrated, as well as the respectivly illustrated results.