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1 .Foundations and Trends R in Databases Vol. 1, No. 2 (2007) 141–259 c 2007 J. M. Hellerstein, M. Stonebraker and J. Hamilton DOI: 10.1561/1900000002 Architecture of a Database System Joseph M. Hellerstein1 , Michael Stonebraker2 and James Hamilton3 1 University of California, Berkeley, USA, email@example.com 2 Massachusetts Institute of Technology, USA 3 Microsoft Research, USA Abstract Database Management Systems (DBMSs) are a ubiquitous and critical component of modern computing, and the result of decades of research and development in both academia and industry. Historically, DBMSs were among the earliest multi-user server systems to be developed, and thus pioneered many systems design techniques for scalability and relia- bility now in use in many other contexts. While many of the algorithms and abstractions used by a DBMS are textbook material, there has been relatively sparse coverage in the literature of the systems design issues that make a DBMS work. This paper presents an architectural dis- cussion of DBMS design principles, including process models, parallel architecture, storage system design, transaction system implementa- tion, query processor and optimizer architectures, and typical shared components and utilities. Successful commercial and open-source sys- tems are used as points of reference, particularly when multiple alter- native designs have been adopted by diﬀerent groups.
2 . 1 Introduction Database Management Systems (DBMSs) are complex, mission-critical software systems. Today’s DBMSs embody decades of academic and industrial research and intense corporate software development. Database systems were among the earliest widely deployed online server systems and, as such, have pioneered design solutions spanning not only data management, but also applications, operating systems, and net- worked services. The early DBMSs are among the most inﬂuential soft- ware systems in computer science, and the ideas and implementation issues pioneered for DBMSs are widely copied and reinvented. For a number of reasons, the lessons of database systems architec- ture are not as broadly known as they should be. First, the applied database systems community is fairly small. Since market forces only support a few competitors at the high end, only a handful of successful DBMS implementations exist. The community of people involved in designing and implementing database systems is tight: many attended the same schools, worked on the same inﬂuential research projects, and collaborated on the same commercial products. Second, academic treat- ment of database systems often ignores architectural issues. Textbook presentations of database systems traditionally focus on algorithmic 142
3 . 1.1 Relational Systems: The Life of a Query 143 and theoretical issues — which are natural to teach, study, and test — without a holistic discussion of system architecture in full implementa- tions. In sum, much conventional wisdom about how to build database systems is available, but little of it has been written down or commu- nicated broadly. In this paper, we attempt to capture the main architectural aspects of modern database systems, with a discussion of advanced topics. Some of these appear in the literature, and we provide references where appro- priate. Other issues are buried in product manuals, and some are simply part of the oral tradition of the community. Where applicable, we use commercial and open-source systems as examples of the various archi- tectural forms discussed. Space prevents, however, the enumeration of the exceptions and ﬁner nuances that have found their way into these multi-million line code bases, most of which are well over a decade old. Our goal here is to focus on overall system design and stress issues not typically discussed in textbooks, providing useful context for more widely known algorithms and concepts. We assume that the reader is familiar with textbook database systems material (e.g.,  or ) and with the basic facilities of modern operating systems such as UNIX, Linux, or Windows. After introducing the high-level architecture of a DBMS in the next section, we provide a number of references to back- ground reading on each of the components in Section 1.2. 1.1 Relational Systems: The Life of a Query The most mature and widely used database systems in production today are relational database management systems (RDBMSs). These systems can be found at the core of much of the world’s application infrastructure including e-commerce, medical records, billing, human resources, payroll, customer relationship management and supply chain management, to name a few. The advent of web-based commerce and community-oriented sites has only increased the volume and breadth of their use. Relational systems serve as the repositories of record behind nearly all online transactions and most online content management sys- tems (blogs, wikis, social networks, and the like). In addition to being important software infrastructure, relational database systems serve as
4 .144 Introduction Fig. 1.1 Main components of a DBMS. a well-understood point of reference for new extensions and revolutions in database systems that may arise in the future. As a result, we focus on relational database systems throughout this paper. At heart, a typical RDBMS has ﬁve main components, as illustrated in Figure 1.1. As an introduction to each of these components and the way they ﬁt together, we step through the life of a query in a database system. This also serves as an overview of the remaining sections of the paper. Consider a simple but typical database interaction at an airport, in which a gate agent clicks on a form to request the passenger list for a ﬂight. This button click results in a single-query transaction that works roughly as follows: 1. The personal computer at the airport gate (the “client”) calls an API that in turn communicates over a network to estab- lish a connection with the Client Communications Manager of a DBMS (top of Figure 1.1). In some cases, this connection
5 . 1.1 Relational Systems: The Life of a Query 145 is established between the client and the database server directly, e.g., via the ODBC or JDBC connectivity protocol. This arrangement is termed a “two-tier” or “client-server” system. In other cases, the client may communicate with a “middle-tier server” (a web server, transaction process- ing monitor, or the like), which in turn uses a protocol to proxy the communication between the client and the DBMS. This is usually called a “three-tier” system. In many web- based scenarios there is yet another “application server” tier between the web server and the DBMS, resulting in four tiers. Given these various options, a typical DBMS needs to be compatible with many diﬀerent connectivity protocols used by various client drivers and middleware systems. At base, however, the responsibility of the DBMS’ client com- munications manager in all these protocols is roughly the same: to establish and remember the connection state for the caller (be it a client or a middleware server), to respond to SQL commands from the caller, and to return both data and control messages (result codes, errors, etc.) as appro- priate. In our simple example, the communications manager would establish the security credentials of the client, set up state to remember the details of the new connection and the current SQL command across calls, and forward the client’s ﬁrst request deeper into the DBMS to be processed. 2. Upon receiving the client’s ﬁrst SQL command, the DBMS must assign a “thread of computation” to the command. It must also make sure that the thread’s data and control out- puts are connected via the communications manager to the client. These tasks are the job of the DBMS Process Man- ager (left side of Figure 1.1). The most important decision that the DBMS needs to make at this stage in the query regards admission control : whether the system should begin processing the query immediately, or defer execution until a time when enough system resources are available to devote to this query. We discuss Process Management in detail in Section 2.
6 .146 Introduction 3. Once admitted and allocated as a thread of control, the gate agent’s query can begin to execute. It does so by invoking the code in the Relational Query Processor (center, Figure 1.1). This set of modules checks that the user is authorized to run the query, and compiles the user’s SQL query text into an internal query plan. Once compiled, the resulting query plan is handled via the plan executor. The plan executor consists of a suite of “operators” (relational algorithm implementa- tions) for executing any query. Typical operators implement relational query processing tasks including joins, selection, projection, aggregation, sorting and so on, as well as calls to request data records from lower layers of the system. In our example query, a small subset of these operators — as assembled by the query optimization process — is invoked to satisfy the gate agent’s query. We discuss the query processor in Section 4. 4. At the base of the gate agent’s query plan, one or more operators exist to request data from the database. These operators make calls to fetch data from the DBMS’ Trans- actional Storage Manager (Figure 1.1, bottom), which man- ages all data access (read) and manipulation (create, update, delete) calls. The storage system includes algorithms and data structures for organizing and accessing data on disk (“access methods”), including basic structures like tables and indexes. It also includes a buﬀer management mod- ule that decides when and what data to transfer between disk and memory buﬀers. Returning to our example, in the course of accessing data in the access methods, the gate agent’s query must invoke the transaction management code to ensure the well-known “ACID” properties of transactions  (discussed in more detail in Section 5.1). Before access- ing data, locks are acquired from a lock manager to ensure correct execution in the face of other concurrent queries. If the gate agent’s query involved updates to the database, it would interact with the log manager to ensure that the trans- action was durable if committed, and fully undone if aborted.
7 . 1.1 Relational Systems: The Life of a Query 147 In Section 5, we discuss storage and buﬀer management in more detail; Section 6 covers the transactional consistency architecture. 5. At this point in the example query’s life, it has begun to access data records, and is ready to use them to compute results for the client. This is done by “unwinding the stack” of activities we described up to this point. The access meth- ods return control to the query executor’s operators, which orchestrate the computation of result tuples from database data; as result tuples are generated, they are placed in a buﬀer for the client communications manager, which ships the results back to the caller. For large result sets, the client typically will make additional calls to fetch more data incrementally from the query, resulting in multiple itera- tions through the communications manager, query execu- tor, and storage manager. In our simple example, at the end of the query the transaction is completed and the connec- tion closed; this results in the transaction manager cleaning up state for the transaction, the process manager freeing any control structures for the query, and the communi- cations manager cleaning up communication state for the connection. Our discussion of this example query touches on many of the key components in an RDBMS, but not all of them. The right-hand side of Figure 1.1 depicts a number of shared components and utilities that are vital to the operation of a full-function DBMS. The catalog and memory managers are invoked as utilities during any transaction, including our example query. The catalog is used by the query proces- sor during authentication, parsing, and query optimization. The mem- ory manager is used throughout the DBMS whenever memory needs to be dynamically allocated or deallocated. The remaining modules listed in the rightmost box of Figure 1.1 are utilities that run indepen- dently of any particular query, keeping the database as a whole well- tuned and reliable. We discuss these shared components and utilities in Section 7.
8 .148 Introduction 1.2 Scope and Overview In most of this paper, our focus is on architectural fundamentals sup- porting core database functionality. We do not attempt to provide a comprehensive review of database algorithmics that have been exten- sively documented in the literature. We also provide only minimal dis- cussion of many extensions present in modern DBMSs, most of which provide features beyond core data management but do not signiﬁcantly alter the system architecture. However, within the various sections of this paper we note topics of interest that are beyond the scope of the paper, and where possible we provide pointers to additional reading. We begin our discussion with an investigation of the overall archi- tecture of database systems. The ﬁrst topic in any server system archi- tecture is its overall process structure, and we explore a variety of viable alternatives on this front, ﬁrst for uniprocessor machines and then for the variety of parallel architectures available today. This discussion of core server system architecture is applicable to a variety of systems, but was to a large degree pioneered in DBMS design. Following this, we begin on the more domain-speciﬁc components of a DBMS. We start with a single query’s view of the system, focusing on the relational query processor. Following that, we move into the storage architecture and transactional storage management design. Finally, we present some of the shared components and utilities that exist in most DBMSs, but are rarely discussed in textbooks.
9 . 2 Process Models When designing any multi-user server, early decisions need to be made regarding the execution of concurrent user requests and how these are mapped to operating system processes or threads. These decisions have a profound inﬂuence on the software architecture of the system, and on its performance, scalability, and portability across operating systems.1 In this section, we survey a number of options for DBMS process mod- els, which serve as a template for many other highly concurrent server systems. We begin with a simpliﬁed framework, assuming the availabil- ity of good operating system support for threads, and we initially target only a uniprocessor system. We then expand on this simpliﬁed discus- sion to deal with the realities of how modern DBMSs implement their process models. In Section 3, we discuss techniques to exploit clusters of computers, as well as multi-processor and multi-core systems. The discussion that follows relies on these deﬁnitions: • An Operating System Process combines an operating system (OS) program execution unit (a thread of control) with an 1 Many but not all DBMSs are designed to be portable across a wide variety of host operating systems. Notable examples of OS-speciﬁc DBMSs are DB2 for zSeries and Microsoft SQL Server. Rather than using only widely available OS facilities, these products are free to exploit the unique facilities of their single host. 149
10 .150 Process Models address space private to the process. Included in the state maintained for a process are OS resource handles and the security context. This single unit of program execution is scheduled by the OS kernel and each process has its own unique address space. • An Operating System Thread is an OS program execution unit without additional private OS context and without a private address space. Each OS thread has full access to the memory of other threads executing within the same multi- threaded OS Process. Thread execution is scheduled by the operating system kernel scheduler and these threads are often called “kernel threads” or k-threads. • A Lightweight Thread Package is an application-level con- struct that supports multiple threads within a single OS process. Unlike OS threads scheduled by the OS, lightweight threads are scheduled by an application-level thread sched- uler. The diﬀerence between a lightweight thread and a kernel thread is that a lightweight thread is scheduled in user-space without kernel scheduler involvement or knowl- edge. The combination of the user-space scheduler and all of its lightweight threads run within a single OS process and appears to the OS scheduler as a single thread of execution. Lightweight threads have the advantage of faster thread switches when compared to OS threads since there is no need to do an OS kernel mode switch to schedule the next thread. Lightweight threads have the disadvantage, how- ever, that any blocking operation such as a synchronous I/O by any thread will block all threads in the process. This prevents any of the other threads from making progress while one thread is blocked waiting for an OS resource. Lightweight thread packages avoid this by (1) issuing only asynchronous (non-blocking) I/O requests and (2) not invoking any OS operations that could block. Generally, lightweight threads oﬀer a more diﬃcult programming model than writing software based on either OS processes or OS threads.
11 . 151 • Some DBMSs implement their own lightweight thread (LWT) packages. These are a special case of general LWT packages. We refer to these threads as DBMS threads and simply threads when the distinction between DBMS, general LWT, and OS threads are unimportant to the discussion. • A DBMS Client is the software component that implements the API used by application programs to communicate with a DBMS. Some example database access APIs are JDBC, ODBC, and OLE/DB. In addition, there are a wide vari- ety of proprietary database access API sets. Some programs are written using embedded SQL, a technique of mixing pro- gramming language statements with database access state- ments. This was ﬁrst delivered in IBM COBOL and PL/I and, much later, in SQL/J which implements embedded SQL for Java. Embedded SQL is processed by preproces- sors that translate the embedded SQL statements into direct calls to data access APIs. Whatever the syntax used in the client program, the end result is a sequence of calls to the DBMS data access APIs. Calls made to these APIs are marshaled by the DBMS client component and sent to the DBMS over some communications protocol. The proto- cols are usually proprietary and often undocumented. In the past, there have been several eﬀorts to standardize client-to- database communication protocols, with Open Group DRDA being perhaps the best known, but none have achieved broad adoption. • A DBMS Worker is the thread of execution in the DBMS that does work on behalf of a DBMS Client. A 1:1 map- ping exists between a DBMS worker and a DBMS Client: the DBMS worker handles all SQL requests from a single DBMS Client. The DBMS client sends SQL requests to the DBMS server. The worker executes each request and returns the result to the client. In what follows, we investigate the diﬀerent approaches commercial DBMSs use to map DBMS workers onto OS threads or processes. When the distinction is
12 .152 Process Models signiﬁcant, we will refer to them as worker threads or worker processes. Otherwise, we refer to them simply as workers or DBMS workers. 2.1 Uniprocessors and Lightweight Threads In this subsection, we outline a simpliﬁed DBMS process model taxon- omy. Few leading DBMSs are architected exactly as described in this section, but the material forms the basis from which we will discuss cur- rent generation production systems in more detail. Each of the leading database systems today is, at its core, an extension or enhancement of at least one of the models presented here. We start by making two simplifying assumptions (which we will relax in subsequent sections): 1. OS thread support: We assume that the OS provides us with eﬃcient support for kernel threads and that a process can have a very large number of threads. We also assume that the memory overhead of each thread is small and that the context switches are inexpensive. This is arguably true on a number of modern OS today, but was certainly not true when most DBMSs were ﬁrst designed. Because OS threads either were not available or scaled poorly on some platforms, many DBMSs are implemented without using the underlying OS thread support. 2. Uniprocessor hardware: We will assume that we are design- ing for a single machine with a single CPU. Given the ubiq- uity of multi-core systems, this is an unrealistic assumption even at the low end. This assumption, however, will simplify our initial discussion. In this simpliﬁed context, a DBMS has three natural process model options. From the simplest to the most complex, these are: (1) process per DBMS worker, (2) thread per DBMS worker, and (3) process pool. Although these models are simpliﬁed, all three are in use by commercial DBMS systems today.
13 . 2.1 Uniprocessors and Lightweight Threads 153 2.1.1 Process per DBMS Worker The process per DBMS worker model (Figure 2.1) was used by early DBMS implementations and is still used by many commercial systems today. This model is relatively easy to implement since DBMS work- ers are mapped directly onto OS processes. The OS scheduler man- ages the timesharing of DBMS workers and the DBMS programmer can rely on OS protection facilities to isolate standard bugs like mem- ory overruns. Moreover, various programming tools like debuggers and memory checkers are well-suited to this process model. Complicating this model are the in-memory data structures that are shared across DBMS connections, including the lock table and buﬀer pool (discussed in more detail in Sections 6.3 and 5.3, respectively). These shared data structures must be explicitly allocated in OS-supported shared memory accessible across all DBMS processes. This requires OS support (which is widely available) and some special DBMS coding. In practice, the Fig. 2.1 Process per DBMS worker model: each DBMS worker is implemented as an OS process.
14 .154 Process Models required extensive use of shared memory in this model reduces some of the advantages of address space separation, given that a good fraction of “interesting” memory is shared across processes. In terms of scaling to very large numbers of concurrent connections, process per DBMS worker is not the most attractive process model. The scaling issues arise because a process has more state than a thread and consequently consumes more memory. A process switch requires switch- ing security context, memory manager state, ﬁle and network handle tables, and other process context. This is not needed with a thread switch. Nonetheless, the process per DBMS worker model remains pop- ular and is supported by IBM DB2, PostgreSQL, and Oracle. 2.1.2 Thread per DBMS Worker In the thread per DBMS worker model (Figure 2.2), a single multi- threaded process hosts all the DBMS worker activity. A dispatcher Fig. 2.2 Thread per DBMS worker model: each DBMS worker is implemented as an OS thread.
15 . 2.1 Uniprocessors and Lightweight Threads 155 thread (or a small handful of such threads) listens for new DBMS client connections. Each connection is allocated a new thread. As each client submits SQL requests, the request is executed entirely by its corre- sponding thread running a DBMS worker. This thread runs within the DBMS process and, once complete, the result is returned to the client and the thread waits on the connection for the next request from that same client. The usual multi-threaded programming challenges arise in this architecture: the OS does not protect threads from each other’s mem- ory overruns and stray pointers; debugging is tricky, especially with race conditions; and the software can be diﬃcult to port across OS due to diﬀerences in threading interfaces and multi-threaded scaling. Many of the multi-programming challenges of the thread per DBMS worker model are also found in the process per DBMS worker model due to the extensive use of shared memory. Although thread API diﬀerences across OSs have been minimized in recent years, subtle distinctions across platforms still cause hassles in debugging and tuning. Ignoring these implementation diﬃculties, the thread per DBMS worker model scales well to large numbers of con- current connections and is used in some current-generation production DBMS systems, including IBM DB2, Microsoft SQL Server, MySQL, Informix, and Sybase. 2.1.3 Process Pool This model is a variant of process per DBMS worker. Recall that the advantage of process per DBMS worker was its implementation sim- plicity. But the memory overhead of each connection requiring a full process is a clear disadvantage. With process pool (Figure 2.3), rather than allocating a full process per DBMS worker, they are hosted by a pool of processes. A central process holds all DBMS client connections and, as each SQL request comes in from a client, the request is given to one of the processes in the process pool. The SQL Statement is executed through to completion, the result is returned to the database client, and the process is returned to the pool to be allocated to the next request. The process pool size is bounded and often ﬁxed. If a request comes in
16 .156 Process Models Fig. 2.3 Process Pool: each DBMS Worker is allocated to one of a pool of OS processes as work requests arrive from the Client and the process is returned to the pool once the request is processed. and all processes are already servicing other requests, the new request must wait for a process to become available. Process pool has all of the advantages of process per DBMS worker but, since a much smaller number of processes are required, is consid- erably more memory eﬃcient. Process pool is often implemented with a dynamically resizable process pool where the pool grows potentially to some maximum number when a large number of concurrent requests arrive. When the request load is lighter, the process pool can be reduced to fewer waiting processes. As with thread per DBMS worker, the pro- cess pool model is also supported by a several current generation DBMS in use today. 2.1.4 Shared Data and Process Boundaries All models described above aim to execute concurrent client requests as independently as possible. Yet, full DBMS worker independence and isolation is not possible, since they are operating on the same shared
17 . 2.1 Uniprocessors and Lightweight Threads 157 database. In the thread per DBMS worker model, data sharing is easy with all threads run in the same address space. In other models, shared memory is used for shared data structures and state. In all three mod- els, data must be moved from the DBMS to the clients. This implies that all SQL requests need to be moved into the server processes and that all results for return to the client need to be moved back out. How is this done? The short answer is that various buﬀers are used. The two major types are disk I/O buﬀers and client communication buﬀers. We describe these buﬀers here, and brieﬂy discuss policies for managing them. Disk I/O buﬀers: The most common cross-worker data dependencies are reads and writes to the shared data store. Consequently, I/O inter- actions between DBMS workers are common. There are two sepa- rate disk I/O scenarios to consider: (1) database requests and (2) log requests. • Database I/O Requests: The Buﬀer Pool. All persistent database data is staged through the DBMS buﬀer pool (Section 5.3). With thread per DBMS worker, the buﬀer pool is simply a heap-resident data structure available to all threads in the shared DBMS address space. In the other two models, the buﬀer pool is allocated in shared memory available to all processes. The end result in all three DBMS models is that the buﬀer pool is a large shared data struc- ture available to all database threads and/or processes. When a thread needs a page to be read in from the database, it generates an I/O request specifying the disk address, and a handle to a free memory location (frame) in the buﬀer pool where the result can be placed. To ﬂush a buﬀer pool page to disk, a thread generates an I/O request that includes the page’s current frame in the buﬀer pool, and its destination address on disk. Buﬀer pools are discussed in more detail in Section 4.3. • Log I/O Requests: The Log Tail. The database log (Section 6.4) is an array of entries stored on one or more disks. As log entries are generated during transaction
18 .158 Process Models processing, they are staged to an in-memory queue that is periodically ﬂushed to the log disk(s) in FIFO order. This queue is usually called the log tail. In many systems, a separate process or thread is responsible for periodically ﬂushing the log tail to the disk. With thread per DBMS worker, the log tail is simply a heap-resident data structure. In the other two models, two diﬀerent design choices are common. In one approach, a separate process manages the log. Log records are com- municated to the log manager by shared memory or any other eﬃcient inter-process communications protocol. In the other approach, the log tail is allocated in shared memory in much the same way as the buﬀer pool was handled above. The key point is that all threads and/or processes executing database client requests need to be able to request that log records be written and that the log tail be ﬂushed. An important type of log ﬂush is the commit transaction ﬂush. A transaction cannot be reported as successfully committed until a commit log record is ﬂushed to the log device. This means that client code waits until the commit log record is ﬂushed, and that DBMS server code must hold all resources (e.g., locks) until that time as well. Log ﬂush requests may be postponed for a time to allow the batching of commit records in a single I/O request (“group commit”). Client communication buﬀers: SQL is typically used in a “pull” model: clients consume result tuples from a query cursor by repeatedly issuing the SQL FETCH request, which retrieve one or more tuples per request. Most DBMSs try to work ahead of the stream of FETCH requests to enqueue results in advance of client requests. In order to support this prefetching behavior, the DBMS worker may use the client communications socket as a queue for the tuples it produces. More complex approaches implement client-side cursor caching and use the DBMS client to store results likely to be fetched
19 . 2.2 DBMS Threads 159 in the near future rather than relying on the OS communications buﬀers. Lock table: The lock table is shared by all DBMS workers and is used by the Lock Manager (Section 6.3) to implement database lock- ing semantics. The techniques for sharing the lock table are the same as those of the buﬀer pool and these same techniques can be used to support any other shared data structures needed by the DBMS implementation. 2.2 DBMS Threads The previous section provided a simpliﬁed description of DBMS process models. We assumed the availability of high-performance OS threads and that the DBMS would target only uniprocessor systems. In the remainder of this section, we relax the ﬁrst of those assumptions and describe the impact on DBMS implementations. Multi-processing and parallelism are discussed in the next section. 2.2.1 DBMS Threads Most of today’s DBMSs have their roots in research systems from the 1970s and commercialization eﬀorts from the 1980s. Standard OS fea- tures that we take for granted today were often unavailable to DBMS developers when the original database systems were built. Eﬃcient, high-scale OS thread support is perhaps the most signiﬁcant of these. It was not until the 1990s that OS threads were widely implemented and, where they did exist, the implementations varied greatly. Even today, some OS thread implementations do not scale well enough to support all DBMS workloads well [31, 48, 93, 94]. Hence for legacy, portability, and scalability reasons, many widely used DBMS do not depend upon OS threads in their implementa- tions. Some avoid threads altogether and use the process per DBMS worker or the process pool model. Those implementing the remaining process model choice, the thread per DBMS worker model, need a solu- tion for those OS without good kernel thread implementations. One means of addressing this problem adopted by several leading DBMSs
20 .160 Process Models was to implement their own proprietary, lightweight thread package. These lightweight threads, or DBMS threads, replace the role of the OS threads described in the previous section. Each DBMS thread is programmed to manage its own state, to perform all potentially block- ing operations (e.g., I/Os) via non-blocking, asynchronous interfaces, and to frequently yield control to a scheduling routine that dispatches among these tasks. Lightweight threads are an old idea that is discussed in a retro- spective sense in , and are widely used in event-loop programming for user interfaces. The concept has been revisited frequently in the recent OS literature [31, 48, 93, 94]. This architecture provides fast task-switching and ease of porting, at the expense of replicating a good deal of OS logic in the DBMS (task-switching, thread state manage- ment, scheduling, etc.) . 2.3 Standard Practice In leading DBMSs today, we ﬁnd representatives of all three of the architectures we introduced in Section 2.1 and some interesting varia- tions thereof. In this dimension, IBM DB2 is perhaps the most interest- ing example in that it supports four distinct process models. On OSs with good thread support, DB2 defaults to thread per DBMS worker and optionally supports DBMS workers multiplexed over a thread pool. When running on OSs without scalable thread support, DB2 defaults to process per DBMS worker and optionally supports DBMS worker multiplexed over a process pool. Summarizing the process models supported by IBM DB2, MySQL, Oracle, PostgreSQL, and Microsoft SQL Server: Process per DBMS worker : This is the most straight-forward process model and is still heavily used today. DB2 defaults to process per DBMS worker on OSs that do not support high quality, scalable OS threads and thread per DBMS worker on those that do. This is also the default Oracle process model. Oracle also supports process pool as described below as an optional model. PostgreSQL runs the process per DBMS worker model exclusively on all supported operating systems.
21 . 2.3 Standard Practice 161 Thread per DBMS worker : This is an eﬃcient model with two major variants in use today: 1. OS thread per DBMS worker : IBM DB2 defaults to this model when running on systems with good OS thread sup- port and this is the model used by MySQL. 2. DBMS thread per DBMS worker : In this model, DBMS workers are scheduled by a lightweight thread scheduler on either OS processes or OS threads. This model avoids any potential OS scheduler scaling or performance problems at the expense of high implementation costs, poor development tools support, and substantial long-standing software main- tenance costs for the DBMS vendor. There are two main sub-categories of this model: a. DBMS threads scheduled on OS process: A lightweight thread scheduler is hosted by one or more OS processes. Sybase supports this model as does Informix. All current generation systems using this model implement a DBMS thread scheduler that schedules DBMS workers over multiple OS processes to exploit multiple processors. However, not all DBMSs using this model have implemented thread migration: the ability to reassign an existing DBMS thread to a diﬀerent OS process (e.g., for load balancing). b. DBMS threads scheduled on OS threads: Microsoft SQL Server supports this model as a non-default option (default is DBMS workers multiplexed over a thread pool described below). This SQL Server option, called Fibers, is used in some high scale transaction processing benchmarks but, otherwise, is in fairly light use. Process/thread pool : In this model, DBMS workers are multiplexed over a pool of processes. As OS thread support has improved, a second variant of this model
22 .162 Process Models has emerged based upon a thread pool rather than a process pool. In this latter model, DBMS workers are multiplexed over a pool of OS threads: 1. DBMS workers multiplexed over a process pool : This model is much more memory eﬃcient than process per DBMS worker, is easy to port to OSs without good OS thread sup- port, and scales very well to large numbers of users. This is the optional model supported by Oracle and the one they rec- ommend for systems with large numbers of concurrently con- nected users. The Oracle default model is process per DBMS worker. Both of the options supported by Oracle are easy to support on the vast number of diﬀerent OSs they target (at one point Oracle supported over 80 target OSs). 2. DBMS workers multiplexed over a thread pool : Microsoft SQL Server defaults to this model and over 99% of the SQL Server installations run this way. To eﬃciently support tens of thousands of concurrently connected users, as mentioned above, SQL Server optionally supports DBMS threads sched- uled on OS threads. As we discuss in the next section, most current generation com- mercial DBMSs support intra-query parallelism: the ability to execute all or parts of a single query on multiple processors in parallel. For the purposes of our discussion in this section, intra-query parallelism is the temporary assignment of multiple DBMS workers to a single SQL query. The underlying process model is not impacted by this feature in any way other than that a single client connection may have more than a single DBMS worker executing on its behalf. 2.4 Admission Control We close this section with one remaining issue related to supporting multiple concurrent requests. As the workload in any multi-user system increases, throughput will increase up to some maximum. Beyond this point, it will begin to decrease radically as the system starts to thrash. As with OSs, thrashing is often the result of memory pressure: the
23 . 2.4 Admission Control 163 DBMS cannot keep the “working set” of database pages in the buﬀer pool, and spends all its time replacing pages. In DBMSs, this is particu- larly a problem with query processing techniques like sorting and hash joins that tend to consume large amounts of main memory. In some cases, DBMS thrashing can also occur due to contention for locks: trans- actions continually deadlock with each other and need to be rolled back and restarted . Hence any good multi-user system has an admission control policy, which does not accept new work unless suﬃcient DBMS resources are available. With a good admission controller, a system will display graceful degradation under overload: transaction latencies will increase proportionally to the arrival rate, but throughput will remain at peak. Admission control for a DBMS can be done in two tiers. First, a simple admission control policy may be in the dispatcher process to ensure that the number of client connections is kept below a threshold. This serves to prevent overconsumption of basic resources like network connections. In some DBMSs this control is not provided, under the assumption that it is handled by another tier of a multi-tier system, e.g., application servers, transaction processing monitors, or web servers. The second layer of admission control must be implemented directly within the core DBMS relational query processor. This execution admission controller runs after the query is parsed and optimized, and determines whether a query is postponed, begins execution with fewer resources, or begins execution without additional constraints. The exe- cution admission controller is aided by information from the query optimizer that estimates the resources that a query will require and the current availability of system resources. In particular, the opti- mizer’s query plan can specify (1) the disk devices that the query will access, and an estimate of the number of random and sequential I/Os per device, (2) estimates of the CPU load of the query based on the operators in the query plan and the number of tuples to be processed, and, most importantly (3) estimates about the memory footprint of the query data structures, including space for sorting and hashing large inputs during joins and other query execution tasks. As noted above, this last metric is often the key for an admission controller, since memory pressure is typically the main cause of thrashing. Hence
24 .164 Process Models many DBMSs use memory footprint and the number of active DBMS workers as the main criterion for admission control. 2.5 Discussion and Additional Material Process model selection has a substantial inﬂuence on DBMS scaling and portability. As a consequence, three of the more broadly used com- mercial systems each support more than one process model across their product line. From an engineering perspective, it would clearly be much simpler to employ a single process model across all OSs and at all scal- ing levels. But, due to the vast diversity of usage patterns and the non-uniformity of the target OSs, each of these three DBMSs have elected to support multiple models. Looking forward, there has been signiﬁcant interest in recent years in new process models for server systems, motivated by changes in hardware bottlenecks, and by the scale and variability of workload on the Internet well [31, 48, 93, 94]. One theme emerging in these designs is to break down a server system into a set of independently scheduled “engines,” with messages passed asynchronously and in bulk between these engines. This is something like the “process pool” model above, in that worker units are reused across multiple requests. The main novelty in this recent research is to break the functional granules of work in a more narrowly scoped task-speciﬁc manner than was done before. This results in many-to-many relationship between workers and SQL requests — a single query is processed via activities in multiple workers, and each worker does its own specialized tasks for many SQL requests. This architecture enables more ﬂexible scheduling choices — e.g., it allows dynamic trade-oﬀs between allowing a single worker to complete tasks for many queries (perhaps to improve overall system throughput), or to allow a query to make progress among multiple workers (to improve that query’s latency). In some cases this has been shown to have advantages in processor cache locality, and in the ability to keep the CPU busy from idling during cache misses in hardware. Further investigation of this idea in the DBMS context is typiﬁed by the StagedDB research project , which is a good starting point for additional reading.
25 . 3 Parallel Architecture: Processes and Memory Coordination Parallel hardware is a fact of life in modern servers and comes in a variety of conﬁgurations. In this section, we summarize the standard DBMS terminology (introduced in ), and discuss the process models and memory coordination issues in each. 3.1 Shared Memory A shared-memory parallel system (Figure 3.1) is one in which all pro- cessors can access the same RAM and disk with roughly the same performance. This architecture is fairly standard today — most server hardware ships with between two and eight processors. High-end machines can ship with dozens of processors, but tend to be sold at a large premium relative to the processing resources provided. Highly parallel shared-memory machines are one of the last remaining “cash cows” in the hardware industry, and are used heavily in high-end online transaction processing applications. The cost of server hardware is usu- ally dwarfed by costs of administering the systems, so the expense of 165
26 .166 Parallel Architecture: Processes and Memory Coordination Fig. 3.1 Shared-memory architecture. buying a smaller number of large, very expensive systems is sometimes viewed to be an acceptable trade-oﬀ.1 Multi-core processors support multiple processing cores on a sin- gle chip and share some infrastructure such as caches and the memory bus. This makes them quite similar to a shared-memory architecture in terms of their programming model. Today, nearly all serious database deployments involve multiple processors, with each processor having more than one CPU. DBMS architectures need to be able to fully exploit this potential parallelism. Fortunately, all three of the DBMS architectures described in Section 2 run well on modern shared-memory hardware architectures. The process model for shared-memory machines follows quite naturally from the uniprocessor approach. In fact, most database systems evolved from their initial uniprocessor implementations to shared-memory implementations. On shared-memory machines, the OS typically supports the transparent assignment of workers (processes or 1 The dominant cost for DBMS customers is typically paying qualiﬁed people to adminis- ter high-end systems. This includes Database Administrators (DBAs) who conﬁgure and maintain the DBMS, and System Administrators who conﬁgure and maintain the hard- ware and operating systems.
27 . 3.2 Shared-Nothing 167 threads) across the processors, and the shared data structures continue to be accessible to all. All three models run well on these systems and support the execution of multiple, independent SQL requests in paral- lel. The main challenge is to modify the query execution layers to take advantage of the ability to parallelize a single query across multiple CPUs; we defer this to Section 5. 3.2 Shared-Nothing A shared-nothing parallel system (Figure 3.2) is made up of a cluster of independent machines that communicate over a high-speed network interconnect or, increasingly frequently, over commodity networking components. There is no way for a given system to directly access the memory or disk of another system. Shared-nothing systems provide no hardware sharing abstractions, leaving coordination of the various machines entirely in the hands of the DBMS. The most common technique employed by DBMSs to support these clusters is to run their standard process model on each machine, or node, in the cluster. Each node is capable of accepting client SQL Fig. 3.2 Shared-nothing architecture.
28 .168 Parallel Architecture: Processes and Memory Coordination requests, accessing necessary metadata, compiling SQL requests, and performing data access just as on a single shared memory system as described above. The main diﬀerence is that each system in the cluster stores only a portion of the data. Rather than running the queries they receive against their local data only, the requests are sent to other members of the cluster and all machines involved execute the query in parallel against the data they are storing. The tables are spread over multiple systems in the cluster using horizontal data partitioning to allow each processor to execute independently of the others. Each tuple in the database is assigned to an individual machine, and hence each table is sliced “horizontally” and spread across the machines. Typical data partitioning schemes include hash-based parti- tioning by tuple attribute, range-based partitioning by tuple attribute, round-robin, and hybrid which is a combination of both range-based and hash-based. Each individual machine is responsible for the access, locking and logging of the data on its local disks. During query execu- tion, the query optimizer chooses how to horizontally re-partition tables and intermediate results across the machines to satisfy the query, and it assigns each machine a logical partition of the work. The query execu- tors on the various machines ship data requests and tuples to each other, but do not need to transfer any thread state or other low-level information. As a result of this value-based partitioning of the database tuples, minimal coordination is required in these systems. Good par- titioning of the data is required, however, for good performance. This places a signiﬁcant burden on the Database Administrator (DBA) to lay out tables intelligently, and on the query optimizer to do a good job partitioning the workload. This simple partitioning solution does not handle all issues in the DBMS. For example, explicit cross-processor coordination must take place to handle transaction completion, provide load balancing, and support certain maintenance tasks. For example, the processors must exchange explicit control messages for issues like distributed deadlock detection and two-phase commit . This requires additional logic, and can be a performance bottleneck if not done carefully. Also, partial failure is a possibility that has to be managed in a shared-nothing system. In a shared-memory system, the failure of a
29 . 3.2 Shared-Nothing 169 processor typically results in shutdown of the entire machine, and hence the entire DBMS. In a shared-nothing system, the failure of a single node will not necessarily aﬀect other nodes in the cluster. But it will certainly aﬀect the overall behavior of the DBMS, since the failed node hosts some fraction of the data in the database. There are at least three possible approaches in this scenario. The ﬁrst is to bring down all nodes if any node fails; this in essence emulates what would hap- pen in a shared-memory system. The second approach, which Informix dubbed “Data Skip,” allows queries to be executed on any nodes that are up, “skipping” the data on the failed node. This is useful in sce- narios where data availability is more important than completeness of results. But best-eﬀort results do not have well-deﬁned semantics, and for many workloads this is not a useful choice — particularly because the DBMS is often used as the “repository of record” in a multi-tier system, and availability-vs-consistency trade-oﬀs tend to get done in a higher tier (often in an application server). The third approach is to employ redundancy schemes ranging from full database failover (requir- ing double the number of machines and software licenses) to ﬁne-grain redundancy like chained declustering . In this latter technique, tuple copies are spread across multiple nodes in the cluster. The advantage of chained declustering over simpler schemes is that (a) it requires fewer machines to be deployed to guarantee availability than na¨ıve schemes, and (b) when a node does fails, the system load is distributed fairly evenly over the remaining nodes: the n − 1 remaining nodes each do n/(n − 1) of the original work, and this form of linear degrada- tion in performance continues as nodes fail. In practice, most current generation commercial systems are somewhere in the middle, nei- ther as coarse-grained as full database redundancy nor as ﬁne-grained as chained declustering The shared-nothing architecture is fairly common today, and has unbeatable scalability and cost characteristics. It is mostly used at the extreme high end, typically for decision-support applications and data warehouses. In an interesting combination of hardware architectures, a shared-nothing cluster is often made up of many nodes each of which is a shared-memory multi-processors.