Looking for:
Berkeley db free download for windows |
The Architecture of Open Source Applications: Berkeley DB
Berkeley db free download for windows
A conflict matrix defines the different types of locks present in the system and how they interact. Let's call the entity holding a lock, the holder and the entity requesting a lock the requester, and let's also assume that the holder and requester have different locker ids.
The conflict matrix is an array indexed by [requester][holder] , where each entry contains a zero if there is no conflict, indicating that the requested lock can be granted, and a one if there is a conflict, indicating that the request cannot be granted. The lock manager contains a default conflict matrix, which happens to be exactly what Berkeley DB needs, however, an application is free to design its own lock modes and conflict matrix to suit its own purposes.
The only requirement on the conflict matrix is that it is square it has the same number of rows and columns and that the application use 0-based sequential integers to describe its lock modes e.
Table 4. Before explaining the different lock modes in the Berkeley DB conflict matrix, let's talk about how the locking subsystem supports hierarchical locking. Hierarchical locking is the ability to lock different items within a containment hierarchy. For example, files contain pages, while pages contain individual elements. When modifying a single page element in a hierarchical locking system, we want to lock just that element; if we were modifying every element on the page, it would be more efficient to simply lock the page, and if we were modifying every page in a file, it would be best to lock the entire file.
Additionally, hierarchical locking must understand the hierarchy of the containers because locking a page also says something about locking the file: you cannot modify the file that contains a page at the same time that pages in the file are being modified. The question then is how to allow different lockers to lock at different hierarchical levels without chaos resulting. The answer lies in a construct called an intention lock. A locker acquires an intention lock on a container to indicate the intention to lock things within that container.
So, obtaining a read-lock on a page implies obtaining an intention-to-read lock on the file. Similarly, to write a single page element, you must acquire an intention-to-write lock on both the page and the file. In the conflict matrix above, the iRead , iWrite , and iWR locks are all intention locks that indicate an intention to read, write or do both, respectively. Therefore, when performing hierarchical locking, rather than requesting a single lock on something, it is necessary to request potentially many locks: the lock on the actual entity as well as intention locks on any containing entities.
Although Berkeley DB doesn't use hierarchical locking internally, it takes advantage of the ability to specify different conflict matrices, and the ability to specify multiple lock requests at once. We use the default conflict matrix when providing transactional support, but a different conflict matrix to provide simple concurrent access without transaction and recovery support.
In lock coupling, you hold one lock only long enough to acquire the next lock. That is, you lock an internal Btree page only long enough to read the information that allows you to select and lock a page at the next level. Berkeley DB's general-purpose design was well rewarded when we added concurrent data store functionality. Initially Berkeley DB provided only two modes of operation: either you ran without any write concurrency or with full transaction support.
Transaction support carries a certain degree of complexity for the developer and we found some applications wanted improved concurrency without the overhead of full transactional support. To provide this feature, we added support for API-level locking that allows concurrency, while guaranteeing no deadlocks.
This required a new and different lock mode to work in the presence of cursors. Rather than adding special purpose code to the lock manager, we were able to create an alternate lock matrix that supported only the lock modes necessary for the API-level locking.
Thus, simply by configuring the lock manager differently, we were able provide the locking support we needed. Sadly, it was not as easy to change the access methods; there are still significant parts of the access method code to handle this special mode of concurrent access.
The log manager provides the abstraction of a structured, append-only file. As with the other modules, we intended to design a general-purpose logging facility, however the logging subsystem is probably the module where we were least successful. When you find an architectural problem you don't want to fix "right now" and that you're inclined to just let go, remember that being nibbled to death by ducks will kill you just as surely as being trampled by elephants.
Don't be too hesitant to change entire frameworks to improve software structure, and when you make the changes, don't make a partial change with the idea that you'll clean up later—do it all and then move forward. As has been often repeated, "If you don't have the time to do it right now, you won't find the time to do it later. A log is conceptually quite simple: it takes opaque byte strings and writes them sequentially to a file, assigning each a unique identifier, called a log sequence number LSN.
Additionally, the log must provide efficient forward and backward traversal and retrieval by LSN. There are two tricky parts: first, the log must guarantee it is in a consistent state after any possible failure where consistent means it contains a contiguous sequence of uncorrupted log records ; second, because log records must be written to stable storage for transactions to commit, the performance of the log is usually what bounds the performance of any transactional application.
As the log is an append-only data structure, it can grow without bound. We implement the log as a collection of sequentially numbered files, so log space may be reclaimed by simply removing old log files. Given the multi-file architecture of the log, we form LSNs as pairs specifying a file number and offset within the file.
Thus, given an LSN, it is trivial for the log manager to locate the record: it seeks to the given offset of the given log file and returns the record written at that location.
But how does the log manager know how many bytes to return from that location? The log must persist per-record metadata so that, given an LSN, the log manager can determine the size of the record to return. At a minimum, it needs to know the length of the record. We prepend every log record with a log record header containing the record's length, the offset of the previous record to facilitate backward traversal , and a checksum for the log record to identify log corruption and the end of the log file.
This metadata is sufficient for the log manager to maintain the sequence of log records, but it is not sufficient to actually implement recovery; that functionality is encoded in the contents of log records and in how Berkeley DB uses those log records. Berkeley DB uses the log manager to write before- and after-images of data before updating items in the database [ HR83 ].
These log records contain enough information to either redo or undo operations on the database. Berkeley DB then uses the log both for transaction abort that is, undoing any effects of a transaction when the transaction is discarded and recovery after application or system failure. Only then does Mpool write the page to disk.
Mpool and Log use internal handle methods to facilitate write-ahead logging, and in some cases, the method declaration is longer than the code it runs, since the code is often comparing two integral values and nothing more. Why bother with such insignificant methods, just to maintain consistent layering? Because if your code is not so object-oriented as to make your teeth hurt, it is not object-oriented enough. Every piece of code should do a small number of things and there should be a high-level design encouraging programmers to build functionality out of smaller chunks of functionality, and so on.
If there's anything we have learned about software development in the past few decades, it is that our ability to build and maintain significant pieces of software is fragile. Building and maintaining significant pieces of software is difficult and error-prone, and as the software architect, you must do everything that you can, as early as you can, as often as you can, to maximize the information conveyed in the structure of your software. Berkeley DB imposes structure on the log records to facilitate recovery.
Most Berkeley DB log records describe transactional updates. Thus, most log records correspond to page modifications to a database, performed on behalf of a transaction. This description provides the basis for identifying what metadata Berkeley DB must attach to each log record: a database, a transaction, and a record type. The transaction identifier and record type fields are present in every record at the same location.
This allows the recovery system to extract a record type and dispatch the record to an appropriate handler that can interpret the record and perform appropriate actions. The transaction identifier lets the recovery process identify the transaction to which a log record belongs, so that during the various stages of recovery, it knows whether the record can be ignored or must be processed.
There are also a few "special" log records. Checkpoint records are, perhaps, the most familiar of those special records. Checkpointing is the process of making the on-disk state of the database consistent as of some point in time.
In other words, Berkeley DB aggressively caches database pages in Mpool for performance. However, those pages must eventually get written to disk and the sooner we do so, the more quickly we will be able to recover in the case of application or system failure. This implies a trade-off between the frequency of checkpointing and the length of recovery: the more frequently a system takes checkpoints, the more quickly it will be able to recover. Checkpointing is a transaction function, so we'll describe the details of checkpointing in the next section.
For the purposes of this section, we'll talk about checkpoint records and how the log manager struggles between being a stand-alone module and a special-purpose Berkeley DB component.
In general, the log manager, itself, has no notion of record types, so in theory, it should not distinguish between checkpoint records and other records—they are simply opaque byte strings that the log manager writes to disk. In practice, the log maintains metadata revealing that it does understand the contents of some records. For example, during log startup, the log manager examines all the log files it can find to identify the most recently written log file.
It assumes that all log files prior to that one are complete and intact, and then sets out to examine the most recent log file and determine how much of it contains valid log records.
In either case, it determines the logical end of log. During this process of reading the log to find the current end, the log manager extracts the Berkeley DB record type, looking for checkpoint records.
It retains the position of the last checkpoint record it finds in log manager metadata as a "favor" to the transaction system. That is, the transaction system needs to find the last checkpoint, but rather than having both the log manager and transaction manager read the entire log file to do so, the transaction manager delegates that task to the log manager.
This is a classic example of violating abstraction boundaries in exchange for performance. What are the implications of this tradeoff? Imagine that a system other than Berkeley DB is using the log manager.
If it happens to write the value corresponding to the checkpoint record type in the same position that Berkeley DB places its record type, then the log manager will identify that record as a checkpoint record. In short, this is either a harmful layering violation or a savvy performance optimization.
File management is another place where the separation between the log manager and Berkeley DB is fuzzy. As mentioned earlier, most Berkeley DB log records have to identify a database. Each log record could contain the full filename of the database, but that would be expensive in terms of log space, and clumsy, because recovery would have to map that name to some sort of handle it could use to access the database either a file descriptor or a database handle. Instead, Berkeley DB identifies databases in the log by an integer identifier, called a log file id, and implements a set of functions, called dbreg for "database registration" , to maintain mappings between filenames and log file ids.
However, we also need in-memory representations of this mapping to facilitate transaction abort and recovery. What subsystem should be responsible for maintaining this mapping? In theory, the file to log-file-id mapping is a high-level Berkeley DB function; it does not belong to any of the subsystems, which were intended to be ignorant of the larger picture. In the original design, this information was left in the logging subsystems data structures because the logging system seemed like the best choice.
However, after repeatedly finding and fixing bugs in the implementation, the mapping support was pulled out of the logging subsystem code and into its own small subsystem with its own object-oriented interfaces and private data structures.
In retrospect, this information should logically have been placed with the Berkeley DB environment information itself, outside of any subsystem. There is rarely such thing as an unimportant bug. Sure, there's a typo now and then, but usually a bug implies somebody didn't fully understand what they were doing and implemented the wrong thing.
When you fix a bug, don't look for the symptom: look for the underlying cause, the misunderstanding, if you will, because that leads to a better understanding of the program's architecture as well as revealing fundamental underlying flaws in the design itself. Our last module is the transaction manager, which ties together the individual components to provide the transactional ACID properties of atomicity, consistency, isolation, and durability. The transaction manager is responsible for beginning and completing either committing or aborting transactions, coordinating the log and buffer managers to take transaction checkpoints, and orchestrating recovery.
We'll visit each of these areas in order. Atomicity means that all the operations performed within a transaction appear in the database in a single unit—they either are all present in the database or all absent. Consistency means that a transaction moves the database from one logically consistent state to another.
For example, if the application specifies that all employees must be assigned to a department that is described in the database, then the consistency property enforces that with properly written transactions.
Isolation means that from the perspective of a transaction, it appears that the transaction is running sequentially without any concurrent transactions running. Finally, durability means that once a transaction is committed, it stays committed—no failure can cause a committed transaction to disappear. The transaction subsystem enforces the ACID properties, with the assistance of the other subsystems.
It uses traditional transaction begin, commit, and abort operations to delimit the beginning and ending points of a transaction. It also provides a prepare call, which facilitates two phase commit, a technique for providing transactional properties across distributed transactions, which are not discussed in this chapter.
Transaction commit writes a commit log record and then forces the log to disk unless the application indicates that it is willing to forego durability in exchange for faster commit processing , ensuring that even in the presence of failure, the transaction will be committed. Transaction abort reads backwards through the log records belonging to the designated transaction, undoing each operation that the transaction had done, returning the database to its pre-transaction state.
The transaction manager is also responsible for taking checkpoints. There are a number of different techniques in the literature for taking checkpoints [ HR83 ].
Berkeley DB uses a variant of fuzzy checkpointing. Fundamentally, checkpointing involves writing buffers from Mpool to disk.
This is a potentially expensive operation, and it's important that the system continues to process new transactions while doing so, to avoid long service disruptions.
At the beginning of a checkpoint, Berkeley DB examines the set of currently active transactions to find the lowest LSN written by any of them.
The transaction manager then asks Mpool to flush its dirty buffers to disk; writing those buffers might trigger log flush operations. After all the buffers are safely on disk, the transaction manager then writes a checkpoint record containing the checkpoint LSN. This record states that all the operations described by log records before the checkpoint LSN are now safely on disk. Therefore, log records prior to the checkpoint LSN are no longer necessary for recovery.
This has two implications: First, the system can reclaim any log files prior to the checkpoint LSN. Second, recovery need only process records after the checkpoint LSN, because the updates described by records prior to the checkpoint LSN are reflected in the on-disk state.
Note that there may be many log records between the checkpoint LSN and the actual checkpoint record. That's fine, since those records describe operations that logically happened after the checkpoint and that may need to be recovered if the system fails.
The last piece of the transactional puzzle is recovery. The goal of recovery is to move the on-disk database from a potentially inconsistent state to a consistent state. Berkeley DB uses a fairly conventional two-pass scheme that corresponds loosely to "relative to the last checkpoint LSN, undo any transactions that never committed and redo any transactions that did commit. Berkeley DB needs to reconstruct its mapping between log file ids and actual databases so that it can redo and undo operations on the databases.
During recovery, Berkeley DB uses these log records to reconstruct the file mapping. This record contains the checkpoint LSN. Berkeley DB needs to recover from that checkpoint LSN, but in order to do so, it needs to reconstruct the log file id mapping that existed at the checkpoint LSN; this information appears in the checkpoint prior to the checkpoint LSN.
Checkpoint records contain, not only the checkpoint LSN, but the LSN of the previous checkpoint to facilitate this process. Starting with the checkpoint selected by the previous algorithm, recovery reads sequentially until the end of the log to reconstruct the log file id mappings. When it reaches the end of the log, its mappings should correspond exactly to the mappings that existed when the system stopped.
Also during this pass, recovery keeps track of any transaction commit records encountered, recording their transaction identifiers. Any transaction for which log records appear, but whose transaction identifier does not appear in a transaction commit record, was either aborted or never completed and should be treated as aborted. When recovery reaches the end of the log, it reverses direction and begins reading backwards through the log.
For each transactional log record encountered, it extracts the transaction identifier and consults the list of transactions that have committed, to determine if this record should be undone. If it finds that the transaction identifier does not belong to a committed transaction, it extracts the record type and calls a recovery routine for that log record, directing it to undo the operation described. If the record belongs to a committed transaction, recovery ignores it on the backwards pass.
This backward pass continues all the way back to the checkpoint LSN 1. Finally, recovery reads the log one last time in the forward direction, this time redoing any log records belonging to committed transactions. When this final pass completes, recovery takes a checkpoint. At this point, the database is fully consistent and ready to begin running the application.
In theory, the final checkpoint is unnecessary. In practice, it bounds the time for future recoveries and leaves the database in a consistent state. Database recovery is a complex topic, difficult to write and harder to debug because recovery simply shouldn't happen all that often. In his Turing Award Lecture, Edsger Dijkstra argued that programming was inherently difficult and the beginning of wisdom is to admit we are unequal to the task.
Our goal as architects and programmers is to use the tools at our disposal: design, problem decomposition, review, testing, naming and style conventions, and other good habits, to constrain programming problems to problems we can solve.
Berkeley DB is now over twenty years old. The lessons we've learned over the course of its development and maintenance are encapsulated in the code and summarized in the design tips outlined above.
We offer them in the hope that other software designers and architects will find them useful. Design Lesson 1 It is vital for any complex software package's testing and maintenance that the software be designed and built as a cooperating set of modules with well-defined API boundaries. Design Lesson 2 A software design is simply one of several ways to force yourself to think through the entire problem before attempting to solve it. Application APIs 1. DBP handle operations 2. Into Lock 5.
Into Mpool 6. Into Log 7. Into Lock 9. Into Mpool Into Log Into Dbreg Into Lock From Log Design Lesson 3 Software architecture does not age gracefully. The Library Interface Layer Over time, as we added additional functionality, we discovered that both applications and internal code needed the same top-level functionality for example, a table join operation uses multiple cursors to iterate over the rows, just as an application might use a cursor to iterate over those same rows.
Design Lesson 4 It doesn't matter how you name your variables, methods, functions, or what comments or code style you use; that is, there are a large number of formats and styles that are "good enough.
Design Lesson 5 Software architects must choose their upgrade battles carefully: users will accept minor changes to upgrade to new releases if you guarantee compile-time errors, that is, obvious failures until the upgrade is complete; upgrade changes should never fail in subtle ways.
The Underlying Components There are four components underlying the access methods: a buffer manager, a lock manager, a log manager and a transaction manager. Design Lesson 6 In library design, respect for the namespace is vital. Design Lesson 7 Before we wrote a shared-memory linked-list package, Berkeley DB engineers hand-coded a variety of different data structures in shared memory, and these implementations were fragile and difficult to debug.
The Buffer Manager: Mpool The Berkeley DB Mpool subsystem is an in-memory buffer pool of file pages, which hides the fact that main memory is a limited resource, requiring the library to move database pages to and from disk when handling databases larger than memory. Design Lesson 8 Write-ahead logging is another example of providing encapsulation and layering, even when the functionality is never going to be useful to another piece of software: after all, how many programs care about LSNs in the cache?
The Lock Manager: Lock Like Mpool, the lock manager was designed as a general-purpose component: a hierarchical lock manager see [ GLPT76 ] , designed to support a hierarchy of objects that can be locked such as individual data items , the page on which a data item lives, the file in which a data item lives, or even a collection of files.
Lock Objects Lock objects are arbitrarily long opaque byte-strings that represent the objects being locked. Design Lesson 9 Berkeley DB's choice to use page-level locking was made for good reasons, but we've found that choice to be problematic at times. The Conflict Matrix The last abstraction of the locking subsystem we'll discuss is the conflict matrix. Supporting Hierarchical Locking Before explaining the different lock modes in the Berkeley DB conflict matrix, let's talk about how the locking subsystem supports hierarchical locking.
Design Lesson 10 Berkeley DB's general-purpose design was well rewarded when we added concurrent data store functionality. The Log Manager: Log The log manager provides the abstraction of a structured, append-only file. Design Lesson 11 When you find an architectural problem you don't want to fix "right now" and that you're inclined to just let go, remember that being nibbled to death by ducks will kill you just as surely as being trampled by elephants.
Log Record Formatting The log must persist per-record metadata so that, given an LSN, the log manager can determine the size of the record to return. Microsoft Office YTD Video Downloader. Adobe Photoshop CC. VirtualDJ Avast Free Security. WhatsApp Messenger. Talking Tom Cat. Clash of Clans. Subway Surfers. TubeMate 3. Google Play. Windows Windows. Most Popular.
New Releases. Desktop Enhancements. Networking Software. Software Coupons. Visit Site. Clicking on the Download Now Visit Site button above will open a connection to a third-party site. Developer's Description By Oracle. Oracle Berkeley DB is the industry-leading open source, embeddable storage engine that provides developers a fast, reliable, local database with zero administration. Oracle Berkeley DB is a library that links directly into your application.
❿
Comments
Post a Comment