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 <h1>Leveldb</h1>
 <address>Jeff Dean, Sanjay Ghemawat</address>
 <p>
 The <code>leveldb</code> library provides a persistent key value store.  Keys and
 values are arbitrary byte arrays.  The keys are ordered within the key
 value store according to a user-specified comparator function.

 <p>
 <h1>Opening A Database</h1>
 <p>
 A <code>leveldb</code> database has a name which corresponds to a file system
 directory.  All of the contents of database are stored in this
 directory.  The following example shows how to open a database,
 creating it if necessary:
 <p>
 <pre>
   #include &lt;assert&gt;
   #include "leveldb/db.h"

   leveldb::DB* db;
   leveldb::Options options;
   options.create_if_missing = true;
   leveldb::Status status = leveldb::DB::Open(options, "/tmp/testdb", &amp;db);
   assert(status.ok());
   ...
 </pre>
 If you want to raise an error if the database already exists, add
 the following line before the <code>leveldb::DB::Open</code> call:
 <pre>
   options.error_if_exists = true;
 </pre>
 <h1>Status</h1>
 <p>
 You may have noticed the <code>leveldb::Status</code> type above.  Values of this
 type are returned by most functions in <code>leveldb</code> that may encounter an
 error.  You can check if such a result is ok, and also print an
 associated error message:
 <p>
 <pre>
    leveldb::Status s = ...;
    if (!s.ok()) cerr &lt;&lt; s.ToString() &lt;&lt; endl;
 </pre>
 <h1>Closing A Database</h1>
 <p>
 When you are done with a database, just delete the database object.
 Example:
 <p>
 <pre>
   ... open the db as described above ...
   ... do something with db ...
   delete db;
 </pre>
 <h1>Reads And Writes</h1>
 <p>
 The database provides <code>Put</code>, <code>Delete</code>, and <code>Get</code> methods to
 modify/query the database.  For example, the following code
 moves the value stored under key1 to key2.
 <pre>
   std::string value;
   leveldb::Status s = db-&gt;Get(leveldb::ReadOptions(), key1, &amp;value);
   if (s.ok()) s = db-&gt;Put(leveldb::WriteOptions(), key2, value);
   if (s.ok()) s = db-&gt;Delete(leveldb::WriteOptions(), key1);
 </pre>

 <h1>Atomic Updates</h1>
 <p>
 Note that if the process dies after the Put of key2 but before the
 delete of key1, the same value may be left stored under multiple keys.
 Such problems can be avoided by using the <code>WriteBatch</code> class to
 atomically apply a set of updates:
 <p>
 <pre>
   #include "leveldb/write_batch.h"
   ...
   std::string value;
   leveldb::Status s = db-&gt;Get(leveldb::ReadOptions(), key1, &amp;value);
   if (s.ok()) {
     leveldb::WriteBatch batch;
     batch.Delete(key1);
     batch.Put(key2, value);
     s = db-&gt;Write(leveldb::WriteOptions(), &amp;batch);
   }
 </pre>
 The <code>WriteBatch</code> holds a sequence of edits to be made to the database,
 and these edits within the batch are applied in order.  Note that we
 called <code>Delete</code> before <code>Put</code> so that if <code>key1</code> is identical to <code>key2</code>,
 we do not end up erroneously dropping the value entirely.
 <p>
 Apart from its atomicity benefits, <code>WriteBatch</code> may also be used to
 speed up bulk updates by placing lots of individual mutations into the
 same batch.

 <h1>Synchronous Writes</h1>
 By default, each write to <code>leveldb</code> is asynchronous: it
 returns after pushing the write from the process into the operating
 system.  The transfer from operating system memory to the underlying
 persistent storage happens asynchronously.  The <code>sync</code> flag
 can be turned on for a particular write to make the write operation
 not return until the data being written has been pushed all the way to
 persistent storage.  (On Posix systems, this is implemented by calling
 either <code>fsync(...)</code> or <code>fdatasync(...)</code> or
 <code>msync(..., MS_SYNC)</code> before the write operation returns.)
 <pre>
   leveldb::WriteOptions write_options;
   write_options.sync = true;
   db-&gt;Put(write_options, ...);
 </pre>
 Asynchronous writes are often more than a thousand times as fast as
 synchronous writes.  The downside of asynchronous writes is that a
 crash of the machine may cause the last few updates to be lost.  Note
 that a crash of just the writing process (i.e., not a reboot) will not
 cause any loss since even when <code>sync</code> is false, an update
 is pushed from the process memory into the operating system before it
 is considered done.

 <p>
 Asynchronous writes can often be used safely.  For example, when
 loading a large amount of data into the database you can handle lost
 updates by restarting the bulk load after a crash.  A hybrid scheme is
 also possible where every Nth write is synchronous, and in the event
 of a crash, the bulk load is restarted just after the last synchronous
 write finished by the previous run.  (The synchronous write can update
 a marker that describes where to restart on a crash.)

 <p>
 <code>WriteBatch</code> provides an alternative to asynchronous writes.
 Multiple updates may be placed in the same <code>WriteBatch</code> and
 applied together using a synchronous write (i.e.,
 <code>write_options.sync</code> is set to true).  The extra cost of
 the synchronous write will be amortized across all of the writes in
 the batch.

 <p>
 <h1>Concurrency</h1>
 <p>
 A database may only be opened by one process at a time.
 The <code>leveldb</code> implementation acquires a lock from the
 operating system to prevent misuse.  Within a single process, the
 same <code>leveldb::DB</code> object may be safely shared by multiple
 concurrent threads.  I.e., different threads may write into or fetch
 iterators or call <code>Get</code> on the same database without any
 external synchronization (the leveldb implementation will
 automatically do the required synchronization).  However other objects
 (like Iterator and WriteBatch) may require external synchronization.
 If two threads share such an object, they must protect access to it
 using their own locking protocol.  More details are available in
 the public header files.
 <p>
 <h1>Iteration</h1>
 <p>
 The following example demonstrates how to print all key,value pairs
 in a database.
 <p>
 <pre>
   leveldb::Iterator* it = db-&gt;NewIterator(leveldb::ReadOptions());
   for (it-&gt;SeekToFirst(); it-&gt;Valid(); it-&gt;Next()) {
     cout &lt;&lt; it-&gt;key().ToString() &lt;&lt; ": "  &lt;&lt; it-&gt;value().ToString() &lt;&lt; endl;
   }
   assert(it-&gt;status().ok());  // Check for any errors found during the scan
   delete it;
 </pre>
 The following variation shows how to process just the keys in the
 range <code>[start,limit)</code>:
 <p>
 <pre>
   for (it-&gt;Seek(start);
        it-&gt;Valid() &amp;&amp; it-&gt;key().ToString() &lt; limit;
        it-&gt;Next()) {
     ...
   }
 </pre>
 You can also process entries in reverse order.  (Caveat: reverse
 iteration may be somewhat slower than forward iteration.)
 <p>
 <pre>
   for (it-&gt;SeekToLast(); it-&gt;Valid(); it-&gt;Prev()) {
     ...
   }
 </pre>
 <h1>Snapshots</h1>
 <p>
 Snapshots provide consistent read-only views over the entire state of
 the key-value store.  <code>ReadOptions::snapshot</code> may be non-NULL to indicate
 that a read should operate on a particular version of the DB state.
 If <code>ReadOptions::snapshot</code> is NULL, the read will operate on an
 implicit snapshot of the current state.
 <p>
 Snapshots are created by the DB::GetSnapshot() method:
 <p>
 <pre>
   leveldb::ReadOptions options;
   options.snapshot = db-&gt;GetSnapshot();
   ... apply some updates to db ...
   leveldb::Iterator* iter = db-&gt;NewIterator(options);
   ... read using iter to view the state when the snapshot was created ...
   delete iter;
   db-&gt;ReleaseSnapshot(options.snapshot);
 </pre>
 Note that when a snapshot is no longer needed, it should be released
 using the DB::ReleaseSnapshot interface.  This allows the
 implementation to get rid of state that was being maintained just to
 support reading as of that snapshot.
 <h1>Slice</h1>
 <p>
 The return value of the <code>it->key()</code> and <code>it->value()</code> calls above
 are instances of the <code>leveldb::Slice</code> type.  <code>Slice</code> is a simple
 structure that contains a length and a pointer to an external byte
 array.  Returning a <code>Slice</code> is a cheaper alternative to returning a
 <code>std::string</code> since we do not need to copy potentially large keys and
 values.  In addition, <code>leveldb</code> methods do not return null-terminated
 C-style strings since <code>leveldb</code> keys and values are allowed to
 contain '\0' bytes.
 <p>
 C++ strings and null-terminated C-style strings can be easily converted
 to a Slice:
 <p>
 <pre>
    leveldb::Slice s1 = "hello";

    std::string str("world");
    leveldb::Slice s2 = str;
 </pre>
 A Slice can be easily converted back to a C++ string:
 <pre>
    std::string str = s1.ToString();
    assert(str == std::string("hello"));
 </pre>
 Be careful when using Slices since it is up to the caller to ensure that
 the external byte array into which the Slice points remains live while
 the Slice is in use.  For example, the following is buggy:
 <p>
 <pre>
    leveldb::Slice slice;
    if (...) {
      std::string str = ...;
      slice = str;
    }
    Use(slice);
 </pre>
 When the <code>if</code> statement goes out of scope, <code>str</code> will be destroyed and the
 backing storage for <code>slice</code> will disappear.
 <p>
 <h1>Comparators</h1>
 <p>
 The preceding examples used the default ordering function for key,
 which orders bytes lexicographically.  You can however supply a custom
 comparator when opening a database.  For example, suppose each
 database key consists of two numbers and we should sort by the first
 number, breaking ties by the second number.  First, define a proper
 subclass of <code>leveldb::Comparator</code> that expresses these rules:
 <p>
 <pre>
   class TwoPartComparator : public leveldb::Comparator {
    public:
     // Three-way comparison function:
     //   if a &lt; b: negative result
     //   if a &gt; b: positive result
     //   else: zero result
     int Compare(const leveldb::Slice&amp; a, const leveldb::Slice&amp; b) const {
       int a1, a2, b1, b2;
       ParseKey(a, &amp;a1, &amp;a2);
       ParseKey(b, &amp;b1, &amp;b2);
       if (a1 &lt; b1) return -1;
       if (a1 &gt; b1) return +1;
       if (a2 &lt; b2) return -1;
       if (a2 &gt; b2) return +1;
       return 0;
     }

     // Ignore the following methods for now:
     const char* Name() const { return "TwoPartComparator"; }
     void FindShortestSeparator(std::string*, const leveldb::Slice&amp;) const { }
     void FindShortSuccessor(std::string*) const { }
   };
 </pre>
 Now create a database using this custom comparator:
 <p>
 <pre>
   TwoPartComparator cmp;
   leveldb::DB* db;
   leveldb::Options options;
   options.create_if_missing = true;
   options.comparator = &amp;cmp;
   leveldb::Status status = leveldb::DB::Open(options, "/tmp/testdb", &amp;db);
   ...
 </pre>
 <h2>Backwards compatibility</h2>
 <p>
 The result of the comparator's <code>Name</code> method is attached to the
 database when it is created, and is checked on every subsequent
 database open.  If the name changes, the <code>leveldb::DB::Open</code> call will
 fail.  Therefore, change the name if and only if the new key format
 and comparison function are incompatible with existing databases, and
 it is ok to discard the contents of all existing databases.
 <p>
 You can however still gradually evolve your key format over time with
 a little bit of pre-planning.  For example, you could store a version
 number at the end of each key (one byte should suffice for most uses).
 When you wish to switch to a new key format (e.g., adding an optional
 third part to the keys processed by <code>TwoPartComparator</code>),
 (a) keep the same comparator name (b) increment the version number
 for new keys (c) change the comparator function so it uses the
 version numbers found in the keys to decide how to interpret them.
 <p>
 <h1>Performance</h1>
 <p>
 Performance can be tuned by changing the default values of the
 types defined in <code>include/leveldb/options.h</code>.

 <p>
 <h2>Block size</h2>
 <p>
 <code>leveldb</code> groups adjacent keys together into the same block and such a
 block is the unit of transfer to and from persistent storage.  The
 default block size is approximately 4096 uncompressed bytes.
 Applications that mostly do bulk scans over the contents of the
 database may wish to increase this size.  Applications that do a lot
 of point reads of small values may wish to switch to a smaller block
 size if performance measurements indicate an improvement.  There isn't
 much benefit in using blocks smaller than one kilobyte, or larger than
 a few megabytes.  Also note that compression will be more effective
 with larger block sizes.
 <p>
 <h2>Compression</h2>
 <p>
 Each block is individually compressed before being written to
 persistent storage.  Compression is on by default since the default
 compression method is very fast, and is automatically disabled for
 uncompressible data.  In rare cases, applications may want to disable
 compression entirely, but should only do so if benchmarks show a
 performance improvement:
 <p>
 <pre>
   leveldb::Options options;
   options.compression = leveldb::kNoCompression;
   ... leveldb::DB::Open(options, name, ...) ....
 </pre>
 <h2>Cache</h2>
 <p>
 The contents of the database are stored in a set of files in the
 filesystem and each file stores a sequence of compressed blocks.  If
 <code>options.cache</code> is non-NULL, it is used to cache frequently used
 uncompressed block contents.
 <p>
 <pre>
   #include "leveldb/cache.h"

   leveldb::Options options;
   options.cache = leveldb::NewLRUCache(100 * 1048576);  // 100MB cache
   leveldb::DB* db;
   leveldb::DB::Open(options, name, &db);
   ... use the db ...
   delete db
   delete options.cache;
 </pre>
 Note that the cache holds uncompressed data, and therefore it should
 be sized according to application level data sizes, without any
 reduction from compression.  (Caching of compressed blocks is left to
 the operating system buffer cache, or any custom <code>Env</code>
 implementation provided by the client.)
 <p>
 When performing a bulk read, the application may wish to disable
 caching so that the data processed by the bulk read does not end up
 displacing most of the cached contents.  A per-iterator option can be
 used to achieve this:
 <p>
 <pre>
   leveldb::ReadOptions options;
   options.fill_cache = false;
   leveldb::Iterator* it = db-&gt;NewIterator(options);
   for (it-&gt;SeekToFirst(); it-&gt;Valid(); it-&gt;Next()) {
     ...
   }
 </pre>
 <h2>Key Layout</h2>
 <p>
 Note that the unit of disk transfer and caching is a block.  Adjacent
 keys (according to the database sort order) will usually be placed in
 the same block.  Therefore the application can improve its performance
 by placing keys that are accessed together near each other and placing
 infrequently used keys in a separate region of the key space.
 <p>
 For example, suppose we are implementing a simple file system on top
 of <code>leveldb</code>.  The types of entries we might wish to store are:
 <p>
 <pre>
    filename -&gt; permission-bits, length, list of file_block_ids
    file_block_id -&gt; data
 </pre>
 We might want to prefix <code>filename</code> keys with one letter (say '/') and the
 <code>file_block_id</code> keys with a different letter (say '0') so that scans
 over just the metadata do not force us to fetch and cache bulky file
 contents.
 <p>
 <h2>Filters</h2>
 <p>
 Because of the way <code>leveldb</code> data is organized on disk,
 a single <code>Get()</code> call may involve multiple reads from disk.
 The optional <code>FilterPolicy</code> mechanism can be used to reduce
 the number of disk reads substantially.
 <pre>
    leveldb::Options options;
    options.filter_policy = NewBloomFilterPolicy(10);
    leveldb::DB* db;
    leveldb::DB::Open(options, "/tmp/testdb", &amp;db);
    ... use the database ...
    delete db;
    delete options.filter_policy;
 </pre>
 The preceding code associates a
 <a href="http://en.wikipedia.org/wiki/Bloom_filter">Bloom filter</a>
 based filtering policy with the database.  Bloom filter based
 filtering relies on keeping some number of bits of data in memory per
 key (in this case 10 bits per key since that is the argument we passed
 to NewBloomFilterPolicy).  This filter will reduce the number of unnecessary
 disk reads needed for <code>Get()</code> calls by a factor of
 approximately a 100.  Increasing the bits per key will lead to a
 larger reduction at the cost of more memory usage.  We recommend that
 applications whose working set does not fit in memory and that do a
 lot of random reads set a filter policy.
 <p>
 If you are using a custom comparator, you should ensure that the filter
 policy you are using is compatible with your comparator.  For example,
 consider a comparator that ignores trailing spaces when comparing keys.
 <code>NewBloomFilterPolicy</code> must not be used with such a comparator.
 Instead, the application should provide a custom filter policy that
 also ignores trailing spaces.  For example:
 <pre>
   class CustomFilterPolicy : public leveldb::FilterPolicy {
    private:
     FilterPolicy* builtin_policy_;
    public:
     CustomFilterPolicy() : builtin_policy_(NewBloomFilterPolicy(10)) { }
     ~CustomFilterPolicy() { delete builtin_policy_; }

     const char* Name() const { return "IgnoreTrailingSpacesFilter"; }

     void CreateFilter(const Slice* keys, int n, std::string* dst) const {
       // Use builtin bloom filter code after removing trailing spaces
       std::vector&lt;Slice&gt; trimmed(n);
       for (int i = 0; i &lt; n; i++) {
         trimmed[i] = RemoveTrailingSpaces(keys[i]);
       }
       return builtin_policy_-&gt;CreateFilter(&amp;trimmed[i], n, dst);
     }

     bool KeyMayMatch(const Slice& key, const Slice& filter) const {
       // Use builtin bloom filter code after removing trailing spaces
       return builtin_policy_-&gt;KeyMayMatch(RemoveTrailingSpaces(key), filter);
     }
   };
 </pre>
 <p>
 Advanced applications may provide a filter policy that does not use
 a bloom filter but uses some other mechanism for summarizing a set
 of keys.  See <code>leveldb/filter_policy.h</code> for detail.
 <p>
 <h1>Checksums</h1>
 <p>
 <code>leveldb</code> associates checksums with all data it stores in the file system.
 There are two separate controls provided over how aggressively these
 checksums are verified:
 <p>
 <ul>
 <li> <code>ReadOptions::verify_checksums</code> may be set to true to force
   checksum verification of all data that is read from the file system on
   behalf of a particular read.  By default, no such verification is
   done.
 <p>
 <li> <code>Options::paranoid_checks</code> may be set to true before opening a
   database to make the database implementation raise an error as soon as
   it detects an internal corruption.  Depending on which portion of the
   database has been corrupted, the error may be raised when the database
   is opened, or later by another database operation.  By default,
   paranoid checking is off so that the database can be used even if
   parts of its persistent storage have been corrupted.
 <p>
   If a database is corrupted (perhaps it cannot be opened when
   paranoid checking is turned on), the <code>leveldb::RepairDB</code> function
   may be used to recover as much of the data as possible
 <p>
 </ul>
 <h1>Approximate Sizes</h1>
 <p>
 The <code>GetApproximateSizes</code> method can used to get the approximate
 number of bytes of file system space used by one or more key ranges.
 <p>
 <pre>
    leveldb::Range ranges[2];
    ranges[0] = leveldb::Range("a", "c");
    ranges[1] = leveldb::Range("x", "z");
    uint64_t sizes[2];
    leveldb::Status s = db-&gt;GetApproximateSizes(ranges, 2, sizes);
 </pre>
 The preceding call will set <code>sizes[0]</code> to the approximate number of
 bytes of file system space used by the key range <code>[a..c)</code> and
 <code>sizes[1]</code> to the approximate number of bytes used by the key range
 <code>[x..z)</code>.
 <p>
 <h1>Environment</h1>
 <p>
 All file operations (and other operating system calls) issued by the
 <code>leveldb</code> implementation are routed through a <code>leveldb::Env</code> object.
 Sophisticated clients may wish to provide their own <code>Env</code>
 implementation to get better control.  For example, an application may
 introduce artificial delays in the file IO paths to limit the impact
 of <code>leveldb</code> on other activities in the system.
 <p>
 <pre>
   class SlowEnv : public leveldb::Env {
     .. implementation of the Env interface ...
   };

   SlowEnv env;
   leveldb::Options options;
   options.env = &amp;env;
   Status s = leveldb::DB::Open(options, ...);
 </pre>
 <h1>Porting</h1>
 <p>
 <code>leveldb</code> may be ported to a new platform by providing platform
 specific implementations of the types/methods/functions exported by
 <code>leveldb/port/port.h</code>.  See <code>leveldb/port/port_example.h</code> for more
 details.
 <p>
 In addition, the new platform may need a new default <code>leveldb::Env</code>
 implementation.  See <code>leveldb/util/env_posix.h</code> for an example.

 <h1>Other Information</h1>

 <p>
 Details about the <code>leveldb</code> implementation may be found in
 the following documents:
 <ul>
 <li> <a href="impl.html">Implementation notes</a>
 <li> <a href="table_format.txt">Format of an immutable Table file</a>
 <li> <a href="log_format.txt">Format of a log file</a>
 </ul>

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