Fast & simple key-value storage.
There are a lot of great in-memory key-value storages like Redis/Memcache. But all of them are limited by RAM. Imagine you want to store 100,000,000,000 of key-value pairs and have quite fast random-access to items by key. Filedict was designed exactly for this case.
$ git clone https://bitbucket.org/dkuryakin/filedict.git $ cd filedict && python setup.py install
$ pip install filedict
I believe that the best documentation - is a comprehensive and in-place commented set of examples. So here they are:
import ctypes import filedict class Storage(filedict.BaseStorage): # This is simple in-file storage based on hash table. max_records = 1000 # You can not save more than 1000 values in this database! This limit # can not be changed in the future. It is necessary to initially # correctly estimate the maximum number of entries in the database. # In the worst case it is necessary to copy this database to the new # larger database element by element. key_types = ctypes.c_uint64, val_types = ctypes.c_uint32, ctypes.c_uint32 # Specify key & value binary representation. Each record in database # file has size = 1 + sizeof(key) + sizeof(val). In current case it's # 1 + 8 + (4 + 4) = 17 bytes. So resulting database file has size # 1000 * 17 = 17000 bytes. You can check this fact yourself. Note: # key-value types are fixed, and can't be changed in future! # Use simple try-finally form: db = Storage('/path/to/db', read_only=False, read_count=100) # There are 3 parameters when create instance of Storage: path, read_only # and read_count. Path - path to database dir. Only one writer per database # is allowed, so use read_only=True if you don't need any writes. Read # count specify read block size for all operations which involve a lot of # sequential reads. Default value for read_count is quite good for most of # cases. db.record_size == 1 + 8 + (4 + 4) # True. try: db.open() # do something with db finally: db.close() # Or use context-manager form: with Storage('/path/to/db', read_only=False, read_count=100) as db: # You can add key-value pair to database. Return None. Duplicate keys # allowed. Duplicate key-value pairs allowed too. key1 = 0, key2 = 1, val1 = 0, 1 val2 = 1, 2 val3 = 2, 3 db.add(key1, val1) db.add(key1, val2) db.add(key2, val3) # Note: this type of storage is rapidly becoming ineffective in the # case of a large number of records with the same key. So it is # recommended to take care of a high degree of uniqueness at the # application level. Besides the performance starts to drop # dramatically when free space ends in the database. It is recommended # to set value of max_records 10-15% more than the actual maximum # size of the database. Also writes slow down on SATA disks for huge # databases. If you want to store huge amount of data and you don't # have SSD disk - try to use BaseShardedStorage instead (see below). # You can iterate over different values for target key. it = db.values(key1) # Return generator. set(it) == set([val1, val2]) # Sets are equal, but iteration order is # not defined! # You can iterate over all key-value pairs in database. Note: order is # not defined! it = db.items() # Returns generator. set(it) == set([(key1, val1), (key1, val2), (key2, val3)]) # You can get estimation for database size. Note: this estimation is # precise for writer. It can be outdated for readers in case of active # writer. But if there is no active writer, estimation will be precise # for readers too. len(db) == 3 # That's True. # It's possible to delete all mentions of key-value pair from database. 1 == db.del_value(key1, val1) # Return number of deleted records. len(db) == 2 # True. db.deleted_count == 1 # True. # We also can delete all records with target key. 1 == db.del_key(key1) # Return number of deleted records. len(db) == 1 # True. db.deleted_count == 2 # True. # After set of add-del operations database can come to not optimized # state. We can fix it in-place: db.defragmentation() db.deleted_count == 0 # True. All "voids" are optimized. # Note: this is VERY heavy operation. Use it only at worst case. # len(db) & db.deleted_count - are kind of estimations. If server is # hung or shut down suddenly - these estimations may deviate from the # actual values. In that case we can fix it by following way: db.fix_statistis() # Note: this is really SLOW operation. Use it as seldom as possible. # You can create defragmented copy of database: db.copy('/path/to/db-copy', read_count=100) # Read count - read_count parameter passed to constructor of created # database. Note: this is VERY heavy operation! # And finally, you can copy database content to another storage: class ExtendedStorage(Storage): max_records = 2000 with ExtendedStorage('/path/to/extended-db-copy') as edb: db.copy_to_storage(edb) len(db) == len(edb) # True. set(db.items()) == set(edb.items()) # True. # Congratulations! Now you know everything about filedict.BaseStorage. But # there is one more component: filedict.BaseShardedStorage: class Storage(filedict.BaseShardedStorage): # It has some familiar parameters: max_records = 1000 key_types = ctypes.c_uint64, val_types = ctypes.c_uint32, ctypes.c_uint32 # And some new parameters: shard_name_width = 5 # Length of shards names. In case of 3, shard names will be: 00000, # 00001, 00002, .. etc. Default value is good for most of cases. max_shard_fulness = 0.9 # Maximum allowed fulness of each shard subdatabase. Default value is # good for most of cases. # It's worth noting that sharded storage has no limitation for maximum # number of records in database. Value of max_records - is just a # limitation for single shard. And there is no limits for shards count. # But this feature leads to changes in performance balance. First, # ALL reads are slowed SHARDS_COUNT times (both SATA & SSD). Second, # writes on SATA are not slowed if use max_records = # (RAM_SIZE - RAM_SIZE_USED_BY_OS) / (1 + sizeof(key) + sizeof(val)). # For example, we have SATA and 16Gb of RAM. And 4Gb are permanently # used by OS and some applications. In this case, recommended # value for max_records is: # (16 - 4)*1024*1024*1024 / (1 + 8 + (4 + 4)) ~ 750,000,000 # So, set max_records to 750000000 and obtain fast writes! # If you have SSD - just use BaseStorage! # Now let's consider possible exceptions. try: # create database object, open it and perform some operations. except filedict.WrongFileSizeError: # Will be raised if change max_records for existing database. except filedict.UnableToSeekError: # Will be raised if try to seek to position that is greater than file # size. except filedict.UnableToReadError: # Will be raised if can not read from database file. except filedict.UnableToWriteError: # Will be raised if can not write to database file. except filedict.UnableToWriteRawError: # Will be raised if can not write raw data to database file. except filedict.RequiredAttrNotExistsError: # Will be raised if some of required params are not specified (for # example max_records). except filedict.WriteInReadOnlyModeError: # Will be raised if try to perform write operation for read-only opened # database. except filedict.StorageIsFullError: # Will be raised if try to add item in full database. except filedict.CopyAlreadyExistsError: # Will be raised if try to copy database to path that already exists. except filedict.NotOpenedError: # Will be raised if try to perform some operation on database that was # not opened. # Note: # StorageFileError - is base class for WrongFileSizeError, # UnableToSeekError, UnableToReadError, UnableToWriteError, # UnableToWriteRawError. # BaseStorageError - base class for RequiredAttrNotExistsError, # WriteInReadOnlyModeError, StorageIsFullError, CopyAlreadyExistsError, # NotOpenedError
- Simple in-file hash table.
- Can store billions of records providing really fast access.
- Use disk space effectively.
- Use a little bit RAM.
- Support both add & del operations.
- Support defragmentation, copy operation.
- No limits to readers number.
- Support multiple values for single key.
- Get value for given key only in 1 seek + 1 read (in best case, if keys are quite unique).
- Supports local sharding.
- Only python2, only linux for now.
- Max number of records is constant for any database. So it can be choosen only once.
- Supports only fixed data schema.
- Can store only integers & floats.
- Very slow in case of huge amount of duplicate keys.
- Only one writer allowed.
- No transactions, no ACID support.
- If your data can be placed in RAM, use Redis/Memcache instead!
- Not distributed.
Very simple, just run:
$ git clone https://bitbucket.org/dkuryakin/filedict.git $ cd filedict && python setup.py test
$ python -mfiledict.test
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