众所周知，SQLite ，而不是 SQLite 的断言就是受不了.很明显，它可以处理大型数据集如果索引该数据集不是您的用例的一部分.我一直在使用 SQLite 作为日志系统的后端，一段时间以来它不需要被索引，所以我对我经历的放缓感到非常惊讶.
如果有人发现自己想要使用 SQLite 存储大量数据并对其进行索引，使用分片 可能是答案.我最终决定使用 MD5 的前三个字符散列 z 中的唯一列来确定分配给 4,096 个数据库之一.由于我的用例本质上主要是归档，因此架构不会改变，查询也永远不需要分片遍历.数据库大小有限制，因为极旧的数据将被减少并最终被丢弃，所以这种分片、pragma 设置甚至一些de规范化的组合给了我一个很好的平衡，基于上面的基准测试，保持至少 10k 插入/秒的插入速度.
如果您的要求是查找特定的 z_id 和 x_ids 和 y_ids 链接到它(与快速选择一系列 z_ids 不同)你可以查看一个非索引的哈希表嵌套关系数据库，它可以让你立即找到特定的方法z_id 以获得它的 y_ids 和 x_ids —— 没有索引开销和随着索引增长而在插入过程中随之而来的性能下降.为了避免结块(也就是桶冲突)，请选择一种密钥散列算法，该算法将最大权重放在 z_id 的数字上，并具有最大的变化(右加权).
附言例如，使用 b-tree 的数据库最初可能比使用线性散列的 db 更快，但随着 b-tree 上的性能开始下降，插入性能将与线性散列保持一致.
P.P.S.回答@kawing-chiu 的问题:这里相关的核心特征是这样的数据库依赖于所谓的稀疏"表，其中记录的物理位置由散列算法确定，散列算法将记录键作为输入.这种方法允许直接查找表中记录的位置而无需索引的中介.由于不需要遍历索引或重新平衡索引，插入时间保持不变，因为表变得更加密集.相比之下，使用 b 树，插入时间会随着索引树的增长而降低.具有大量并发插入的 OLTP 应用程序可以从这种稀疏表方法中受益.记录分散在整个表格中.分散在稀疏表的苔原"中的记录的缺点是收集具有共同值(例如邮政编码)的大量记录可能会更慢.散列稀疏表方法经过优化，可以插入和检索单个记录，并检索相关记录的网络，而不是具有某些共同字段值的大型记录集.
嵌套关系数据库允许在一行的一列中包含元组.
It is well-known that SQLite needs to be fine tuned to achieve insert speeds on the order of 50k inserts/s. There are many questions here regarding slow insert speeds and a wealth of advice and benchmarks.
There are also claims that SQLite can handle large amounts of data, with reports of 50+ GB not causing any problems with the right settings.
I have followed the advice here and elsewhere to achieve these speeds and I'm happy with 35k-45k inserts/s. The problem I have is that all of the benchmarks only demonstrate fast insert speeds with < 1m records. What I am seeing is that insert speed seems to be inversely proportional to table size.
My use case requires storing 500m to 1b tuples ([x_id, y_id, z_id]) over a few years (1m rows / day) in a link table. The values are all integer IDs between 1 and 2,000,000. There is a single index on z_id.
Performance is great for the first 10m rows, ~35k inserts/s, but by the time the table has ~20m rows, performance starts to suffer. I'm now seeing about 100 inserts/s.
The size of the table is not particularly large. With 20m rows, the size on disk is around 500MB.
The project is written in Perl.
Is this the reality of large tables in SQLite or are there any secrets to maintaining high insert rates for tables with > 10m rows?
SQLITE_FCNTL_CHUNK_SIZE: I don't know C (?!), so I'd prefer to not learn it just to get this done. I can't see any way to set this parameter using Perl though.Following Tim's suggestion that an index was causing increasingly slow insert times despite SQLite's claims that it is capable of handling large data sets, I performed a benchmark comparison with the following settings:
cache_size pragma: 10,000page_size pragma: 4,096temp_store pragma: memoryjournal_mode pragma: deletesynchronous pragma: offIn my project, as in the benchmark results below, a file-based temporary table is created and SQLite's built-in support
for importing CSV data is used. The temporary table is then attached
to the receiving database and sets of 50,000 rows are inserted with an
insert-select statement. Therefore, the insert times do not reflect
file to database insert times, but rather table to table insert
speed. Taking the CSV import time into account would reduce the speeds
by 25-50% (a very rough estimate, it doesn't take long to import the
CSV data).
Clearly having an index causes the slowdown in insert speed as table size increases.
It's quite clear from the data above that the correct answer can be assigned to Tim's answer rather than the assertions that SQLite just can't handle it. Clearly it can handle large datasets if indexing that dataset is not part of your use case. I have been using SQLite for just that, as a backend for a logging system, for a while now which does not need to be indexed, so I was quite surprised at the slowdown I experienced.
If anyone finds themselves wanting to store a large amount of data using SQLite and have it indexed, using shards may be the answer. I eventually settled on using the first three characters of an MD5 hash a unique column in z to determine assignment to one of 4,096 databases. Since my use case is primarily archival in nature, the schema will not change and queries will never require shard walking. There is a limit to database size since extremely old data will be reduced and eventually discarded, so this combination of sharding, pragma settings, and even some denormalisation gives me a nice balance that will, based on the benchmarking above, maintain an insert speed of at least 10k inserts / second.
If your requirement is to find a particular z_id and the x_ids and y_ids linked to it (as distinct from quickly selecting a range of z_ids) you could look into a non-indexed hash-table nested-relational db that would allow you to instantly find your way to a particular z_id in order to get its y_ids and x_ids -- without the indexing overhead and the concomitant degraded performance during inserts as the index grows. In order to avoid clumping (aka bucket collisions), choose a key hashing algorithm that puts greatest weight on the digits of z_id with greatest variation (right-weighted).
P.S. A database that uses a b-tree may at first appear faster than a db that uses linear hashing, say, but the insert performance will remain level with the linear hash as performance on the b-tree begins to degrade.
P.P.S. To answer @kawing-chiu's question: the core feature relevant here is that such a database relies on so-called "sparse" tables in which the physical location of a record is determined by a hashing algorithm which takes the record key as input. This approach permits a seek directly to the record's location in the table without the intermediary of an index. As there is no need to traverse indexes or to re-balance indexes, insert-times remain constant as the table becomes more densely populated. With a b-tree, by contrast, insert times degrade as the index tree grows. OLTP applications with large numbers of concurrent inserts can benefit from such a sparse-table approach. The records are scattered throughout the table. The downside of records being scattered across the "tundra" of the sparse table is that gathering large sets of records which have a value in common, such as a postal code, can be slower. The hashed sparse-table approach is optimized to insert and retrieve individual records, and to retrieve networks of related records, not large sets of records that have some field value in common.
A nested relational database is one that permits tuples within a column of a row.
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