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Data Structures and Algorithms for Big Databases

数据结构

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I/O model and cache-oblivious analysis Write-optimized data structures How write-optimized data structures can help file systems Block-replacement algorithms Indexing strategies Log-structured merge trees Bloom filters

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Data Structures and Algorithms for

Big Databases

Michael A. Bender

Stony Brook & Tokutek

Bradley C. Kuszmaul

MIT & Tokutek

Big data problem

oy vey

???

data indexing

query processor

queries + answers

???

365

data ingestion

Important and universal problem.

Hot topic.

Big data problem

oy vey

???

data indexing

query processor

queries + answers

???

365

data ingestion

For on-disk data, one sees funny tradeoffs in the speeds

of data ingestion, query speed, and freshness of data.

Important and universal problem.

Hot topic.

Don’t Thrash: How to Cache Your Hash in Flash

data indexing

query processor

queries +

answers

???

data

ingestion

Funny tradeoff in ingestion, querying, freshness

• Typical record of all kinds of metadata is < 150 bytes.

• Different parts of metadata are accessed separately.

• “I'm trying to create indexes on a table with 308 million rows. It took ~20

minutes to load the table but 10 days to build indexes on it.”

‣

MySQL bug #9544

• “Select queries were slow until I added an index onto the timestamp ﬁeld...

Adding the index really helped our reporting, BUT now the inserts are taking

forever.”

‣

Comment on mysqlperformanceblog.com

• “They indexed their tables, and indexed them well,

And lo, did the queries run quick!

But that wasn’t the last of their troubles, to tell–

Their insertions, like molasses, ran thick.”

‣

Not from Alice in Wonderland by Lewis Carroll

Don’t Thrash: How to Cache Your Hash in Flash

data indexing

query processor

queries +

answers

???

data

ingestion

Funny tradeoff in ingestion, querying, freshness

• Typical record of all kinds of metadata is < 150 bytes.

• Different parts of metadata are accessed separately.

• “I'm trying to create indexes on a table with 308 million rows. It took ~20

minutes to load the table but 10 days to build indexes on it.”

‣

MySQL bug #9544

• “Select queries were slow until I added an index onto the timestamp ﬁeld...

Adding the index really helped our reporting, BUT now the inserts are taking

forever.”

‣

Comment on mysqlperformanceblog.com

• “They indexed their tables, and indexed them well,

And lo, did the queries run quick!

But that wasn’t the last of their troubles, to tell–

Their insertions, like molasses, ran thick.”

‣

Not from Alice in Wonderland by Lewis Carroll

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