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The new InfluxDB storage engine and some query language ideas Paul Dix CEO at InfluxDB @pauldix paul@influxdb.com

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preliminary intro materials…

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Everything is indexed by time and series

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Shards 10/11/2015 10/12/2015 Data organized into Shards of time, each is an underlying DB efficient to drop old data 10/13/2015 10/10/2015

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InfluxDB data temperature,device=dev1,building=b1 internal=80,external=18 1443782126

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InfluxDB data temperature,device=dev1,building=b1 internal=80,external=18 1443782126 Measurement

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InfluxDB data temperature,device=dev1,building=b1 internal=80,external=18 1443782126 Measurement Tags

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InfluxDB data temperature,device=dev1,building=b1 internal=80,external=18 1443782126 Measurement Tags Fields

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InfluxDB data temperature,device=dev1,building=b1 internal=80,external=18 1443782126 Measurement Tags Fields Timestamp

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InfluxDB data temperature,device=dev1,building=b1 internal=80,external=18 1443782126 Measurement Tags Fields Timestamp We actually store up to ns scale timestamps but I couldn’t fit on the slide

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Each series and field to a unique ID temperature,device=dev1,building=b1#internal temperature,device=dev1,building=b1#external 1 2

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Data per ID is tuples ordered by time temperature,device=dev1,building=b1#internal temperature,device=dev1,building=b1#external 1 2 1 (1443782126,80) 2 (1443782126,18)

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Storage Requirements

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High write throughput to hundreds of thousands of series

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Awesome read performance

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Better Compression

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Writes can’t block reads

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Reads can’t block writes

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Write multiple ranges simultaneously

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Hot backups

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Many databases open in a single process

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InfluxDB’s Time Structured Merge Tree (TSM Tree)

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InfluxDB’s Time Structured Merge Tree (TSM Tree) like LSM, but different

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Components WAL In memory cache Index Files

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Components WAL In memory cache Index Files Similar to LSM Trees

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Components WAL In memory cache Index Files Similar to LSM Trees Same

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Components WAL In memory cache Index Files Similar to LSM Trees Same like MemTables

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Components WAL In memory cache Index Files Similar to LSM Trees Same like MemTables like SSTables

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awesome time series data WAL (an append only file)

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awesome time series data WAL (an append only file) in memory index

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In Memory Cache // cache and flush variables cacheLock sync.RWMutex cache map[string]Values flushCache map[string]Values temperature,device=dev1,building=b1#internal

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In Memory Cache // cache and flush variables cacheLock sync.RWMutex cache map[string]Values flushCache map[string]Values writes can come in while WAL flushes

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// cache and flush variables cacheLock sync.RWMutex cache map[string]Values flushCache map[string]Values dirtySort map[string]bool values can come in out of order. mark if so, sort at query time

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Values in Memory type Value interface { Time() time.Time UnixNano() int64 Value() interface{} Size() int }

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awesome time series data WAL (an append only file) in memory index on disk index (periodic flushes)

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The Index Data File Min Time: 10000 Max Time: 29999 Data File Min Time: 30000 Max Time: 39999 Data File Min Time: 70000 Max Time: 99999 Contiguous blocks of time

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The Index Data File Min Time: 10000 Max Time: 29999 Data File Min Time: 15000 Max Time: 39999 Data File Min Time: 70000 Max Time: 99999 can overlap

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The Index cpu,host=A Min Time: 10000 Max Time: 20000 cpu,host=A Min Time: 21000 Max Time: 39999 Data File Min Time: 70000 Max Time: 99999 but a specific series must not overlap

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The Index Data File Data File Data File a file will never overlap with more than 2 others time ascending Data File Data File

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Data files are read only, like LSM SSTables

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The Index Data File Min Time: 10000 Max Time: 29999 Data File Min Time: 30000 Max Time: 39999 Data File Min Time: 70000 Max Time: 99999 Data File Min Time: 10000 Max Time: 99999 they periodically get compacted (like LSM)

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Compacting while appending new data

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Compacting while appending new data func (w *WriteLock) LockRange(min, max int64) { // sweet code here } func (w *WriteLock) UnlockRange(min, max int64) { // sweet code here }

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Compacting while appending new data func (w *WriteLock) LockRange(min, max int64) { // sweet code here } func (w *WriteLock) UnlockRange(min, max int64) { // sweet code here } This should block until we get it

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Locking happens inside each Shard

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Back to the data files… Data File Min Time: 10000 Max Time: 29999 Data File Min Time: 30000 Max Time: 39999 Data File Min Time: 70000 Max Time: 99999

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Data File Layout

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Data File Layout Similar to SSTables

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Data File Layout

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Data File Layout blocks have up to 1,000 points by default

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Data File Layout

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Data File Layout 4 byte position means data files can be at most 4GB

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Data Files type dataFile struct { f *os.File size uint32 mmap []byte }

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Memory mapping lets the OS handle caching for you

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Compressed Data Blocks

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Timestamps: encoding based on precision and deltas

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Timestamps (best case): Run length encoding Deltas are all the same for a block (only requires start time, delta, and count)

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Timestamps (good case): Simple8B Ann and Moffat in "Index compression using 64-bit words"

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Timestamps (worst case): raw values nano-second timestamps with large deltas

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float64: double delta Facebook’s Gorilla - google: gorilla time series facebook https://github.com/dgryski/go-tsz

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booleans are bits!

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int64 uses zig-zag same as from Protobufs (adding double delta and RLE)

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string uses Snappy same compression LevelDB uses (might add dictionary compression)

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How does it perform?

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Compression depends greatly on the shape of your data

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Write throughput depends on batching, CPU, and memory

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one test: 100,000 series 100,000 points per series 10,000,000,000 total points 5,000 points per request c3.8xlarge, writes from 4 other systems ~390,000 points/sec ~3 bytes/point (random floats, could be better)

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~400 IOPS 30%-50% CPU There’s room for improvement!

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Detailed writeup https://influxdb.com/docs/v0.9/concepts/storage_engine.html

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Query Language Ideas

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Three different kinds of functions

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Aggregates select mean(value) from cpu where host = 'A' and time > now() - 4h group by time(5m)

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Transformations select derivative(value) from cpu where host = 'A' and time > now() - 4h group by time(5m)

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Selectors select min(value) from cpu where host = 'A'; and time > now() - 4h group by time(5m)

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Then there are fills select mean(value) from cpu where host = 'A' and time > now() - 4h group by time(5m) fill(0)

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How to differentiate between the different types?

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How do we chain functions together? without making breaking changes to InfluxQL

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Mix jQuery style with InfluxQL SELECT mean(value).fill(previous).derivate(1s).scale(100).as(‘mvg_avg’) FROM measurement WHERE time > now() - 4h GROUP BY time(1m)

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D3 style SELECT mean(value) .fill(previous) .derivate(1s) .scale(100) .as(‘mvg_avg’) FROM measurement WHERE time > now() - 4h GROUP BY time(1m)

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Moving the FROM? SELECT from('cpu').mean(value) from('memory').mean(value) WHERE time > now() - 4h GROUP BY time(1m)

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Moving the FROM? SELECT from('cpu').mean(value) from('memory').mean(value) WHERE time > now() - 4h GROUP BY time(1m) consistent time and filtering applied to both

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JOIN SELECT join( from('errors') .count(value), from('requests') .count(value) ).fill(0) .count(value) WHERE time > now() - 4h GROUP BY time(1m)

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Thank you! Paul Dix @pauldix paul@influxdb.com