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The TileDB Embedded Storage Engine
- 1. TileDB webinars The TileDB Embedded Storage Engine Founder & CEO of TileDB, Inc. Dr. Stavros Papadopoulos
- 2. Who is this webinar for? Those wanting to learn about data storage fundamentals Layout, compression, IO, etc. Those looking to efficiently store/access any kind of data to/from anywhere Dataframes, genomics, LiDAR, SAR, weather, and more, with a single engine Those tired of managing custom, inefficient data formats Formats not supporting fast updates, indexing, versioning, cloud performance
- 3. Disclaimer I am the exclusive recipient of complaints Email me at: stavros@tiledb.com All the credit for our amazing work goes to our powerful team Check it out at https://tiledb.com/about
- 4. Deep roots at the intersection of HPC, databases and data science Traction with telecoms, pharmas, hospitals and other scientific organizations 40 members with expertise across all applications and domains Who we are TileDB got spun out from MIT and Intel Labs in 2017 WHERE IT ALL STARTED Raised over $20M, we are very well capitalized INVESTORS
- 5. What is TileDB Embedded? An embeddable C library that stores and accesses multi-dimensional arrays Dense array Sparse array It implements very fast array slicing across dimensions
- 6. Superior performance Built in C Fully-parallelized Columnar format Multiple compressors R-trees for sparse arrays TileDB Embedded at a Glance https://github.com/TileDBInc/TileDB Open source: Rapid updates & data versioning Immutable writes Lock-free Parallel reader / writer model Time traveling Schema evolution
- 7. TileDB Embedded at a Glance https://github.com/TileDBInc/TileDB Open source: Extreme interoperability Numerous APIs Numerous integrations All backends Optimized for the cloud Immutable writes Parallel IO Minimization of requests
- 8. TileDB Embedded at a Glance APIs & tool Integrations with zero-copy where possible TileDB Embedded Open-source interoperable storage with a universal open-spec array format ● Parallel IO, rapid reads & writes ● Columnar, cloud-optimized ● Data versioning & time traveling
- 9. Why arrays? The basics Advanced internal mechanics Examples Work in progress Agenda Comparison to other formats and engines Docs at docs.tiledb.com
- 10. Byte 0 1 ... Regardless of what kind of data you have, it is laid out in a 1D storage medium Why Arrays? where each task may slice Algorithm as a task graph Regardless of what kind of algorithm you run, the algorithm involves a set of slices
- 11. Why Arrays? Byte 0 1 ... Byte 0 1 ... Performance is absolutely dictated by the slice result locality on the 1D medium
- 12. Why Arrays? Arrays provide a flexible way to map/slice any-dimensional (ND data to/from a 1D layout Giving different “importance” to different dimensions (order and tiling) Choosing whether dimension coordinates should be materialized or not (dense vs. sparse) Considering compression, encryption and other filters (tiling) Abstracting all the engineering magic that it takes to make everything very fast (engine) Unifying the data model for all application domains! (universality) Building indices for fast search (e.g., R-trees)
- 13. Arrays Subsume Dataframes Sparse array Dataframe Dense vector
- 14. Arrays Are Universal What else can be modeled as an array LiDAR 3D sparse) SAR 2D or 3D dense) Population genomics (3D sparse) Single-cell genomics (2D dense or sparse) Biomedical imaging (2D or 3D dense) Even flat files!!! 1D dense) Time series (ND dense or sparse) Weather (2D or 3D dense) Graphs (2D sparse) Video (3D dense) Key-values (1D or ND sparse)
- 15. The Basics
- 16. dense_array1 ├── __t2_t2_uuid1_v │ ├── __fragment_metadata.tdb │ └── a0.tdb ├── __t2_t2_uuid1.ok ├── __lock.tdb ├── __meta └── __schema └── __t1_t1_uuid2 A Simple 2D Dense Array 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 fragment schema attribute data
- 17. A Simple 2D Sparse Array sparse_array1 ├── __t2_t2_uuid2_v │ ├── __fragment_metadata.tdb │ ├── a0.tdb │ ├── d0.tdb │ └── d1.tdb ├── __t2_t2_uuid2_v.ok ├── __lock.tdb ├── __meta └── __schema └── __t1_t2_uuid1 1 2 3 4 5 6 fragment schema attribute data coordinates
- 18. Groups dense_group ├── __tiledb_group.tdb └── nested_group ├── __tiledb_group.tdb └── dense_array1 ├── __lock.tdb ├── __meta └── __schema Groups provide an easy way to hierarchically organize arrays
- 19. Array Metadata dense_array1 ├── __t2_t2_uuid2_v │ ├── __fragment_metadata.tdb │ └── a0.tdb ├── __t2_t2_uuid2_v.ok ├── __lock.tdb ├── __meta │ └── __t3_t3_uuid3 └── __schema └── __t1_t2_uuid1 You can attach any number of (key, value) pairs to an array The key must be string, and the value can be anything metadata go here
- 20. Multiple Attributes dense_array1 ├── __t2_t2_uuid1_v │ ├── __fragment_metadata.tdb │ └── a0.tdb │ └── a1.tdb ├── __t2_t2_uuid1.ok ├── __lock.tdb ├── __meta └── __schema └── __t1_t1_uuid2 1,a 2,b 3,c 4,d 5,e 6,f 7,g 8,h 9,i 10,j 11,k 12,l 13,m 14,n 15,o 16,p You can store more than one values in each cell, even of different type TileDB has a “columnar” format that allows you to efficiently subselect on attributes attribute data
- 21. Var-length Attributes dense_array3 ├── __t2_t2_uuid1_v │ ├── __fragment_metadata.tdb │ └── a0.tdb │ └── a0_var.tdb ├── __t2_t2_uuid1.ok ├── __lock.tdb ├── __meta └── __schema └── __t1_t1_uuid2 TileDB supports storing variable-length values in a cell (of any data type) a bb ccc dddd e ff ggg hhhh i jj kkk lll m nn ooo pppp offsets var-length data
- 22. Var-length Dimensions sparse_array4 ├── __t2_t2_uuid1_v │ ├── __fragment_metadata.tdb │ └── a0.tdb │ └── d0.tdb │ └── d0_var.tdb ├── __t2_t2_uuid1.ok ├── __lock.tdb ├── __meta └── __schema └── __t1_t1_uuid2 You can also have var-length dimensions and slice naturally using string ranges Applicable only to sparse arrays offsets var-length data a bb ccc dddd e ff 1 2 3 4 5 6 unbounded domain infinite gaps
- 23. Heterogeneous Dimensions 4 1.0 0.0 “dddd” 0.4 infinite string dimension infinite float32 dimension Sparse array allow you to have dimensions of different types The following 2D array allows efficient slicing on a string and a float32 dimension
- 24. Arrays as Dataframes An array is essentially a dataframe where dimensions are special (they are “indexed”)
- 25. What About Cloud Object Stores? array_name → {s3,azure,gcs,tiledb}://path/array_name Everything demonstrated works as is on the cloud
- 26. Tiling & Layout
- 27. Tiling | Dense Arrays fetches the whole array from storage 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 space tile extents fetches only a portion of the array (a tile) A space tile is the atomic unit of IO space tile extents
- 28. Cell Layout | Dense Arrays Three parameters define the values layout on storage, called the global order Space tile extents Tile order/layout (row-major or column-major) Cell order/layout (row-major or column major) row-major tile order row-major cell order 22 space tiles col-major tile order row-major cell order 22 space tiles row-major tile order col-major cell order 42 space tiles
- 29. Tiling & Cell Layout | Sparse Arrays Sparse arrays store only non-empty cells Grouping non-empty cells with space tiles would be inefficient (due to potential skew) The atomic unit of IO in sparse arrays is the data tile, of fixed (user-defined) capacity First impose a global order similar to dense arrays, then group based on capacity col-major tile order row-major cell order 22 space tiles capacity 2 space tile extents space tile extents col-major tile order row-major cell order 22 space tiles capacity 4 data tile
- 30. Hilbert Order | Sparse Arrays Space tiles greatly affect the cell layout in sparse arrays Sometimes it is very difficult to define a good space tiling (especially with floats and strings) For such cases, the Hilbert order is ideal (no tile extents and order) For floats we discretize the domain into buckets based on the number of dimensions For strings we assign a number of bits per dimension and then use the string prefixes as numbers
- 31. Tile Filters TileDB allows a wide range of filters to be applied to each tile prior to its storage Compressors (gzip, zstd, bzip2, …) Checksums Encryption The atomic unit of filtering is the chunk (typically equal to the L1 cache size) TileDB applies the filters across chunks in parallel in a pipeline chunk tile zstd AES256
- 33. Versioning and Time Traveling In TileDB, every write is immutable Each (batch) write creates a timestamped fragment With fragments, TileDB implements versioning and time traveling
- 34. Versioning and Time Traveling | Dense Arrays 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Read at 0,t1 100 3 4 7 8 9 10 11 12 13 14 15 16 Read at 0,t2 200 500 600 100 - - - - - - - - - - - - Read at (t1,t2 200 500 600 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Write at t1 100 200 500 600 Write at t2
- 35. 100 Write at t2 40 Versioning and Time Traveling | Sparse Arrays 1 2 3 4 5 6 Write at t1 100 2 3 40 5 6 Read at 0,t2 1 100 Read at (t1,t2 40 When no dups allowed 1 4 6 Read at 0,t1 2 3 5
- 36. When dups are allowed 4 dups 100 Write at t2 40 Versioning and Time Traveling | Sparse Arrays 1 2 3 4 5 6 Write at t1 100 Read at (t1,t2 40 1 4 6 Read at 0,t1 2 3 5 100 2 3 40 5 6 Read at 0,t2 1
- 37. Indexing TileDB has a three-level indexing approach Fragment timestamps (in the fragment names) for time traveling Non-empty domain in each fragment’s metadata Either simple offset arithmetic (dense) or R-trees (sparse) 1. Get list of fragment names (with .ok) t1_t1_uuid1_v t2_t2_uuid2_v ... 2. Ignore fragments with timestamp not in time traveling interval 3. Ignore fragments with non-empty domain not overlapping slice __fragment_metadata.tdb __fragment_metadata.tdb 4a. Ignore dense tiles via implicit positional indexing, or 4b. Ignore sparse tiles from the R-tree that do not overlap the slice Algorithm
- 38. A slicing query would just traverse the tree top-down, visiting only nodes/MBRs that intersect the slice Indexing Given the non-empty domain, the space tile extents and the tile order, we can find easily that this slice overlaps the second and fourth tile 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 row-major tile order 22 space tiles MBR1 MBR2 MBR3 MBR4 col-major tile order row-major cell order 22 space tiles capacity 2 R-tree (stored in fragment metadata) MBR1 MBR2 MBR3 MBR4
- 39. Consolidation & Vacuuming Numerous fragments can lead to performance degradation (loss of locality, expensive listing) TileDB supports two levels of consolidation Fragment metadata (group the non-empty domains in a single place) Fragments (better preserve data locality) Old fragments are preserved after consolidation (for time traveling) TileDB can vacuum old fragments to save space and boost listing Time traveling will not work on vacuumed fragments
- 40. Attribute Filter Push-Down TileDB supports pushing attribute filter conditions down to the engine That typically boosts performance Much fewer data gets copied around More L1-cache conscious More opportunities for parallelism and vectorization
- 41. Schema Evolution TileDB supports schema evolution (since v2.4 Adding an attribute Dropping an attribute More schema evolution features are coming up Full versioning and time traveling is supported
- 42. Notes on Writing Lots of flexibility in writing in different orders, different domain subarray, etc. Support for lock-free, parallel writing Tips for performance: Each tile should be 100KB 1MB Each fragment should be 1 2GB Fragments should not “interleave” Run fragment metadata consolidation (especially on cloud object stores) No support for deletions and updates yet (coming up soon)
- 43. Notes on Reading TileDB is eventually consistent Support for parallel writers, parallel readers (all lock-free) Support for reads in different layouts Support for “streaming reads” (incomplete queries) Tips for performance: Allocate sufficient space for the result buffers (minimize incomplete queries) Tune written layout based on the read layout (application dependent) Push down coordinate and attribute filter conditions
- 44. Work In Progress
- 45. Coming Up More schema evolution features Support for deletes and updates Git-like versioning ACID via modularizing locking More tile filters (e.g., sum, min, max) RLE and dictionary compression on strings Computations on compressed data Linear Algebra operations More SQL push down (e.g., group by) Graph algorithms
- 47. High-level Comparisons vs. HDF5 TileDB is cloud-native TileDB has support for sparse arrays vs. Zarr TileDB is built in C and is more interoperable TileDB has support for sparse arrays TileDB has support for versioning and time traveling TileDB has support for versioning and time traveling
- 48. High-level Comparisons vs. Parquet TileDB is multi-dimensional and supports more flexible layouts TileDB has support for dense arrays vs. Delta Lake TileDB does not rely on Spark, Presto or other subsystem TileDB has support for dense arrays TileDB has support for versioning and time traveling TileDB does not support deletes, updates and full ACID (yet) TileDB is natively multi-dimensional and supports more flexible layouts
- 49. The Universal Database Thank you WE ARE HIRING Apply at tiledb.workable.com