A Closer Look at Apache Kudu
•
7 likes•2,083 views
Presentation conducted at Morning@Lohika: http://morning.lohika.com/past-events/fast-data
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to A Closer Look at Apache Kudu
Similar to A Closer Look at Apache Kudu (20)
Recently uploaded
Recently uploaded (20)
A Closer Look at Apache Kudu
- 1. Apache Kudu A Closer Look at By Andriy Zabavskyy Mar 2017
- 2. A species of antelope from BigData Zoo
- 4. Analytics on Hadoop before Kudu Fast Scans Fast Random Access
- 5. Weak side of combining Parquet and HBase • Complex code to manage the flow and synchronization of data between the two systems. • Manage consistent backups, security policies, and monitoring across multiple distinct systems.
- 6. Lambda Architecture Challenges • In the real world, systems often need to accommodate • Late-arriving data • Corrections on past records • Privacy-related deletions on data that has already been migrated to the immutable store.
- 7. Happy Medium • High Throughput. Goal within 2x Impala • Low Latency for random read/write. Goal 1ms on SSD • SQL and NoSQL style API Fast Scans Fast Random Access
- 9. Tables, Schemas, Keys • Kudu is a storage system for tables of structured data • Schema consisting of a finite number of columns • Each such column has a name, type: • Boolean, Integers, Unixtime_Micros, • Floating, String, Binary
- 10. Keys • Some ordered subset of those columns are specified to be the table’s primary key • The primary key: • enforces a uniqueness constraint • acts as the sole index by which rows may be efficiently updated or deleted
- 11. Write Operations • User mutates the table using Insert, Update, and Delete APIs • Note: a primary key must be fully specified • Java, C++, Python API • No multi-row transactional APIs: • each mutation conceptually executes as its own transaction, • despite being automatically batched with other mutations for better performance.
- 12. Read Operations • Scan operation: • any number of predicates to filter the results • two types of predicates: • comparisons between a column and a constant value, • and composite primary key ranges. • An user may specify a projection for a scan. • A projection consists of a subset of columns to be retrieved.
- 13. Read/Write Python API Sample
- 15. Storage Layout Goals • Fast columnar scans • best-of-breed immutable data formats such as Parquet • efficiently encoded columnar data files. • Low-latency random updates • O(lg n) lookup complexity for random access • Consistency of performance • Majority of users are willing predictability
- 16. MemRowSet • In-memory concurrent B-tree • No removal from tree – MVCC records instead • No in-place updates – only modifications without changing the value size • Link together leaf nodes for sequential scans • Row-wise layout - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- 17. DiskRowSet • Column-organized • Each column is written to disk in a single contiguous block of data. • The column itself is subdivided into small pages • Granular random reads, and • An embedded B-tree index
- 18. Deltas • A DeltaMemStore is a concurrent B-tree which shares the implementation of MemRowSets • A DeltaMemStore flushes into a DeltaFile • A DeltaFile is a simple binary column
- 19. Insert Path • Each DiskRowSet stores a Bloom filter of the set of keys present • Each DiskRowSet, we store the minimum and maximum primary key,
- 20. Read Path • Converts the key range predicate into a row offset range predicate • Performs the scan one column at a time • Seeks the target column to the correct row offset • Consult the delta stores to see if any later updates
- 21. Delta Compaction • Background maintenance manager periodically • scans DiskRowSets to find any cases where a large number of deltas have accumulated, and • schedules a delta compaction operation which merges those deltas back into the base data columns.
- 22. RowSet Compaction • A key-based merge of two or more DiskRowSets • The output is written back to new DiskRowSets rolling every 32 MB • RowSet compaction has two goals: • We take this opportunity to remove deleted rows. • This process reduces the number of DiskRowSets that overlap in key range
- 23. Kudu Trade-Offs • Random Updates will be slower • Kudu requires key-lookup before update, bloom lookup before insert • Single Row Seek may be slower • Columnar Design is optimized for scans • Especially slow at reading a row with many recent updates
- 25. Cluster Roles
- 26. The Kudu Master Kudu’s central master process has several key responsibilities: • A catalog manager • keeping track of which tables and tablets exist, as well as their schemas, desired replication levels, and other metadata • A cluster coordinator • keeping track of which servers in the cluster are aliveand coordinating redistribution of data • A tablet directory • keeping track of which tablet servers are hosting replicas of each tablet
- 28. Partitioning • Tables in Kudu are horizontally partitioned. • Kudu, like BigTable, calls these partitions tablets • Kudu supports a flexible array of partitioning schemes
- 29. Partitioning: Hash Img source: https://github.com/cloudera/kudu/blob/master/docs/images/hash-partitioning-example.png
- 30. Partitioning: Range Img source: https://github.com/cloudera/kudu/blob/master/docs/images/range-partitioning-example.png
- 31. Partitioning: Hash plus Range Img source: https://github.com/cloudera/kudu/blob/master/docs/images/hash-range-partitioning-example.png
- 32. Partitioning Recommendations • Bigger tables, like fact tables are recommended to partition in a way so that 1 tablet would contain about 1GB of data • Do not partition small tables like dimensions • Note: Impala doesn’t allow skipping the partitioning clause, so you need to specify the 1 range partition explicitly:
- 33. Dimension Table with One Partition
- 35. Replication Approach • Kudu uses the Leader/Follower or Master-Slave replication • Kudu employs the Raft[25] consensus algorithm to replicate its tablets • If a majority of replicas accept the write and log it to their own local write-ahead logs, • the write is considered durably replicated and thus can be committed on all replicas
- 36. Raft: Replicated State Machine • Replicated log ensures state machines execute same commandsinsame order • Consensus module ensures proper log replication • System makes progress as long as any majority of servers are up • Visualization: https://raft.github.io/raftscope/index.html
- 37. Consistency Model • Kudu provides clients the choice between two consistency modes for reads(scans): • READ_AT_SNAPSHOT • READ_LATEST
- 38. READ_LATEST consistency • Monotonic reads are guaranteed(?) Read-your-writes is not • Corresponds to "Read Committed" ACID Isolation mode: • This is the default mode.
- 39. READ_LATEST consistency • The server will always return committed writes at the time the request was received. • This type of read is not repeatable.
- 40. READ_AT_SNAPSHOT Consistency • Guarantees read-your-writes consistency from a single client • Corresponds "Repeatable Read” ACID Isolation mode.
- 41. READ_AT_SNAPSHOT Consistency • The server attempts to perform a read at the provided timestamp • In this mode reads are repeatable • at the expense of waiting for in-flight transactions whose timestamp is lower than the snapshot's timestamp to complete
- 42. Write Consistency • Writes to a single tablet are always internally consistent • By default, Kudu does not provide an external consistency guarantee. • However, for users who require a stronger guarantee, Kudu offers the option to manually propagate timestamps between clients
- 43. Replication Factor Limitation • Since Kudu 1.2.0: • The replication factor of tables is now limited to a maximum of 7 • In addition, it is no longer allowed to create a table with an even replication factor
- 44. Kudu and CAP Theorem • Kudu is a CP type of storage engine. • Writing to a tablet will be delayed if the server that hosts that tablet’s leader replica fails • Kudu gains the following properties by using Raft consensus: • Leader elections are fast • Follower replicas don’t allow writes, but they do allow reads
- 46. Applications for which Kudu is a viable • Reporting applications where new data must be immediately available for end users • Time-series applications with • queries across large amounts of historic data • granular queries about an individual entity • Applications that use predictive models to make real-time decisions
- 47. Why Kudu Streaming Analytics Case Study
- 48. Business Case • A leader in health care compliance consulting and technology-driven managed services • Cloud-based multi-services platform • It offers • enhanced data security and scalability, • operational managed services, and access to business information http://ihealthone.com /wp-c ontent/uploads/2016/12/ Healthcare_Complianc e_Cons ultants-495x400.jpg
- 49. ETL Approach Key Points: • Leverage Confluent platform with Schema Registry • Apply configuration based approach: • Avro Schema in Schema Registry for Input Schema • Impala Kudu SQL scripts for Target Schema • Stick to Python App as primary ETL code, but extend: • Develop new abstractions to work with mapping rules • Streaming processing for both facts and dimensions Cons: • Scaling needs extra effortsData Flow Analytics DWH Event Topics ETL Code Configuration Input Schema Mapping Rules Target Schema Other Configurations
- 50. Stream ETL using Pipeline Architecture Cache Manager Mapper/ Flattener Types Adjuster Data Enricher DB Sinker Data Reader Configuration Pipeline Modules: • Data Reader: reads data from source DB • Mapper/Flattener: flatten JSON treelike structure into flat one and maps the field names to target ones • Types Adjuster: adjusts/converts data types properly • Data Enricher: enriches the data structure with new data: • Generates surrogate key • Looks up for the data from target DB(using cache) • DB Sinker: writes data into target DB Other Modules: • Cache Manager: manages the cache with dimension data
- 52. Kudu Numeric vs String Keys • Reason: • Generating surrogate numeric keys adds extra processing step and complexityto the overall ETL process • Sample Schema: • Dimension: • Promotion dimension with 1000 unique members, 30 categories • Products dimension with 50 000 unique members, 300 categories • Facts • Fact table containing the references to the 2 dimension above with 1 million of rows • Fact table containing the references to the 2 dimension above with 100 million of rows
- 53. Benchmark Result
- 55. Pain Points • Often releases with many changes • Data types Limitations (especially in Python Lib, Impala) • Lack of Sequences/Constraints • Lack of Multi-Row transactions
- 56. Limitations • Not recommended more than 50 columns • Immutable primary keys • Non-alterable Primary Key, Partitioning, Column Types • Partitions splitable
- 57. Modeling Recommendations: Star Schema Dimensions : • Replication factor equal to number of nodes in a cluster • 1 Tablet per dimension Facts: • Aim for as many tablets as you have cores in the cluster
- 58. Why Kudu What Kudu is Not
- 59. What Kudu is Not • Not a SQL interface itself • It’s just the storage layer – you should use Impala or SparkSQL • Not an application that runs on HDFS • It’s an alternative, native Hadoop storage engine • Not a replacement for HDFS or Hbase • Select the right storage for the right use case • Cloudera will support and invest in all three
- 60. Why Kudu Kudu vs MPP Data Warehouse
- 61. Kudu vs MPP Data Warehouses In Common: • Fast analytics queries via SQL • Ability to insert, update, delete data Differences: üFaster streaming inserts üImproved Hadoop integration o Slower batch inserts o No transactional data loading, multi-row transactions, indexing
- 62. Useful resources • Community, Downloads, VM: • https://kudu.apache.org • Whitepaper: • http://kudu.apache.org/kudu.pdf • Slack channel: • https://getkudu-slack.herokuapp.com
- 63. USA HQ Toll Free: 866-687-3588 Tel: +1-512-516-8880 Ukraine HQ Tel: +380-32-240-9090 Bulgaria Tel: +359-2-902-3760 Germany Tel: +49-69-2602-5857 Netherlands Tel: +31-20-262-33-23 Poland Tel: +48-71-382-2800 UK Tel: +44-207-544-8414 EMAIL info@softserveinc.com WEBSITE: www.softserveinc.com Questions ?
Editor's Notes
- Structured storage in the Hadoop ecosystem has typically been achieved in two ways: for static data sets, data is typically stored on HDFS using binary data formats such as Apache Avro[1] or Apache Parquet[3]. However, neither HDFS nor these formats has any provision for updating indi- vidual records, or for efficient random access. Mutable data sets are typically stored in semi-structured stores such as Apache HBase[2] or Apache Cassandra[21]. These systems allow for low-latency record-level reads and writes, but lag far behind the static file formats in terms of sequential read throughput for applications such as SQL-based analytics or machine learning.
- Following design of BigTable Kudu relies on: A single Master server, responsible for metadata Can be replicated for fault tolerance An arbitrary number of Tablet Servers, responsible for data
- When READ_LATEST is specified the server will always return committed writes at the time the request was received. This type of read does not return a snapshot timestamp and is not repeatable. In ACID terms this corresponds to Isolation mode: "Read Committed" This is the default mode. Monotonic reads [19] is a guarantee that this kind of anomaly does not happen. It’s a lesser guarantee than strong consistency, but a stronger guarantee than eventual con‐ sistency. When you read data, you may see an old value; monotonic reads only means that if one user makes several reads in sequence, they will not see time go backwards, i.e. they will not read older data after having previously read newer data. In this situation, we need read-after-write consistency, also known as read-your-writes consistency [20]. This is a guarantee that if the user reloads the page, they will always see any updates they submitted themselves. It makes no promises about other users: other users’ updates may not be visible until some later time. However, it reassures the user that their own input has been saved correctly.
- When READ_LATEST is specified the server will always return committed writes at the time the request was received. This type of read does not return a snapshot timestamp and is not repeatable. In ACID terms this corresponds to Isolation mode: "Read Committed" This is the default mode.
- By default, Kudu does not provide an external consistency guarantee. That is to say, if a client performs a write, then communicates with a di↵erent client via an external mecha- nism (e.g. a message bus) and the other performs a write, the causal dependence between the two writes is not captured. A third reader may see a snapshot which contains the second write without the first However, for users who require a stronger guarantee, Kudu o↵ers the option to man- ually propagate timestamps between clients: after performing a write, the user may ask the client library for a timestamp to- ken. This token may be propagated to another client through the external channel, and passed to the Kudu API on the other side, thus preserving the causal relationship between writes made across the two clients. In this situation, we need read-after-write consistency, also known as read-your-writes consistency [20]. This is a guarantee that if the user reloads the page, they will always see any updates they submitted themselves. It makes no promises about other users: other users’ updates may not be visible until some later time. However, it reassures the user that their own input has been saved correctly.
- By default, Kudu does not provide an external consistency guarantee. That is to say, if a client performs a write, then communicates with a di↵erent client via an external mecha- nism (e.g. a message bus) and the other performs a write, the causal dependence between the two writes is not captured. A third reader may see a snapshot which contains the second write without the first However, for users who require a stronger guarantee, Kudu o↵ers the option to man- ually propagate timestamps between clients: after performing a write, the user may ask the client library for a timestamp to- ken. This token may be propagated to another client through the external channel, and passed to the Kudu API on the other side, thus preserving the causal relationship between writes made across the two clients.
- By default, Kudu does not provide an external consistency guarantee. That is to say, if a client performs a write, then communicates with a di↵erent client via an external mecha- nism (e.g. a message bus) and the other performs a write, the causal dependence between the two writes is not captured. A third reader may see a snapshot which contains the second write without the first However, for users who require a stronger guarantee, Kudu o↵ers the option to man- ually propagate timestamps between clients: after performing a write, the user may ask the client library for a timestamp to- ken. This token may be propagated to another client through the external channel, and passed to the Kudu API on the other side, thus preserving the causal relationship between writes made across the two clients.