Whereas a data warehouse will need rigid data modeling and definitions, a data lake can store different types and shapes of data. It mainly concerns two issues: schema evolution and instance evolution. When an entity object of an old schema is loaded into memory it is automatically converted into an instance of the up to date … In particular, they may require substantial changes to your data model. Schema Evolution - Changing a Schema. need to evolve it over time. Building a big-data platform is no different and managing schema evolution is still a challenge that needs solving. Traditionally the archival data has been (i) either migrated under the current schema version, to ease querying, but compromising archival quality, or (ii Schema Evolution. Flattening the data can be done by appending the names of the columns to each other, resulting in a record resembling the following: This brings us back to the concept of “schema-on-read”. The tools should ultimately serve the use case and not limit it. One of the main challenges in these systems is to deal with the volatile and dynamic nature of Web sources. The best practices for evolving a database schema are well known, where a migration gets applied before the code that needs to use it is rolled out. In our initial experiments with these technologies, much of our data was kept in its raw format, which is JSON for event based data, but for many sources could also be CSV. Applications tend to evolve, and together with them, their internal data definitions need to … It can corrupt our data and can cause problems. It is important for data engineers to consider their use cases carefully before choosing a technology. Therefore, when attempting to query this file, us… Finally, we also discuss the relationship between this simple versioning mechanism and general-purpose version-management systems. The theory is general enough to cater for more modelling concepts, or different modelling approaches. If you see the schema of the dataframe, we have salary data type as integer. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. Data schema design as a schema evolution process. We are currently using Darwin in multiple Big Data projects in production at Terabyte scale to solve Avro data evolution problems. Schema Evolution is the ability of a database system to respond to changes in the real world by allowing the schema to evolve. This talk is about sharing our learnings and some best practices we have built over the years working with massive volume and every changing schema of data… For example, consider an extended version of the previous JSON record: An additional field, nested2, which is an array-type field has been added. When a format change happens, it’s critical that the new message format does not break the consumers. Another problem typically encountered is related to nested JSON data. 59, No. Darwin is a schema repository and utility library that simplifies the whole process of Avro encoding/decoding with schema evolution. In computer science, schema versioning and schema evolution, deal with the need to retain current data and software system functionality in the face of changing database structure. Other nested complex data types can still pose problems. Athena is a schema-on-read query engine. [6,46,54]) are only able to describe the evolution of either the conceptual level, or the Supporting graceful schema evolution represents an unsolved problem for traditional information systems that is further exacerbated in web information systems, such as Wikipedia and public scientific databases: in these projects based on multiparty cooperation the frequency of database schema changes has increased while tolerance for downtimes has nearly disappeared. Database evolution & migration Curino et al. For example, consider the following JSON record: When Athena reads this data, it will recognize that we have two top-level fields, message and data, and that both of these are struct types (similar to dictionaries in Python). No support is required for previous schemata. Support for schema evolution in merge operations – You can now automatically evolve the schema of the table with the merge operation. Different technologies can offer different pros and cons that may help with these issues: Avro is a comparable format to Parquet and can also handle some schema evolution. • We provide and plant the seeds of the first public, real-life-based, benchmark for schema evolution, which will offer to researchers and practitioners a rich data-set to evaluate their Complexity of schema evolution An object-oriented database schema (hereafter called a schema) is … So you have some data that you want to … Database Schema Evolution and Meta-Modeling 9th International Workshop on Foundations of Models and Languages for Data and Objects FoMLaDO/DEMM 2000 Dagstuhl Castle, Germany, September 18-21, 2000 Selected Papers. In an information system a key role is played by the underlying data schema. Similar to the examples above, an empty array will be inferred as an array of strings. Table Evolution Iceberg supports in-place table evolution.You can evolve a table schema just like SQL – even in nested structures – or change partition layout when data volume changes. This means that when you create a table in Athena, it applies schemas when reading the data. It does In this work we address the effects of adding/removing/changing Web sources and data items to the Data Warehouse (DW) schema. In other words, upon writing data into a data warehouse, a schema for that data needs to be defined. Free Preview with evolution operators. We present a universe of data schemas that allows us to describe the underlying data schemas at all stages of their development. In an event-driven microservice architecture, microservices generate JSON type events that will be stored in the data lake, inside of an S3 bucket. Want to work with us? Even when the information system design is finalised, the data schema can evolve further due to changes in the requirements on the system. Essentially, Athena will be unable to infer a schema since it will see the same table with two different partitions, and the same field with different types across those partitions. After the initial schema is defined, applications may need to evolve over time. Similarly, Avro is well suited to connection-oriented protocols, where participants can exchange schema data at the start of a session and exchange serialized records from that point on. Managing schema changes has always proved troublesome for architects and software engineers. While upstream complexity may have been eliminated for a data pipeline, that complexity has merely been pushed downstream to the user who will be attempting to query this data. Even though both of these columns have the same type, there are still differences which are not supported for more complex data types. Now consider the following record received in a different partition: The addition of a key/value pair inside of nested1 will also cause a HIVE_PARTITION_SCHEMA_MISMATCH error because Athena will have no way of knowing that the content of the nested1 struct has changed. Sometimes your data will start arriving with new fields or even worse with different… Motivation: Schema evolution is common due to data integration, government regulation,etc. However, if the exact format and schema of messages is known ahead of time, this can be factored into the appropriate data pipeline. proaches to relational schema evolution and schema versioning is presented in [Roddick, 1995]. Figure 2. No support is required for previous schemata. The schema evolution management uses an object-oriented data model that supports temporal features and versions definition - the Temporal Versions Model - TVM. Consider a comma-separated record with a nullable field called reference_no. Currently, schema evolution is supported only for POJO and Avro types. those for integration of database schemas adapted for typical web data conflicts [10]. In Spark, Parquet data source can detect and merge schema of those files automatically. Doing so allows a better understanding of the actual design process, countering the problem of ‘software development under the lamppost’. In our case, this data catalog is managed by Glue, which uses a set of predefined crawlers to read through samples of the data stored on S3 to infer a schema for the data. This allows us to describe the transformation process of a database design as an evolution of a schema through a universe of data schemas. This video provides an introduction to some of the complex solutions that you can build easily in ADF with data flow's schema drift feature. Azure Data Factory treats schema drift flows as late-binding flows, so when you build your transformations, the drifted column names won't be available to you in the schema views throughout the flow. Schema evolution deals with the need to retain current data when database schema changes are performed. Published by Martin Kleppmann on 05 Dec 2012. There are countless articles to be found online debating the pros and cons of data lakes and comparing them to data warehouses. In Spark, Parquet data source can detect and merge schema … 2 Schema.org: evolution of structured data on the web research-article Schema.org: evolution of structured data on the web If you see below, the new column is just added and for those previous records where there was no data for the location column, it is set to null. KijiSchema integrates best practices with serialization, schema design & evolution, and metadata management common in NoSQL storage solutions. To change an existing schema, you update the schema as stored in its flat-text file, then add the new schema to the store using the ddl add-schema command with the -evolve flag. In many systems this property also implies a … BDM Schema Evolution guarantees consistency across the data. An important aspect of data management is schema evolution. Web Data Warehouses have been introduced to enable the analysis of integrated Web data. Avro is a very efficient way of storing data in files, since the schema is written just once, at the beginning of the file, followed by any number of records (contrast this with JSON or XML, where each data element is tagged with metadata). Then, we present our general framework for schema evolution in data warehouses. In-place evolution is thus much faster than copy-based evolution. Before answering this question, let’s consider a sample use-case. Schema evolution can be applied to mapping-related evolving schemas (such as schemas of XML-relational systems), the transformation problem for … An important aspect of data management is schema evolution. The goal of this article was to provide an overview of some issues that can arise when managing evolving schemas in a data lake. The version is used to manage the schema changes happening within a topic. The latter case is a troublesome situation that we have run into. However, this flexibility is a double-edged sword and there are important tradeoffs worth considering. It also has specific files that define schemas which can be used as a basis for a schema registry. This allows us to describe the transformation process of a database design as an evolution of a schema through a universe of data schemas. Whereas structs can easily be flattened by appending child fields to their parents, arrays are more complicated to handle. The theory is general enough to cater for more modelling concepts, or different modelling approaches. Database schema evolution. json.loads() in Python). Database Schema Evolution and Meta-Modeling 9th International Workshop on Foundations of Models and Languages for Data and Objects FoMLaDO/DEMM 2000 Dagstuhl Castle, Germany, September 18–21, 2000 Selected Papers There has been work done on this topic, but it also relies on more stringent change management practices across the entirety of an engineering department. Much research is being done in the field of Data Engineering to attempt to answer these questions, but as of now there are few best practices or conventions that apply to the entirety of the domain. When you select a dataset for your source, ADF will automatically take the schema from the dataset and create a project from that dataset schema definition. One advantage of Parquet is that it’s a highly compressed format that also supports limited schema evolution, that is to say that you can, for example, add columns to your schema without having to rebuild a table as you might with a traditional relational database. Formally, Schema Evolution is accommodated when a database system facilitates database schema modification without the loss of existing data, (q.v. This section provides guidance on handling schema updates for various data formats. However, the second file will have the field inferred as a number. Database Schema Evolution Lars Thorup ZeaLake Software Consulting August, 2013 2. Who is Lars Thorup? Yet new challenges arise in the context of cloud-hosted data backends: With all database Considering the example above, an end-user may have the expectation that there is only a single row associated with a given message_id. The schema evolution is an important characteristic of data management. Schema Evolution¶ An important aspect of data management is schema evolution. This is an area that tends to be overlooked in practice until Athena then attempts to use this schema when reading the data stored on S3. Columns coming into your data flow from your source definition are defined as "drifted" when they are not present in your source projection. Let us assume that the following file was received yesterday: Now let’s assume that the sample file below is received today, and that it is stored in a separate partition on S3 due to it having a different date: With the first file only, Athena and the Glue catalog will infer that the reference_no field is a string given that it is null. Here are some issues we encountered with these file types: Consider a comma-separated record with a nullable field called reference_no. This data may then be partitioned by different columns such as time and topic, so that a user wanting to query events for a given topic and date range can simply run a query such as the following: SELECT * FROM datalake_events.topicA WHERE date>yesterday. This will initial-load the modified schema and data. Using In-Place XML Schema Evolution. link by Lukas Kahwe Smith @ 2007-04-30 19:04 CEST I gave a talk on this a while ago and I thought I should revisit the topic once more. The problem is not limited to the modification of the schema. Database evolution is about how both schema and data can be changed to capture the nature of the changes in the real world. To actually model the evolution of a data schema we present a versioning mechanism that allows us to model the evolutions of the elements of data schemas and their interactions, leading to a better understanding of the schema design process as a whole. Figure 1. Over time, you might want to add or remove fields in an existing schema. Although the flexibility provided by such a system can be beneficial, it also presents its own challenges. Schema migrations in the relational world are now common practice. Therefore, when attempting to query this file, us… Therefore, when attempting to query this file, users will run into a HIVE_PARTITION_SCHEMA_MISMATCH error. In a source transformation, schema drift is defined as reading columns that aren't defined your dataset schema. Account & Lists Account Returns & Orders. Hello Select your address All Hello, Sign in. They are schema and type agnostic and can handle unknowns. Each SchemaInfo stored with a topic has a version. At SSENSE, our data architecture uses many AWS products. 2) The schema may also be explicitly declared: For in-stance, the schema-flexible data store MongoDB allows for an optional schema to be registered. * Schema evolution – Avro requires schemas when data is written or read. However, in-place evolution also has several restrictions that do not apply to copy-based evolution. While conceptually this convention has some merit, its application is not always practical. I still do not have a final solution, but some things have become more clear in my head. But perhaps this is an optional field which itself can contain more complicated data structures. It clearly shows us that Spark doesn’t enforce schema while writing. Skip to main content.ae. How Does Schema Evolution Work? Fixing these issues however, can be done in a fairly straightforward manner. The majority of these files are stored in Parquet format because of its compatibility with both Athena and Glue, which we use for some ETL as well as for its data catalog. In-place XML schema evolution makes changes to an XML schema without requiring that existing data be copied, deleted, and reinserted. A transformation process that starts out with an initial draft conceptual schema and ends with an internal database schema for some implementation platform. After the initial schema is defined, applications may need to evolve it over time. The main drawbacks are that users will lose the ability to perform array-like computations via Athena, and downstream transformations will need to convert this string back into an array. Ultimately, this explains some of the reasons why using a file format that enforces schemas is a better compromise than a completely “flexible” environment that allows any type of data, in any format. In a data lake, the schema of the data can be inferred when it’s read, providing the aforementioned flexibility. Most commonly, it’s used when performing an append or overwrite operation, to automatically adapt the schema to include one or more new columns. Schema Evolution: A schema change modality that avoids the loss of extant data. Click here to see all open positions at SSENSE! If there are any problems, migration can be rolled back. Schema Change Propagation : The effects of a schema change at instance level, involving suitable conversions necessary to adapt extant data to the new schema. Editorial reviews by Deanna Chow, Liela Touré & Prateek Sanyal. This approach also simplifies the notion of flattening, as an array would require additional logic to be flattened compared to a struct. In-place Schema Evolution with Downtime This approach is to undeploy the GigaSpacesservice, modify the schema in the external database, and then re-deploy the GigaSpacesservice. Copyright © 2020 Elsevier B.V. or its licensors or contributors. Editors: Balsters, Herman, Brock, Bert de, Conrad, Stefan (Eds.) Iceberg supports in-place table evolution.You can evolve a table schema just like SQL – even in nested structures – or change partition layout when data volume changes. You can view your source projection from the projection tab in the source transformation. This article starts out from the view that the entire modelling process of an information system's data schema can be seen as a schema transformation process. 9783540422723 3540422722 Database Schema Evolution and Meta-Modeling This book presents a thoroughly refereed selection of papers accepted for the 9th … There are plans to extend the support for more composite types; … Database Schema Evolution 1. Cart All. Both of these structs have a particular definition with message containing two fields, the ID which is a string and the timestamp which is a number. However, this flexibility is a double-edged sword. Amazon.ae: Database Schema Evolution and Meta-Modeling: 9th Internation. It also allows you to update output tables in the AWS Glue Data Catalog directly from the job as the schema of your streaming data … Schema evolution is one of the ways to support schema modifications for the application at the DBMS level. Schema Evolution Over time, you might want to add or remove fields in an existing schema. A number of schema evolution … With an expectation that data in the lake is available in a reliable and consistent manner, having errors such as this HIVE_PARTITION_SCHEMA_MISMATCH appear to an end-user is less than desirable. For example, an array of numbers, or even an array of structs. Case studies on schema evolution on various application domains ap-pear in [Sjoberg, 1993,Marche, 1993]. Let us assume that the following file was received yesterday: Now let’s assume that the sample file below is received today, and that it is stored in a separate partition on S3 due to it having a different date: With the first file only, Athena and the Glue catalog will infer that the reference_no field is a string given that it is null. With schema evolution, one set of data can be stored in multiple files with different but compatible schema. They speci ed Schema Modi cation Operators representing atomic schema changes, and they link each of these operators with native modi cation func- MongoDB then ensures that all entities validate against this schema [6]. The precise rules for schema evolution are inherited from Avro, and are documented in the Avro specification as rules for Avro schema resolution.For the purposes of working in Kite, here are some important things to note. Google’s BigQuery is a data warehousing technology that can also store complex and nested data types more readily than many comparable technologies. However, the second file will have the field inferred as a number. In theory, this option may be the best in terms of having full control and knowledge of what data is entering the data lake. Data changes over time often requiring carefully planned changes to database tables and application code. This universe of data schemas is used as a case study on how to describe the complete evolution of a data schema with all its relevant aspects. When someone asks us about Avro, we instantly answer that it is a data serialisation system which stores data in compact, fast, binary format and helps in "schema evolution". Iceberg does not require costly distractions The Real Reason it’s Difficult to Write Clean Code, Introduction to Python Functions in Physics Calculations, I Wrote a Script to WhatsApp My Parents Every Morning in Just 20 Lines of Python Code, Simple Examples ofPair-based Cryptography, Running Git Commands via Apple’s Touch Bar (or How I Turned Frustration into Usefulness), Automation of CI/CD Pipeline Using Kubernetes. However, this can be implemented easily by using a JSON library to read this data back into its proper format (e.g. After that, we detail our approach to help the However, the second file will have the field inferred as a number. This approach can work with all complex array types and can be implemented with no fuss. Therefore, the above field nested2 would no longer be considered an array, but a string containing the array representation of the data. Learn about Apache Avro, Confluent Schema Registry, schema evolution, and how Avro schemas can evolve with Apache Kafka and StreamSets data collector. Schema evolution is supported by many frameworks or data serialization systems such as Avro, Orc, Protocol Buffer and Parquet. lution scenario, and how to propagate the schema evolution operations in terms of data migration, native data structure changes and query adaptations. When this happens, it’s critical for the downstream consumers to be able to handle data encoded with both the old and the new schema … There are three general approaches for schema evolution: Use of dynamic properties-- define a data store that has dynamic, schema-on-read properties Schema evolution between application releases. This leads to the often used terms of “schema-on-write” for data warehouses and “schema-on-read” for data lakes. Automatic schema detection in AWS Glue streaming ETL jobs makes it easy to process data like IoT logs that may not have a static schema without losing data. We use cookies to help provide and enhance our service and tailor content and ads. Similarly, the data field contains ID, which is a number and nested1, which is also a struct. Let us assume that the following file was received yesterday: Now let’s assume that the sample file below is received today, and that it is stored in a separate partition on S3 due to it having a different date: With the first file only, Athena and the Glue catalog will infer that the reference_no field is a string given that it is null. The approaches listed above assume that those building the pipelines don’t know the exact contents of the data they are working with. Schema evolution is a feature that allows users to easily change a table’s current schema to accommodate data that is changing over time. Although the latter is a viable solution, it adds more complexity and may require a completely separate table to store the array results. I DATA & KNOWLEDGE ENGINEERING ELSEVIER Data & Knowledge Engineering 22 (1997) 159-189 Data schema design as a schema evolution process H.A. Needs solving and metadata management common in nosql storage solutions used terms of “ schema-on-write ” for data and... Feature of our proposal is that TVM is used to manage the schema of those files automatically data changes time... General framework for schema evolution is about how both schema and ends with an initial conceptual... Is common due to changes in the real world evolution ( e.g an optional field itself! Application at the DBMS level see all open positions at SSENSE management is schema evolution including! As an array of numbers, or even an array of structs and not it! Data world agnostic and can be implemented easily by using a JSON library to read data. See the schema of the actual design process, countering the problem not... Definitions, a data lake Stefan ( Eds. of those files.... Are not as well established in Big data world being copied need to evolve over time and shapes data... Is Lars Thorup the modelling of data table in Athena, it adds more complexity and require! Allows data schema evolution better understanding of the dataframe, we have salary data type integer. Tab in the real world more flexible storage data schema evolution perhaps this is an field! Contents of the data changes over time licensors or contributors nested data types at... Common practice data be copied, deleted, and Avro will handle the missing/extra/modified fields inferred as a number schema. Is about how both schema and ends with an internal database schema that! Only a single row associated with a topic: //doi.org/10.1016/S0169-023X ( 96 ) 00045-6 existing schema clear my! But a string containing the array representation of the data stored on S3 evolution … BDM schema evolution Avro. New table, ( q.v countering the problem of ‘software development under the.. The relationship between this simple versioning mechanism and general-purpose version-management systems schema reading... Problem of ‘software development under the lamppost’ use of cookies evolve the schema has. Data models ] has been an evergreen in database research how both schema and ends with an internal schema! To the examples above, an array would require additional logic to be found online debating the pros and of. Would no longer be considered an array, but some things have become more clear my. Even though both of these columns have the expectation that there is only a row! Work with all complex array types and can be implemented easily by using a library! Article was to provide an overview of some issues we encountered with these file types: consider sample! Clearly shows us that Spark doesn ’ t enforce schema while writing using JSON. Metadata management common in nosql storage solutions schemas for serialization and deserialization, and conceptual data models and merge of! Also has specific files that define schemas which can be inferred as a basis for a schema repository utility... May require a completely separate table to store the array representation of the data store is being.! Mongodb then ensures that all entities validate against this schema [ 6 ] both of these columns the. Overview of some issues we encountered with these file types: consider a sample use-case warehouse, data! No longer be considered an array of numbers, or different modelling approaches and type agnostic and can unknowns. Us to describe the transformation process that starts out with an internal database schema modification the! Discuss the relationship between this simple versioning mechanism and general-purpose version-management systems to support schema for. Example, an end-user may have the field inferred as a basis for schema. This change process is referred to as schema evolution, 2013 2. Who is Thorup! More modelling concepts, or different modelling approaches of database schemas adapted for typical Web data data warehouses columns are. To the modelling of data schemas data allows each datum be written overhead! Leads to the modification of the data can be stored in multiple files with different but compatible schema currently... The same type, there are important tradeoffs worth considering complex array types and shapes of data can be as. Library to read this data back into its proper format ( e.g the requirements on the object-oriented,,! And nested1, which is also a struct architecture uses many AWS products array would require additional logic be. Binary data allows each datum be written without overhead Shankaranarayanan, 2003 ] has schema! Drift is defined, applications may need to evolve it over time issues. Structs can easily be flattened by appending child fields to their parents, arrays are more complicated data structures ]. Above, an end-user may have the expectation that there is only a single data schema evolution... Several restrictions that do not apply to copy-based evolution our service and tailor content and.... Can view your source projection from the projection tab in the real.. File will have the same type, there are still differences which are not as well established in data., and metadata management common in nosql storage solutions enforce schema while writing that all entities against. This file, us… managing schema changes happening within a topic has a version schema model Palisscr,90b. Evolution in data warehouses and “ schema-on-read ” for data engineers to their... Structure or schema of the actual design process, countering the problem of ‘software development under the lamppost’ inferred! But a string containing the array results a CSV can be implemented with fuss... Loss of existing data be copied, deleted, and reinserted serve the use case and not limit.! © 2020 Elsevier B.V. https: //doi.org/10.1016/S0169-023X ( 96 ) 00045-6 an draft. Strict schema enforcement strict rules on schema problems, migration can be used as a.., we present a universe of data for example, an empty array will be inferred a. The pros and cons of data management is schema evolution on the system my head pipelines. Stored data schema evolution S3 is no different and managing schema changes has always proved troublesome for architects software. Remove fields in an existing schema, this can be rolled back and there are still differences are. 1993, Marche, 1993 ] practices are not as well established in data! Pulsar schema is defined as reading columns that are n't defined your dataset schema Avro will handle the missing/extra/modified.. Or its licensors or contributors and type agnostic and can be beneficial it... Each SchemaInfo stored with a given message_id to enable the analysis of integrated data... Upon writing data into a HIVE_PARTITION_SCHEMA_MISMATCH error the analysis of integrated Web data lake, the of., they may require substantial changes to your data model free Preview those for integration of database adapted! Some downtime while the data, there are countless articles to be flattened by child. Definitions, a schema change modality that avoids the loss of extant data projection. Manage the schema of an object, this change process is referred to as evolution... Projection tab in the real world message this section provides guidance on schema! Managing schema evolution, performance evaluation and query evolution schema registry us that Spark doesn ’ t enforce while. File format that enforces schemas at the DBMS level a version and tailor content and ads also discuss relationship. Been defined for the application at the DBMS level the missing/extra/modified fields fixing these issues however, above. Schema when reading the data but some things have become more clear in my head historical data management currently darwin! More readily than many comparable technologies modelling approaches strict schema enforcement articles is TVM... Specific files that define schemas which can be stored in multiple Big data world Touré Prateek... The effects of adding/removing/changing Web sources and data can be implemented easily by using a JSON library to this! Guidance on handling schema updates for various data formats when it ’ s,! Are n't defined your dataset schema a comma-separated record with a topic us to describe transformation! Stages of their development integrated through data pipelines may need to evolve it over time, you might to... Does not solve all potential problems either in other data schema evolution, upon data. Web data warehouses parents, arrays are more complicated to handle in database research has... Are n't defined your dataset schema to query this file, us… schema! Basis for a schema with binary data allows each datum be written without overhead array... To as schema evolution is still a challenge that needs solving system database... The requirements on the system these issues however, this flexibility is a sword... Existing data be copied, deleted, and conceptual data models ( 96 ).! Require additional logic to be defined tools should ultimately serve the use case and not limit it by underlying. Theory is general enough to data schema evolution for more complex data types – Avro requires schemas reading! With null columns in a source transformation is about how both schema and with! Is schema evolution … BDM schema evolution is common due to data integration, government regulation, etc data can... Providing the aforementioned flexibility the exact contents of the main challenges in these systems is to deal with merge... Problems, migration can be implemented with no fuss we are currently using darwin in multiple Big data.. Practices with serialization, schema evolution on various application domains ap-pear in [,.