This topic describes the release notes for key features of MaxCompute.
For more information about the major feature updates of MaxCompute, see Product Updates.
2025
July
Feature | Description | Release date | Regions | References |
MaxCompute supports automatic table partitioning based on time calculation functions | MaxCompute can automatically partition a table by applying specific functions to its time or date columns. The results of these functions are used to generate partition values. | 2025-07-31 | All regions | |
MaxCompute supports automatic table partitioning based on data write time | MaxCompute automatically retrieves the time when data is written to a table. It uses this time with a specified function, such as TRUNC_TIME, to generate partition column values and partition the table. | 2025-07-31 | All regions | |
MaxFrame jobs support worker-level CPU and memory monitoring | MaxFrame lets you use Fuxi Sensor to view the actual CPU and memory consumption of a specific worker. | 2025-07-17 | All regions | |
New built-in large models added to MaxFrame AI Function | The MaxFrame AI Function adds built-in Qwen 3 series models, from Qwen3-0.6B to Qwen3-14B. | 2025-07-17 | All regions | |
MaxFrame supports the partial commit feature for jobs | In large-scale jobs, some instances may run for a long time due to issues such as data skew or infinite loops. This can slow down the job or cause it to fail. The partial commit feature allows a small part of the job to be ignored, letting you retrieve results from successful instances even if the overall job fails. | 2025-07-17 | All regions |
May
Feature | Description | Release date | Regions | References |
MaxCompute SQL engine enhancements | The new SQL V51 version is online:
| 2025-05-30 | All regions |
April
Feature | Description | Release date | Regions | References |
Upgrade of MaxCompute data transmission service observability | Data accuracy is improved by optimizing the metric data collection and processing pipeline. New capabilities are added to support viewing resource usage at the project level, along with more usage options. This enhancement enriches the observability of the data transmission service, enabling support for a broader range of analysis scenarios and requirements. | 2025-04-24 | China (Hangzhou), China (Shanghai), China (Beijing), China (Shenzhen), China (Chengdu), China East 1 Finance, China (Hong Kong), Singapore, Japan (Tokyo), Malaysia (Kuala Lumpur), Indonesia (Jakarta), Germany (Frankfurt), UK (London), US (Silicon Valley), US (Virginia), and SAU (Riyadh - Partner Region) | |
MaxFrame AI Function released | The MaxFrame AI Function feature allows users to call large models through a simple and user-friendly programming interface to perform offline processing on massive datasets within MaxCompute tables. | 2025-04-09 | China (Hangzhou), China (Shanghai), China (Beijing), China (Zhangjiakou), China (Ulanqab), China (Shenzhen), China (Chengdu), China (Hong Kong), Japan (Tokyo), Singapore, Malaysia (Kuala Lumpur), Indonesia (Jakarta), Germany (Frankfurt), UK (London), US (Silicon Valley), US (Virginia), UAE (Dubai), and SAU (Riyadh - Partner Region) |
March
Feature | Description | Release date | Regions | References |
Custom policy-based cost estimation released for optimization of tiered storage configuration | The optimization of tiered storage configuration supports the custom policy-based cost estimation feature. This allows users to estimate changes in storage costs based on custom policies, in addition to the default policy, to assist in decision-making for configuring tiered storage policy attributes. | 2025-03-24 | China (Hangzhou), China (Shanghai), China (Beijing), China (Shenzhen), China (Chengdu), China (Zhangjiakou), China (Ulanqab), China (Hong Kong), Japan (Tokyo), Singapore, Malaysia (Kuala Lumpur), Indonesia (Jakarta), and Germany (Frankfurt) | |
Multi-zone storage mode released | Multi-zone storage mode has been launched for MaxCompute, and the original default storage mode has been renamed to single-zone storage. We recommend that you use multi-zone storage mode for data related to enterprise production workloads, which can withstand zone-level failures. In the event of a zone-level failure, multi-zone storage ensures uninterrupted data read and write services while preventing data loss. | 2025-03-24 | China (Hangzhou), China (Shanghai), China (Beijing), China (Shenzhen), China East 2 Finance, China (Hong Kong), Singapore, and Indonesia (Jakarta) | |
Clustering optimization recommendation released | The MaxCompute clustering optimization recommendation feature analyzes the recent read and write characteristics of a table and generates corresponding clustering suggestions to help improve job performance and reduce CU consumption. You can decide whether to apply the system-recommended clustering suggestions based on the estimated benefits and recommendation details. Additionally, you can view the daily benefits after applying the suggestions. | 2025-03-17 | China (Hangzhou), China (Shanghai), China (Beijing), China (Zhangjiakou), China (Shenzhen), and China (Chengdu) |
February
Feature | Description | Release date | Regions | References |
Automatic Materialized View (AutoMV) released | Materialized views can be automatically created based on your job query habits and performance. This improves computing efficiency and reduces repeated computing. This feature is suitable for scenarios where multiple repeated queries are performed on a MaxCompute table and different users are unaware that others are also using the same computational logic. | 2025-02-20 | Public cloud regions in the Chinese mainland | |
Public preview of MaxCompute Query Accelerator (MaxQA) | Control links, cache capabilities, and SQL computing engines are optimized on the basis of an exclusive resource pool for query acceleration. This helps reduce the query response time. This feature is suitable for business scenarios that require low latency and high stability, such as BI, interactive analytics, and near real-time data warehousing. | 2025-02-17 | All regions | |
MaxCompute SQL upgraded to enhance engine capabilities | MaxCompute SQL V50 is launched.
| 2025-02-10 | All regions | |
Dynamic data masking released | The dynamic data masking feature is provided to support multiple masking policies, such as masking, hashing, character replacement, numeric value rounding, and date rounding. This feature effectively protects sensitive data such as personally identifiable information (PII) in scenarios such as business development and testing, data sharing, and O&M. | 2025-02-10 | China (Hangzhou), China (Shanghai), China (Beijing), China (Zhangjiakou), China (Ulanqab), China (Shenzhen), China (Chengdu), China (Hong Kong), Japan (Tokyo), Malaysia (Kuala Lumpur), Indonesia (Jakarta), and Germany (Frankfurt) | |
Row-level permission feature updated | The row-level permission feature is updated to support the result cache, materialized view, and local mode scenarios. This update, together with other optimized features, improves the usability and performance of the row-level permission feature. | 2025-02-10 | All regions | |
Lakehouse and external table capabilities enhanced |
| 2025-02-10 | All regions |
January
Feature | Description | Release date | Regions | References |
Overview page released in the console | An overview page is added to the MaxCompute console to provide information such as a quick start guide, a list of resource instances, followed projects and quotas, alert and risk warnings, and product updates. This helps you quickly get started with O&M and management. | 2025-01-20 | All regions |
2024
November
Feature | Description | Release date | Region | References |
Adaptation of LogView 2.0 to MaxFrame | LogView 2.0 allows you to view the execution records and running durations of all directed acyclic graphs (DAGs) submitted in MaxFrame sessions. | 2024-11-29 | China (Hangzhou), China (Shanghai), China (Beijing), China (Shenzhen), China (Chengdu), China (Ulanqab), and China (Hong Kong) | |
Optimization of the materialized view recommendation and management feature | Estimated impact and actual benefit metrics are added to help you determine whether to adopt recommendations and manage existing materialized views. | 2024-11-26 | All regions | |
Release of intelligent job diagnostics | The intelligent job diagnostics feature can be used to perform intelligent diagnostics on SQL jobs based on the execution details of jobs. This helps you identify issues such as job errors or abnormal job runtime in a timely manner, and provides exception analysis results and solutions. | 2024-11-18 | All regions | |
Automatic packaging service of MaxFrame | MaxFrame allows you to declare required external dependencies during job development. This simplifies the management of third-party packages in Python development. | 2024-11-01 | China (Hangzhou), China (Shanghai), China (Beijing), China (Shenzhen), China (Chengdu), China (Ulanqab), and China (Hong Kong) |
October
Feature | Description | Release date | Region | References |
Purchase of multiple level-1 subscription computing quotas in the same region | MaxCompute allows you to purchase multiple level-1 subscription computing quotas for the same Alibaba Cloud account in the same region. Each level-1 subscription computing quota has independent resources. | 2024-10-14 | All regions |
September
Feature | Description | Release date | Region | References |
Release of storage cost optimization | The storage cost optimization feature determines whether the storage tier of a table or partition can be changed from standard storage to infrequent access (IA) or long-term storage based on the default optimization policy and the last access time of the table or partition. The default optimization policy recommends that you change the standard storage tier of a table or partition that is not accessed in the previous 30 days to the IA storage tier and change the standard storage tier of a table or partition that is not accessed in the previous 180 days to the long-term storage tier. This feature also estimates the storage cost savings (catalog price) after the storage tier is changed. This helps you configure tiered storage policies. | 2024-09-27 | China (Hangzhou), China (Shanghai), China (Beijing), China (Shenzhen), China (Chengdu), China (Zhangjiakou), and China (Ulanqab) | |
Optimized UI for quota management | The UI for quota management of subscription computing resources in the MaxCompute console is optimized. The UI provides two tabs for interactions based on the original logic. This helps improve the experience of quota management.
| 2024-09-11 | All regions | Manage quotas for computing resources in the MaxCompute console |
August
Feature | Description | Release date | Region | References |
Release of storage resource observation | You can view the total storage usage and the storage usage percentages of different storage types in the selected region. You can also view the storage usage trends of different storage types and the detailed table or partition storage information based on the project and the time range that you select. This way, you can determine whether the storage usage unexpectedly spikes. If a spike occurs, you can optimize the top N projects, tables, or partitions that consume the most storage resources at the earliest opportunity. | 2024-08-16 | China (Hangzhou), China (Shanghai), China (Beijing), China (Shenzhen), China (Chengdu), China (Zhangjiakou), and China (Ulanqab) | |
Improved job performance observation | You can view the trend of data amounts that are scanned by jobs per CU-hour, the top N jobs that consume the most resources, and the top N jobs that consume the most time. You can use metrics to quickly locate jobs that consume a large amount of resources or require a long period of time, and manage the jobs in a timely manner. You can also identify the time periods in which the job performance is poor, locate the causes based on the job O&M feature, and troubleshoot related issues in a timely manner. | 2024-08-13 | All regions |
July
Feature | Description | Release date | Region | References |
Job performance observation | This feature allows you to use relevant metrics to check whether the number of jobs or the resource usage unexpectedly surges. If surges occur, you can resolve these issues at the earliest opportunity. This feature also allows you to compare running durations of jobs during different periods and determine whether slow-running jobs cause overall job performance to deteriorate. | 2024-07-31 | All regions | |
Availability of the Start Time, Wait Time, and Execution Duration parameters on the Jobs page and Job Summary tab | Based on these parameters, you can quickly determine whether a job slowly runs because a long period of time is spent waiting for resource allocation. You can further determine whether the slow running of the job is caused by resource insufficiency or resource competition among multiple jobs based on the parameters and job analysis results. Then, you can optimize job execution, adjust job priorities, or manage computing resources based on your business scenarios. | 2024-07-04 | All regions | |
Availability of computing cost optimization in four regions | You can use the computing cost optimization feature to generate a resource configuration optimization plan for the computing resources in a level-1 subscription quota based on the actual number of job requests and expected resource configurations. You can also use this feature to generate a resource configuration optimization plan for a project that changes the pay-as-you-go computing resources to subscription computing resources and view the estimated optimization effect. This helps you further reduce computing costs and improve resource utilization. | 2024-07-01 | China (Hong Kong), Singapore, Indonesia (Jakarta), and Germany (Frankfurt) |
June
Feature | Description | Release date | Region | References |
Row-level access control | MaxCompute supports row-level access control to help manage the permissions of users or roles to access specific data in MaxCompute tables. You can define the rules for matching users and the data that is allowed to access in a MaxCompute table. This way, specific users or roles can view only the data that they have permissions to access. This improves data security and compliance. | 2024-06-28 | All regions |
May
Feature | Description | Release date | Region | References |
Release of the job performance observation feature | For computing jobs, you can use the metrics related to the overall job runtime to check whether the number of jobs or the resource usage unexpectedly surges and check whether job performance meets your expectations. The metrics include the number of jobs, CU usage, and job running durations. | 2024-05-08 | All regions |
April
Feature | Description | Release date | Region | References |
Row filtering based on window functions | Window functions allow you to filter out rows that are not used for calculation. | 2024-04-10 | All regions | |
Binary constants | Binary constants are supported to provide an easy method to use data of the BINARY type. You can use X'num' to represent binary constants. | 2024-04-10 | All regions | |
FROM_CHARSET | The FROM_CHARSET function is added to convert binary data that is in a specified encoding format into a UTF-8 encoded string for subsequent calculation. | 2024-04-10 | All regions | |
TIMESTAMP_NTZ data type | The TIMESTAMP_NTZ data type is added. Dates and time points of the TIMESTAMP_NTZ type do not contain time zones and are not affected by the current system time zone. | 2024-04-10 | All regions |
March
Feature | Description | Release date | Region | References |
Activation of MaxCompute in the China (Ulanqab) region | MaxCompute can be activated in the China (Ulanqab) region to provide an enterprise-level serverless intelligent data warehousing service. You can activate the service in the MaxCompute console based on your business requirements. | 2024-03-26 | China (Ulanqab) | - |
Migration of ActionTrail event data to MaxCompute | The ActionTrail service allows you to trace and record the event data of your Alibaba Cloud account within the previous 90 days. If you want to analyze event data that was generated a longer period of time ago, you can create a trail by using ActionTrail to deliver the event data to MaxCompute. Then, you can query and analyze the event data. | 2024-03-14 | All regions | |
Release of SQL Analysis | The SQL Analysis feature of MaxCompute is supported. You can use this feature to perform the following operations: Edit and execute SQL statements, and analyze the execution results in charts. View the metadata of tables, resources, and user-defined functions (UDFs) in all projects, including the metadata of views provided by the built-in tenant-level Information Schema service. Open an SQL file of the built-in public dataset demo and run the code to use and test MaxCompute based on the public datasets. | 2024-03-14 | All regions |
January
Feature | Description | Release date | Region | References |
Release of computing cost optimization | The computing cost optimization feature is released to replace the computing resource optimization feature of MaxCompute. The computing cost optimization feature is developed based on the computing resource optimization feature. Compared with the computing resource optimization feature, the computing cost optimization feature can generate a resource configuration optimization plan for changing the pay-as-you-go computing resources of a project to subscription computing resources and allows you to view the estimated optimization effect. This helps you further optimize computing costs and improve resource utilization. | 2024-01-25 | All regions | |
Change of the metric aggregation algorithm on the Resource Observation page in the MaxCompute console | The Resource Observation page in the MaxCompute console is provided to improve user experience on the chart display. A maximum of 60 time points can be displayed for each metric in a chart. Therefore, if the selected time range is greater than one hour, the average aggregation algorithm is used and the aggregation data within the time range is displayed in a chart by default. The average aggregation data is the number of minutes within the selected time range divided by 60. You can change the average aggregation algorithm to the maximum value aggregation algorithm based on your business requirements for a more comprehensive analysis of resource consumption. | 2024-01-02 | All regions |
2023
December
Feature | Description | Release date | Region | References |
Release of the Spot Edition | The Spot Edition of MaxCompute provides pay-as-you-go computing resources with lower unit prices. This reduces the cost of using MaxCompute in latency-insensitive scenarios. | 2023-12-28 | China (Hangzhou), China (Shanghai), China (Beijing), China (Shenzhen), China (Hong Kong), Singapore, UK (London), Japan (Tokyo), Malaysia (Kuala Lumpur), Germany (Frankfurt), Indonesia (Jakarta), US (Virginia), and US (Silicon Valley) | |
Editing of public IP addresses in the MaxCompute console | You can add or remove public IP addresses that are available for projects in the MaxCompute console. | 2023-12-20 | All regions |
November
Feature | Description | Release date | Region | References |
Configuration for the processing of dirty data in Tablestore external tables | The logic for processing dirty data can be configured when data is read from a Tablestore external table. | 2023-11-22 | All regions | |
Apache Paimon external tables | An Apache Paimon external table in MaxCompute can be used to read data from Apache Paimon. | 2023-11-22 | All regions | |
Column-level data encryption | Keysets, key rotation, and data encryption and decryption based on Key Management Service (KMS) keys are supported for column-level encryption. | 2023-11-22 | All regions | |
Configuration of Super_Administrator role members based on RAM permission control | RAM users that have the UpdateUsersToSuperAdmin permission can be assigned the Super_Administrator role in a project. | 2023-11-21 | All regions |
September
Feature | Description | Release date | Region | References |
MapReduce jobs in SQL runtime execution mode | MapReduce jobs in MaxCompute can run in SQL runtime execution mode. | 2023-09-25 | All regions | |
Job analytics | The job analytics feature is supported in MaxCompute to help developers identify job resource issues. | 2023-09-21 | All regions |
August
Feature | Description | Release date | Region | References |
Tenant-level Information Schema | Metadata such as project metadata and usage history is provided from the perspective of tenants in Tenant-level Information Schema. Tenant-level Information Schema allows you to obtain all metadata of a type of object of a tenant at a time. | 2023-08-08 | All regions |
June
Feature | Description | Release date | Region | References |
JSON data types | JSON data types are supported in MaxCompute. When JSON data is written to a MaxCompute table, MaxCompute automatically performs storage optimization to improve the computing and analysis performance of JSON data. | 2023-06-27 | All regions | |
Enhanced capabilities of ZORDER BY for data writing | The capabilities of the ZORDER BY clause in MaxCompute are enhanced. After the enhancement, the ZORDER BY clause supports global sorting when this clause is executed to write data to a table. This reduces the amount of data that is scanned and improves computing performance. | 2023-06-27 | All regions | |
ALTER TABLE CLEAR COLUMN | The ALTER TABLE CLEAR COLUMN statement can be executed in MaxCompute to clear data from a specific table or columns in specific partitions. This helps save storage space. | 2023-06-27 | All regions | |
Addition of eight built-in complex type functions | Eight built-in complex type functions are added to MaxCompute. These functions help simplify user operations on arrays and improve development efficiency. | 2023-06-27 | All regions |
May
Feature | Description | Release date | Region | References |
PutRow operation for Tablestore external tables | The PutRow operation is supported for Tablestore external tables in MaxCompute. | 2023-05-06 | All regions | |
Support for the first row specified as a table header | When MaxCompute is used to write data to a CSV file in Object Storage Service (OSS), the first row can be specified as a table header. | 2023-05-06 | All regions | |
New built-in functions | Four mathematical functions, three date functions, and one string function are added to MaxCompute. You can use these built-in functions to simplify SQL code. | 2023-05-06 | All regions | |
Scheduled data updates of materialized views | Multiple scheduled data update methods are supported for materialized views. These methods allow the latest data to be stored in materialized views. | 2023-05-06 | All regions | |
Built-in encryption and decryption functions | Built-in encryption and decryption functions are provided in MaxCompute SQL. You can use these functions to manually encrypt or decrypt columns in tables. | 2023-05-06 | All regions | |
QUALIFY | The QUALIFY syntax can be used in MaxCompute to filter the results of window functions based on specified filter conditions. This simplifies SQL code in MaxCompute. | 2023-05-06 | All regions | |
TABLESAMPLE | In MaxCompute, TABLESAMPLE can be used to sample table data in different methods. | 2023-05-06 | All regions |
April
Feature | Description | Release date | Region | References |
Schema evolution | The schema evolution feature can be used to add columns of complex data types, delete columns, change the column sequence, and change the data types of columns. | 2023-04-25 | All regions | |
Computing acceleration based on materialized views | After you enable this feature, the system automatically provides the recommended SQL scripts for you to create materialized views. This feature helps accelerate the creation of materialized views, reduces repeated computing operations, and saves computing resources. | 2023-04-23 | All regions | |
Job management in the new MaxCompute console | In the new MaxCompute console, you can manage all MaxCompute jobs that are listed and view job snapshots and current job information. | 2023-04-06 | All regions |
March
Feature | Description | Release date | Region | References |
Processing of unstructured data in external volumes | In MaxCompute, you can run Spark jobs and MapReduce jobs to process unstructured data in external volumes based on OSS. | 2023-03-30 | All regions | Use MaxCompute external volumes to process unstructured data |
Storage of unstructured data in external volumes | You can use external volumes provided by MaxCompute to store unstructured data. External volumes in MaxCompute are management objects that are mapped to OSS directories. | 2023-03-30 | All regions |
February
Feature | Description | Release date | Region | References |
Display of fine-grained data objects | Information about fine-grained data objects such as tables, views, and functions can be displayed in the output of the SHOW command. | 2023-02-25 | All regions |
January
Feature | Description | Release date | Region | References |
Direct data reading based on Hologres external tables | If you enable this feature, the number of connections that are established between MaxCompute and a Hologres instance can be reduced and the data reading speed can be accelerated when you read data directly from the Hologres instance. | 2023-01-21 | All regions |
2022
December
Feature | Description | Release date | Region | References |
WINDOW keyword in SQL statements | The WINDOW keyword is supported. You can use the WINDOW keyword to specify a custom window only once and reuse the custom window. | 2022-12-14 | All regions | |
From clause used together with the UPDATE statement | The UPDATE statement can be used together with the from clause to update data. | 2022-12-14 | All regions | |
New string function and optimization of string functions and aggregate functions |
| 2022-12-14 | All regions |
November
Feature | Description | Release date | Region | References |
Custom administrator roles | MaxCompute provides the following built-in administrator roles for projects: Admin and Super_Administrator. MaxCompute also allows you to create custom administrator roles. To create a custom administrator role, you can perform the following operations in the MaxCompute console of the new version: In the left-side navigation pane of the MaxCompute console, click Projects. On the Projects page, find the desired project and click Manage in the Actions column. On the page that appears, click the Role Permissions tab and click Create Project-level Role. In the Create Role dialog box, select Admin from the Role Type drop-down list. You can grant only specific administrator permissions to custom administrator roles. For example, you can grant permissions to a role to only manage permissions or to only manage IP address whitelists. | 2022-11-15 | All regions |
October
Feature | Description | Release date | Region | References |
Upgrade of the data storage hierarchy from | A MaxCompute project is a basic organizational unit of MaxCompute and is used for isolation and access control of multiple users. A MaxCompute project contains objects such as tables, resources, and functions. Before the schema feature is provided by MaxCompute, the objects are directly placed in projects. Projects serve as databases or schemas based on the hierarchy of traditional databases. This may cause misunderstanding and inconvenience to users, especially when a large number of tables or objects exist. MaxCompute provides the schema feature. You can use schemas to classify tables, resources, and functions in projects. If the original hierarchy of data storage is | 2022-10-13 | All regions |
September
Feature | Description | Release date | Region | References |
Creation of Hologres external tables in dual-signature authentication mode | The dual-signature authentication mode is an authentication protocol that is developed based on MaxCompute and Hologres. After the user logon information and signature are used on MaxCompute, authentication data is sent to Hologres. Then, Hologres performs authentication by using the same username based on the protocol that is compatible with the underlying layer of MaxCompute. This way, you can directly access Hologres external tables without the need to configure additional authentication information only if you use the same Alibaba Cloud account to access MaxCompute and Hologres. | 2022-09-24 | All regions | |
Creation of a MaxCompute table with the same schema as a table in external data sources by using | You can use the MaxCompute lakehouse solution to create a MaxCompute table that has the same schema as a table in external data sources such as E-MapReduce (EMR), Hadoop, and Data Lake Formation (DLF). You can use the | 2022-09-23 | All regions |
August
Feature | Description | Release date | Region | References |
Automatic deletion of expired partitioned tables | If the lifecycle of partition data in a partitioned table expires, MaxCompute automatically reclaims the partition data. When the data of all partitions is reclaimed, MaxCompute can automatically delete the partitioned table. You can configure parameters to enable the automatic table deletion feature. | 2022-08-27 | All regions | |
New aggregate functions | The following aggregate functions are added: | 2022-08-27 | All regions | |
Schema copy of external tables | When you create an internal table in MaxCompute, you can use the | 2022-08-27 | All regions | |
New function for materialized views | A function is added to MaxCompute. This function allows you to query the status of materialized views. You can use this function to check whether the data of the current materialized view is the same as the data of the original table. You can also use this function to check whether the data of a partition in the current materialized view is the same as the data of the mapped partition in the original table. If the data is the same, True is returned. If the data is different, False is returned. | 2022-08-27 | All regions | |
Empty partitions generated in materialized views | When you update the data of a partition in a partitioned materialized view, MaxCompute can generate an empty partition in the partitioned materialized view if the calculation result of the materialized view indicates that the partition does not contain data. This ensures that partitions are consecutively generated. | 2022-08-27 | All regions | |
Job-level quota groups | MaxCompute allows you to specify a quota group at the job level. This helps you use quota groups in a flexible manner. If specific jobs in a project occupy a large number of resources, the overall timeliness for jobs in the project is affected. For example, data refresh jobs occupy a large number of resources but have low timeliness requirements, and specific algorithm jobs occupy a large number of resources and have high timeliness requirements. In this case, you can specify different quota groups for the jobs to isolate resources between the jobs for data computation. This way, you do not need to create another project to migrate the jobs to the new project and associate a quota group with the project to implement resource isolation. | 2022-08-23 | All regions |
July
Feature | Description | Release date | Region | References |
Addition of a regular function | The regular function | 2022-07-14 | All regions | |
Custom prefixes and extensions of exported file names | When you use the Unload function to export data from MaxCompute to OSS, you can configure prefixes and extensions for the exported data files. | 2022-07-14 | All regions | |
Table split size settings | MaxCompute allows you to configure a split size for tables to control the job parallelism. If resources are sufficient but jobs run at a low speed or if jobs take a large amount of time to wait for resource allocation and resources are insufficient, you can adjust the split size to improve the computing efficiency. | 2022-07-14 | All regions | |
Addition and performance tuning of window functions | The | 2022-07-14 | All regions | |
Addition of aggregate functions | The following aggregate functions are added: | 2022-07-14 | All regions |
June
Feature | Description | Release date | Region | References |
May
Feature | Description | Release date | Region | References |
Separate charging for MaxCompute external tables based on the types of external tables | MaxCompute external tables are separately charged based on the types of external tables. You can view the fees that are incurred by OSS external tables and Tablestore external tables in your bills. This way, you can view the fees that are incurred by different data sources for joint computing. | 2022-05-17 | All regions |
March
Feature | Description | Release date | Region | References |
DISTRIBUTED MAPJOIN | In specific scenarios, DISTRIBUTED MAPJOIN hints can be used to improve the computing performance and reduce the time required for data computation. | 2022-03-17 | All regions | |
Enhancement of OSS external tables | When MaxCompute writes data to an OSS external table, MaxCompute can automatically create a directory to store the data. When you create an OSS external table, you can specify the cache capacity for reading files. | 2022-03-17 | All regions | |
New parsing method of JSON data | MaxCompute allows you to enclose a key in JSON data whose value contains a period (.) with brackets and single quotation marks (['']) to parse the JSON data. | 2022-03-17 | All regions | |
Enhancement of the TRIM, LTRIM, and RTRIM functions | MaxCompute allows you to use the TRIM, LTRIM, and RTRIM functions to remove specified characters from the left side, the right side, or both sides of a string. | 2022-03-17 | All regions | |
Enhancement of the query rewrite feature for materialized views | The query rewrite feature can be enabled for a materialized view to help you rewrite a query statement that includes an OUTER JOIN clause, a UNION clause, or a UNION All clause. | 2022-03-17 | All regions | |
Skipping of headers or footers of TEXTFILE files | The | 2022-03-01 | All regions | |
Compatibility with Apache Spark 3.1 | MaxCompute is compatible with Apache Spark 3.1 in addition to the following Apache Spark versions: 1.6, 2.3, and 2.4. | 2022-03-01 | All regions |
February
Feature | Description | Release date | Region | References |
Data security management of LogView | A custom parameter can be used to specify whether to display the execution results of jobs in LogView of MaxCompute. This enhances data security. | 2022-02-25 | All regions | |
Table schema changes | The schema of a table in MaxCompute can be changed. You can add fields of complex data types to a table, delete fields from a table, and change the sequence of fields in a table. | 2022-02-23 | All regions |
January
Feature | Description | Release date | Region | References |
Display of MaxCompute external project metadata on DataWorks Data Map | The metadata of MaxCompute external projects can be displayed on DataWorks Data Map. | 2022-01-10 | Germany (Frankfurt) and Singapore |
2021
December
Feature | Description | Release date | Region | References |
Establishment of a connection from MaxCompute to Hadoop Hive in a virtual private cloud (VPC), or from MaxCompute to Data Lake Formation (DLF) and Object Storage Service (OSS) | MaxCompute allows you to create a network connection to a VPC, a connection to a data source, and an external project that is used to implement the lakehouse solution in an end-to-end manner. You do not need to submit a ticket. The time required is reduced from days to minutes. The lakehouse page is added to the MaxCompute console. This page provides entry points for the creation, development, management, and governance of lakehouses. | 2021-12-30 | All regions | |
Progressive computing supported by MaxCompute | Progressive computing is supported. The intermediate result data is automatically stored in partitions based on a fixed time granularity. In the next cycle, the data that is computed in the last cycle can be reused. This way, less computing resources are consumed, the scheduling period and costs are reduced, and operation efficiency of jobs is improved. | 2021-12-03 | All regions | |
Enhancement of MaxCompute materialized views | Partitions and clusters can be created based on MaxCompute materialized views. If a materialized view does not contain the partition from which you want to query data when you query the materialized view, you can set enable_auto_substitute to true to allow the system to automatically query data from the source table and return the summary data of the source table and materialized view. | 2021-12-01 | All regions |
November
Feature | Description | Release date | Region | References |
Commercial release of the VPC connection scheme | MaxCompute can access VPCs by using features, such as external tables, user-defined functions (UDFs), and lakehouses. Before MaxCompute can access a VPC, you must establish a network connection between MaxCompute and the destination IP address or service, such as ApsaraDB for HBase, ApsaraDB RDS, or Hadoop clusters. Before the commercial release of the VPC connection scheme, you must fill in an application form. After the commercial release of the VPC connection scheme, you can add or delete a network connection over a VPC on the Network Connection page in the MaxCompute console. You do not need to fill in an application form and wait for approval. This improves development efficiency. Before you configure a network connection, you must understand the permissions related to network connection management. | 2021-11-29 |
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Clearance of data from the specified partition in a partitioned table | MaxCompute allows you to manually clear the data of one or more partitions that are specified in a partitioned table. Metadata information about the partitioned table or partitions in the partitioned table is not deleted. MaxCompute also allows you to specify filter conditions to filter the partitions whose data needs to be cleared. | 2021-11-23 | All regions |
October
Feature | Description | Release date | Region | References |
ApsaraDB for HBase Performance-enhanced Edition and Lindorm supported by MaxCompute external tables | ApsaraDB for HBase Performance-enhanced Edition is a cloud-hosted database that is provided by LindormTable. This edition is fully compatible with open source HBase. Lindorm is a cloud-native multi-mode hyper-converged database that is designed and optimized for scenarios such as Internet of things (IoT), Internet, and Internet of vehicles (IoV). Lindorm supports unified access and processing of a variety of data, such as wide tables, time series, text, objects, streams, and spaces. In various scenarios, you must import data from ApsaraDB for HBase Performance-enhanced Edition or Lindorm to MaxCompute for data processing, data analysis, and federated query. You must also write data from MaxCompute to ApsaraDB for HBase Performance-enhanced Edition or Lindorm. You can create an external table of ApsaraDB for HBase Performance-enhanced Edition or Linorm in MaxCompute to read data from or write data to ApsaraDB for HBase Performance-enhanced Edition or Lindorm. | 2021-10-29 | All regions |
September
Feature | Description | Release date | Region | References |
Service availability of MaxCompute in China South 1 Finance | MaxCompute can be activated in the China South 1 Finance region. | 2021-09-14 | China South 1 Finance | None |
Access to Hadoop clusters for which Kerberos authentication is enabled | Hadoop clusters for which Kerberos authentication is enabled can be accessed by MaxCompute. Kerberos authentication is enabled for Hadoop production clusters of most enterprises. This greatly expands the application scope of the integration of lakehouses and Hadoop clusters. | 2021-09-01 |
| None |
Reading of Hudi data or data in the Delta Lake format from OSS by using the lakehouse solution | The Delta Lake and Apache Hudi storage mechanisms are commonly used in data lake solutions. These storage mechanisms provide stream processing and batch processing capabilities for data lakes. MaxCompute provides a lakehouse solution that supports Delta Lake and Apache Hudi. This solution is developed based on DLF and OSS. You can use MaxCompute to query real-time data and gain instant insight into the changes of business data. | 2021-09-01 |
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August
Feature | Description | Release date | Region | References |
Addition of 16 built-in functions to MaxCompute SQL | The following built-in functions are added to MaxCompute SQL:
| 2021-08-31 | All regions | |
Commercial release of UPDATE, DELETE, and MERGE INTO in MaxCompute SQL | As of August 10, 2021, the public preview of the UPDATE, DELETE, and MERGE INTO statements of MaxCompute SQL ends. After the public preview ends, you are charged when you use these statements. Pay-as-you-go jobs that already use the UPDATE, DELETE, and MERGE INTO statements are charged after the public preview ends. If you did not use the UPDATE, DELETE, or MERGE INTO statement due to concerns about service stability in the public preview phase, you no longer need to worry about stability when you use these statements. MaxCompute provides the same guarantees for the availability and stability of the UPDATE, DELETE, and MERGE INTO statements as the guarantee for MaxCompute SQL. | 2021-08-10 |
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Commercial release of MaxCompute Streaming Tunnel | MaxCompute Streaming Tunnel supports streaming semantic APIs to simplify the development of distributed services. MaxCompute Streaming Tunnel supports the concurrent creation of partitions for distributed services and asynchronous sorting by using the | 2021-08-09 | All regions | |
Materialized views of MaxCompute SQL | MaxCompute SQL supports the materialized view feature. A materialized view is a database object that stores the pre-calculation results of a time-consuming query operation, such as JOIN or AGGREGATE. You can reuse the pre-calculation results when you want to perform the same query. This way, queries are accelerated. Materialized views are suitable for the following queries:
After materialized views are created, MaxCompute can automatically match the optimal materialized view based on the query rewrite capabilities of MaxCompute SQL and read data from the materialized view. This significantly improves query efficiency. You do not need to modify existing queries. | 2021-08-06 | All regions |
July
Feature | Description | Release date | Region | References |
Pre-sorting of input data of specific aggregate functions in MaxCompute SQL | If you specify | 2021-07-30 | All regions | |
Syntax of multi-column operations in MaxCompute SQL subqueries | MaxCompute SQL is compatible with the PostgreSQL subquery syntax and provides the
| 2021-07-29 | All regions | |
New methods to delete MaxCompute projects | A MaxCompute project can be deleted by using an Alibaba Cloud account or a RAM user that is assigned the Super_Administrator role for the project in the MaxCompute console. You can use one of the following methods to delete a MaxCompute project:
| 2021-07-29 |
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April
Feature | Description | Release date | Region | References |
Dynamic filtering | Join operations are common in distributed systems. A join operation is both time- and resource-consuming in scenarios in which a large amount of data is processed. For join scenarios, MaxCompute provides the dynamic filtering feature. This feature allows MaxCompute to dynamically generate filters based on runtime by using the equi-join attribute of join operations. This way, MaxCompute can filter data before shuffle operations or join operations. This accelerates queries. The feature is suitable for the join between a dimension table and a fact table. You can configure a parameter to forcibly enable this feature, intelligently enable this feature, or manually use this feature based on displayed messages. You can also perform dynamic partition pruning to filter partitions before you use dynamic filtering. | 2021-04-07 |
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March
Feature | Description | Release date | Region | References |
Monitoring and alerting on job timeout for all jobs or all SQL jobs of a MaxCompute project | MaxCompute allows you to configure a threshold-triggered alert rule by using CloudMonitor to monitor the job runtime. If the runtime of a job exceeds the threshold specified in the rule, MaxCompute sends an alert notification to the alert contact. This mechanism helps you identify abnormal jobs at the earliest opportunity and improves operations and maintenance (O&M) efficiency. | 2021-03-16 |
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FROM_JSON and TO_JSON functions supported by MaxCompute SQL | The FROM_JSON and TO_JSON functions are added. You can use the FROM_JSON function to convert data in the JSON format into data of a data type that is supported by MaxCompute. You can also use this function to extract information from data in the JSON format and return data of the ARRAY, MAP, or STRUCT type based on the jsonStr and schema information. You can use the TO_JSON function to convert data in the ARRAY, MAP, or STRUCT format into data in the JSON format. | 2021-03-16 | All regions | |
UPDATE, DELETE, and MERGE INTO statements supported by MaxCompute SQL (public preview) | UPDATE and DELETE statements are supported by MaxCompute to perform row-level operations on data in tables or partitions. Before the UPDATE and DELETE statements are supported, you must read full data, update the data based on join or other operations, and then execute the INSERT OVERWRITE statement to write back the updated data even if you need to update only a small amount of data in a table or partition. The UPDATE and DELETE statements can help you significantly reduce the amount of data that needs to be read or written. If you want to perform INSERT, UPDATE, and DELETE operations on a table, you can execute the MERGE INTO statement, which scans the full table once and encapsulates all the operations. This improves execution efficiency. In addition, the MERGE INTO statement is atomic. If any internal logic processing fails, the overall job fails to run. This helps avoid the issue that some logic of the same batch of operations cannot be rolled back or redone. Only transactional tables support the UPDATE, DELETE, and MERGE INTO statements. A big data system ensures the atomicity, consistency, isolation, and durability (ACID) semantics for jobs. If multiple jobs run on the same table at the same time, job conflicts may occur. The UPDATE, DELETE, and MERGE INTO statements are in public preview. You are not charged for computing resources when you use these statements. The availability and stability are not guaranteed for the jobs and data that use these statements for production during public preview. We recommend that you back up important data. | 2021-03-16 | All regions |
February
Feature | Description | Release date | Region | References |
Commercial release of the lakehouse solution of MaxCompute | The lakehouse solution of MaxCompute is implemented by using a MaxCompute data warehouse and a data lake such as OSS or Hadoop Distributed File System (HDFS). You can build a lakehouse by using one of the following methods:
| 2021-02-26 |
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ApsaraDB for HBase external tables (public preview) | MaxCompute can access ApsaraDB for HBase over a VPC. After you complete network connection and authorization, you can create an ApsaraDB for HBase external table and read data from and write data to the tables in an ApsaraDB for HBase database by calling the HBaseStorageHandler interface provided by Hive. You can use the ApsaraDB for HBase external table to synchronize data from an ApsaraDB for HBase database to MaxCompute for subsequent extract, transform, and load (ETL) operations. You can also associate the ApsaraDB for HBase external table with a MaxCompute internal table for federated computing, or export MaxCompute data to a table in the ApsaraDB for HBase database. Reading and writing data of ApsaraDB for HBase external tables is in the public preview stage. Data computing is free of charge but the SLA is not guaranteed. | 2021-02-08 |
| ApsaraDB for HBase external tables (Standard Edition or Performance-enhanced Edition) |
Disabling of header display in the MaxCompute CLI to facilitate shell calls | In a shell window or Windows Command Prompt, you may need to use the value that is dynamically returned by the | 2021-02-08 | All regions | |
Enhancement of data writing to OSS by using OSS external tables | MaxCompute can use the multipart upload feature of OSS to improve data writing efficiency when you perform an INSERT operation to write data to an OSS external table. The data that MaxCompute writes to the OSS external table is stored in the | 2021-02-08 | All regions | |
Custom Serde classes supported by MaxCompute external tables in the Hive-compatible data type edition | MaxCompute is compatible with the Hive Serde interface to process data in open source formats. MaxCompute also provides default built-in Serde classes to process common data formats. If you want to specify custom ROW FORMAT SERDE to support your data format, you must specify required resources, add a JAR package, and define the method of | 2021-02-08 | All regions | |
Addition or enhancement of MaxCompute built-in functions | Some built-in functions are added or enhanced.
| 2021-02-08 | All regions | |
Introduction of the optimizers Freeride and Analyze to collect metadata | Two optimizers Freeride and Analyze are introduced to collect metadata from tables:
| 2021-02-08 | All regions | |
ApsaraDB RDS external tables (public preview) | MaxCompute can access your ApsaraDB RDS instance over a VPC. After you complete a network connection and authorization, you can create an external table of ApsaraDB RDS and read and write data of tables in ApsaraDB RDS. You can use an external table of ApsaraDB RDS to synchronize data such as dimension data and service data from ApsaraDB RDS to MaxCompute for subsequent ETL operations. You can also associate the external table of ApsaraDB RDS with a MaxCompute internal table for federated computing, or export MaxCompute data to ApsaraDB RDS databases. Reading and writing data of ApsaraDB RDS external tables is in the public preview stage. Data computing is free of charge but the SLA is not guaranteed. | 2021-02-08 | All regions | |
Data reading from or writing to MC-Hologres external tables by using the Java Database Connectivity (JDBC) driver | You can use MC-Hologres external tables to access data of MC-Hologres data sources by using the JDBC driver. You can create an MC-Hologres external table in MaxCompute to query the data of MC-Hologres data sources by using the JDBC driver provided by PostgreSQL. This method prevents redundant data storage and allows you to obtain query results at a fast speed without the need to import or export data. Reading and writing data of MC-Hologres external tables is in the public preview stage. Data computing is free of charge but the Service Level Agreement (SLA) is not guaranteed. | 2021-02-08 | All regions | |
Data export from MaxCompute to OSS by using UNLOAD | MaxCompute allows you to export data to OSS by using the UNLOAD command. This way, you can use OSS to store structured data and use other computing engines in OSS to process and analyze the data that is exported from MaxCompute. | 2021-02-08 | All regions | |
Commercial release of MaxCompute SQLML | MaxCompute SQLML allows data engineers, analysts, and data scientists to use SQL to create, train, and apply machine learning models in MaxCompute. This enables SQL practitioners to use existing SQL tools and skills to apply machine learning capabilities without the need for data migration. This achieves inclusiveness of machine learning. | 2021-02-01 | All regions |
January
Feature | Description | Release date | Region | References |
Adjustment of unit prices of MaxCompute storage services | The tiered pricing of MaxCompute storage services is changed to fixed pricing from January 10, 2021. The new pricing reduces MaxCompute storage costs for users that use small- and medium-sized data. Before the adjustment, tiered pricing is used for MaxCompute storage services. For data of less than or equal to 10 TB, the unit price is CNY 0.0072 per GB per day. For data of greater than 10 TB and less than or equal to 100 TB, the unit price is CNY 0.006 per GB per day. For data of greater than 100 TB, the unit price is CNY 0.004 per GB per day. The preceding unit prices are uniformly changed to CNY 0.12 per GB per month, which is CNY 0.004 per GB per day. | 2021-01-10 | All regions |
2020
December
Feature | Description | Release date | Region | References |
Information Schema service automatically provided for new projects | By default, the Information Schema service is provided for new projects to allow users to view and use project data. Administrators do not need to manually install this service for new projects. | 2020-12-21 | All regions | |
Private preview of Information_Schema.Tasks | The Information_Schema.Tasks view is in private preview. This view provides detailed information about the jobs that are running, including the job list and the CPU utilization, memory usage, resource usage, and running duration of each job. This view helps users quickly locate jobs that run slowly and jobs that consume a large number of resources to optimize jobs or plan resource capacity. | 2020-12-08 | None | |
Improvement of data analysis capabilities based on MCQA supported by the MaxCompute query editor | MaxCompute Query Acceleration (MCQA) is supported for data queries of MaxCompute. Data queries of MaxCompute are originally performed in offline mode. After MCQA is enabled, the time that is used to run small- and medium-sized query jobs is reduced from minutes to seconds. If you execute an SQL statement to query data by using the MaxCompute query editor, MCQA is preferentially used. If the MCQA requirements are met, results are returned within seconds. If the MCQA requirements are not met, the MCQA job is rolled back to the offline query mode. This ensures that the query job runs as expected. Data analysts can perform data queries, secondary analysis of results, and data sharing based on the powerful analysis features of Web Excel provided by the MaxCompute query editor. The MCQA feature supports pay-as-you-go resources. You cannot enable this feature for a project that uses subscription resources. | 2020-12-05 | All regions |
November
Feature | Description | Release date | Region | References |
Data import by using MaxCompute sink connectors provided by ApsaraMQ for Kafka | MaxCompute is integrated with ApsaraMQ for Kafka. You can use the MaxCompute sink connectors provided by ApsaraMQ for Kafka to continuously import data from specific topics to MaxCompute tables. This process requires no third-party tools or secondary development, which significantly simplifies data import from ApsaraMQ for Kafka to MaxCompute and reduces both development and O&M costs. | 2020-11-27 | All regions | Import Kafka data to MaxCompute in offline or real-time mode |
Service access as a RAM role | You are allowed to access a MaxCompute project by using an Alibaba Cloud account, a RAM user, or a RAM role. The administrator can add a RAM role to a project and grant permissions to the role. This way, you can log on to the project as the RAM role by using Security Token Service (STS) without the need to use an AccessKey pair for identity authentication. The MaxCompute console supports STS-based authentication. After authentication, you can use a RAM role to access a MaxCompute project in the MaxCompute console for data analysis and data development. | 2020-11-24 | All regions |
June
Feature | Description | Release date | Region | References |
Selection of MaxCompute data type editions for new projects | MaxCompute data type editions can be selected for new projects. When you create a MaxCompute project, you must select the initial data type edition for the project. | 2020-06-30 | All regions |
April
Feature | Description | Release date | Region | References |
Policy-based access control by using the GRANT statement | The GRANT statement can be executed to perform policy-based access control. This facilitates the authorization operation. | 2020-04-23 | All regions |
March
Feature | Description | Release date | Region | References |
Addition of five built-in functions | The following built-in functions of MaxCompute are added: | 2020-03-17 | All regions |
February
Feature | Description | Release date | Region | References |
Disabling of the lifecycle for a table | The | 2020-02-26 | All regions | |
CLONE TABLE command | The CLONE TABLE command can be used to clone data from one table to another. | 2020-02-26 | All regions | |
Extension of | The | 2020-02-26 | All regions | |
Acquisition of data from external tables over HTTPS | The | 2020-02-26 | All regions | |
| The MSCK REPAIR TABLE syntax can be executed to add partitions to an Object Storage Service (OSS) external table based on the OSS directory that is specified when you create the OSS external table. | 2020-02-26 | All regions |
January
Feature | Description | Release date | Region | References |
Support for DATE and CHAR data types | The DATE and CHAR data types are supported. If you enable the MaxCompute V2.0 data type edition, you can create a table that contains data of the DATE and CHAR types and data of the DATE and CHAR types can be read. | 2020-01-03 | All regions |
2019
December
Feature | Description | Release date | Region | References |
Open source geospatial UDFs | Open source geospatial user-defined functions (UDFs) are supported to analyze spatial data. | 2019-12-25 | All regions |
November
Feature | Description | Release date | Region | References |
Connection to a MaxCompute project by using Tableau | The MaxCompute Java Database Connectivity (JDBC) driver can be used to connect Tableau to a MaxCompute project. This way, MaxCompute data can be displayed on Tableau for analysis. | 2019-11-18 | All regions | |
MaxCompute JDBC driver | The MaxCompute JDBC driver is supported to provide a standard JDBC API. You can perform distributed computing and queries on large amounts of data in MaxCompute by using the JDBC API. | 2019-11-18 | All regions |
October
Feature | Description | Release date | Region | References |
Addition or dropping of multiple partitions at the same time | The DDL semantics of MaxCompute is upgraded. This way, you can add or drop multiple partitions at the same time. You can manage multiple partitions at the same time to improve the efficiency of data warehouse management. | 2019-10-28 | All regions | |
New operators | The | 2019-10-28 | All regions | |
NATURAL JOIN | The NATURAL JOIN operation is supported to improve the join capability of the MaxCompute SQL engine. | 2019-10-28 | All regions | |
Configuration of the default values of columns in a table | The DEFAULT VALUE clause is provided to specify a default value for a column when you create a table. | 2019-10-28 | All regions | |
OFFSET used with ORDER BY LIMIT | An OFFSET clause can be used together with an ORDER BY LIMIT clause to improve the capabilities of the MaxCompute SQL engine. | 2019-10-28 | All regions | |
Parameters odps.sql.orderby.position.alias and odps.sql.groupby.position.alias | The odps.sql.orderby.position.alias and odps.sql.groupby.position.alias parameters are supported. If you add the set hive.orderby.position.alias=true; command before a SELECT statement, integer constants in the ORDER BY clause are processed as column numbers in the SELECT statement. If you add the set hive.groupby.position.alias=true; command before a SELECT statement, integer constants in the GROUP BY clause are processed as column numbers in the SELECT statement. | 2019-10-28 | All regions | |
Built-in functions | The built-in functions | 2019-10-28 | All regions | |
Configuration of the priorities of JOIN operations by using parentheses () | Parentheses () can be used to specify the priorities of JOIN operations. | 2019-10-28 | All regions | |
Partition merging | Partitions can be merged. If a large number of partitions exists, you can merge partitions to archive data. | 2019-10-28 | All regions |
September
Feature | Description | Release date | Region | References |
Metadata service | The Information Schema service of MaxCompute is supported to provide information such as project metadata and historical data. | 2019-09-06 | All regions | |
Storage price reduction | The storage prices of MaxCompute are reduced from September 1, 2019. The five tiered prices are changed to three tiered prices, and the tiered unit prices are reduced. | 2019-09-01 | All regions | None |
July
Feature | Description | Release date | Region | References |
Spark on MaxCompute | Spark on MaxCompute is a computing service that is provided by MaxCompute and compatible with open source Spark. This service provides a Spark computing framework based on unified computing resource and dataset permission systems. This service allows you to use your preferred development method to submit and run Spark jobs. Spark on MaxCompute can fulfill diverse data processing and analytics requirements. | 2019-07-23 | All regions | |
Availability of MaxCompute in the China (Chengdu) region | MaxCompute can be activated in the China (Chengdu) region. | 2019-07-04 | China (Chengdu) | None |
June
Feature | Description | Release date | Region | References |
Acquisition of ExecutionContext objects by using UDTs | User-defined types (UDTs) can be used to obtain ExecutionContext objects to access the current ExecutionContext class and resources. | 2019-06-25 | All regions | |
Function overloading supported by UDTs | Generics, class inheritance, and variable-length parameters are supported by UDTs to provide flexible methods to define functions. | 2019-06-25 | All regions | |
Dynamic parameters of UDAFs and UDTFs | Dynamic parameters are supported by user-defined aggregate functions (UDAFs) and user-defined table-valued functions (UDTFs) of MaxCompute to make UDF development more flexible. | 2019-06-24 | All regions | |
Partition pruning supported by UDFs | Partition pruning is supported by UDFs to reduce computing costs. | 2019-06-24 | All regions | None |
Parameterized view | The parameterized view feature is supported. This way, you can import tables or variables to create custom views. | 2019-06-24 | All regions | |
Generation of the DDL statement for creating a table | The | 2019-06-24 | All regions | |
Monitoring and alerting of consumption of subscription jobs | The monitoring and alerting feature of MaxCompute is supported. You can configure metrics and alert rules in the CloudMonitor console to monitor the consumption of subscription jobs. | 2019-06-21 | All regions |