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One of the principal features used in analytic databases is table partitioning. This feature is so frequently used because of its ability to significantly reduce query latency by allowing the execution engine to skip reading data that is not necessary for the query. For example, consider a table of events partitioned on the event time using calendar day granularity. If the table contained 2 years of events and a user wanted to find the events for a given 7-day window, that query would only need to access 7 out of 730 partitions, reducing the amount of data accessed by over 100x.

While static partitioning is very useful, there are several challenges with it. For example, a table with too many partitions can result in a significant amount of metadata to manage and maintain. Partitioning at too fine of a granularity can also limit query parallelism because the amount of data becomes too small in any given partition. And finally, there are a finite number of useful partitioning schemes as it is impossible to partition on everything.

To address these challenges, Apache Impala 2.9 added a number of features and functionality to provide even faster query performance using more advanced data skipping techniques.

Leveraging Parquet Column Statistics and Dictionary Filtering

Each Apache Parquet file contains a footer where metadata can be stored including information like the minimum and maximum value for each column. Starting in v2.9, Impala populates the min_value and max_value fields for each column when writing Parquet files for all data types and leverages data skipping when those files are read. This approach significantly speeds up selective queries by further eliminating data beyond what static partitioning alone can do. For files written by Hive / Spark, Impala only reads the deprecated min and max fields.

The effectiveness of the Parquet min_value / max_value column statistics for data skipping can be increased by ordering (or clustering 1 ) data when it is written by reducing the range of values that fall between the minimum and maximum value for any given file. It was for this reason that Impala 2.9 added the SORT BY clause to table DDL which directs Impala to sort data locally during an INSERT before writing the data to files.

In addition to leveraging Parquet column statistics for data elimination, Impala also added optimizations for dictionary filtering. Low-cardinality columns are generally dictionary encoded so even if the min/max filtering does not disqualify the row group, dictionary predicate evaluation may often do so.

These accounts of what is required to avoid exploitation offer observational researchers, such as the Majengo cohort investigators, little practical guidance. The transactional focus of Fair Benefits suggests a preference for narrow, tractable benefits over broad and complex one, yet the most pressing needs of the Majengo cohort women--as an example--are profound, intimately related to the research, and inextricably rooted in the complexity of social injustice. London's approach, on the other hand, despite its explicit focus on social justice, is under-developed in terms of practical guidance for investigators. What is missing is a framework that straddles these two polar views, one that helps observational researchers reconcile their day-to-day experience with needs arising from injustice with the requirement to ensure that participants benefit from their contributions to research in fair and meaningful ways.

Below, we proposed a way to address this problem, which we call 'relief of oppression'. We emphasize that some of the pressing injustices routinely experienced by observational researchers give rise to humanitarian obligations of 'rescue', or assistance, which vary in strength with the extent of the relationships with their participants, the special capabilities of the researchers and the circumstances of their opportunity to provide assistance. Combined with the now widely recognized obligation to ensure research participants receive some benefits in return for their participation in research, relief of oppression offers a pragmatic strategy for explicitly addressing participants' needs that arise from injustice.

'Relief of oppression' is an organizing principle, analogous to the principle of harm reduction (as we describe in greater detail, below) that is now widely applied in public health practice. Relief of oppression aims to help observational researchers working in conditions of injustice and deprivation to clarify their ethical obligations to participants. It aims to bridge the gap between a narrow, transaction-oriented account of avoiding exploitation and a broad account emphasizing obligations of reparation for historic injustices. Specifically, it focuses explicitly on efforts to ameliorate some of the effects of the background conditions that limit fundamental freedoms of research participants. This explicit focus is necessary to ensure that benefits for research participants, negotiated with investigators and sponsors, do not simply avoid needs arising from systemic injustice, which may be difficult to address, in favour of needs that are more easily met by investigators.

Relief of oppression embodies the substantive aim of a widely held moral intuition that poverty and global injustice are matters of great moral urgency. Many theorists have developed this intuition and expressed it in terms of obligations on the part of individuals and institutions in LMIC to ameliorate these conditions [ 15 , 17 , 18 ]. The practical force of these obligations has been limited by considerable disagreement about the precise requirements of these obligations for various actors in specific real-world circumstances. In the absence of perfect agreement on the nature and scope of the relevant obligations, we view 'relief of oppression' not as a philosophic theory to solve the problems associated with previous accounts of these obligations, but rather as a framework to help clarify and operationalize existing obligations to research participants.

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