Global Interpreter Lock (GIL) google
In CPython, the global interpreter lock, or GIL, is a mutex that prevents multiple native threads from executing Python bytecodes at once. This lock is necessary mainly because CPython’s memory management is not thread-safe. (However, since the GIL exists, other features have grown to depend on the guarantees that it enforces.) CPython extensions must be GIL-aware in order to avoid defeating threads. For an explanation, see Global interpreter lock. The GIL is controversial because it prevents multithreaded CPython programs from taking full advantage of multiprocessor systems in certain situations. Note that potentially blocking or long-running operations, such as I/O, image processing, and NumPy number crunching, happen outside the GIL. Therefore it is only in multithreaded programs that spend a lot of time inside the GIL, interpreting CPython bytecode, that the GIL becomes a bottleneck. However the GIL degrades performance even when it is not a bottleneck. Summarizing those slides: The system call overhead is significant, especially on multicore hardware. Two threads calling a function may take twice as much time as a single thread calling the function twice. The GIL can cause I/O-bound threads to be scheduled ahead of CPU-bound threads. And it prevents signals from being delivered. …

Robust Anomaly Detection (RAD) google
Outlier detection can be a pain point for all data driven companies, especially as data volumes grow. At Netflix we have multiple datasets growing by 10B+ record/day and so there’s a need for automated anomaly detection tools ensuring data quality and identifying suspicious anomalies. Today we are open-sourcing our outlier detection function, called Robust Anomaly Detection (RAD), as part of our Surus project. As we built RAD we identified four generic challenges that are ubiquitous in outlier detection on “big data.”
• High cardinality dimensions: High cardinality data sets – especially those with large combinatorial permutations of column groupings – makes human inspection impractical.
• Minimizing False Positives: A successful anomaly detection tool must minimize false positives. In our experience there are many alerting platforms that “sound an alarm” that goes ultimately unresolved. The goal is to create alerting mechanisms that can be tuned to appropriately balance noise and information.
• Seasonality: Hourly/Weekly/Bi-weekly/Monthly seasonal effects are common and can be mis-identified as outliers deserving attention if not handled properly. Seasonal variability needs to be ignored.
• Data is not always normally distributed: This has been a particular challenge since Netflix has been growing over the last 24 months. Generally though, an outlier tool must be robust so that it works on data that is not normally distributed.
In addition to addressing the challenges above, we wanted a solution with a generic interface (supporting application development). We met these objectives with a novel algorithm encased in a wrapper for easy deployment in our ETL environment. …

Entity Resolution (ER) google
Entity Resolution (ER), the problem of extracting, matching and resolving entity mentions in structured and unstructured data, is a long-standing challenge in database management, information retrieval, machine learning, natural language processing and statistics. Ironically, different subdisciplines refer to it by a variety of names, including record linkage, deduplication, co-reference resolution, reference reconciliation, object consolidation, identity uncertainty and database hardening. Accurate and fast ER has huge practical implications in a wide variety of commercial, scientific and security domains. Despite the long history of work on ER there is still a surprising diversity of approaches – including rule based methods, pair-wise classification, clustering approaches, and richer forms of probabilistic inference – and a lack of guiding theory. Meanwhile, in the age of big data, the need for high quality entity resolution is only growing. We are inundated with more and more data that needs to be integrated, aligned and matched before further utility can be extracted. …