def status(self):
        docs = {'updated'  : [],
                'committed': [],
                'to_commit': []}

        row = self.db.internal.find_one({'_id': 'last_sync'})

        if row and 'update' in row:
            for id in row['update']:
                docs['updated'].append(self.get(id, deleted=True))

        if row and 'commit' in row:
            for id in row['commit']:
                docs['committed'].append(self.get(id, deleted=True))

        to_commit =  self.get_docs_to_commit()
        for row in to_commit:
            doc = Document(**row)
            docs['to_commit'].append(doc)
        return docs
def aggregate(cls, dataset, dimensions, function, **kwargs):
        data = dataset.data
        cols = [d.name for d in dataset.kdims if d in dimensions]
        vdims = dataset.dimensions('value', label='name')
        reindexed = data[cols+vdims]
        agg = function.__name__
        if len(dimensions):
            agg_map = {'amin': 'min', 'amax': 'max'}
            agg = agg_map.get(agg, agg)
            grouped = reindexed.groupby(cols, sort=False)
            if not hasattr(grouped, agg):
                raise ValueError('%s aggregation is not supported on cudf DataFrame.' % agg)
            df = getattr(grouped, agg)().reset_index()
        else:
            agg_map = {'amin': 'min', 'amax': 'max', 'size': 'count'}
            agg = agg_map.get(agg, agg)
            if not hasattr(reindexed, agg):
                raise ValueError('%s aggregation is not supported on cudf DataFrame.' % agg)
            agg = getattr(reindexed, agg)()
            data = dict(((col, [v]) for col, v in zip(agg.index.values_host, agg.to_array())))
            df = util.pd.DataFrame(data, columns=list(agg.index.values_host))

        dropped = []
        for vd in vdims:
            if vd not in df.columns:
                dropped.append(vd)
        return df, dropped